2023 was an eventful year in the tech sector, where AI went mainstream with the explosion of language learning models.

As we progress into 2024, the integration and evolution of artificial intelligence in various domains are not just changing; they are set to revolutionise the way we approach design, development, and deployment in these sectors.

With advancements in artificial intelligence accelerating at an unprecedented pace, we stand at the cusp of a new era where AI’s influence extends beyond mere automation to become a cornerstone of innovation and efficiency.

The engineering team at ByteSnap Design has been reflecting on the future of AI in the technology and electronics design industries.

Here is the team’s forecast for the pivotal AI trends likely to emerge during 2024 to redefine industry standards and drive forward a new age of technological excellence.

AI to trigger a battle of the Smart Assistants?

AI assistant tools will continue to be integrated into existing tools to make tasks easier. An example of this happening this year is Zoom’s AI companion which can summarise meetings into notes.

Expect to see development tools such as an IDE integration which can generate GIT commit notes, and release notes automatically during 2024.

We also predict companies will be trying to increase their profits with more monetisation from smart assistants such as Alexa and this will drive techy people towards open source alternatives, such as Home Assistant which run locally.

Rise of the Robotaxi

We anticipate that the first un-geofenced electric Robotaxis will become operational and start accepting paying customers over the coming months.

This is likely to scale steadily over the next few years to replace Uber as the transport medium of choice, providing legal issues can be overcome.

Ultimately, this will make car ownership a questionable decision because travelling this way could be cheaper than running a car.

Apple to enter the Generative AI race

We look forward to Apple unveiling a product to join the AI arms race with their own large language model. Other companies are embracing AI faster and already implementing it; for example, Bard into Google Assistant, and Microsoft’s push for AI in their Office 365 products. Nevertheless, Apple have a stronger than most in-house development philosophy, and it’s hard to see them allowing these products to go unchallenged.

Expect announcements in late 2024 from Apple around its generative AI offering.

Further AI disruption for the Smart Home market

Expect to see more innovation in the smart home market as consumers continue to look for ways to reduce their energy bills, with smart thermostats and TRVs becoming ever more popular. Nest are apparently trying to use AI to help understand consumers behaviour around energy consumption and we anticipate that this trend will continue.

With Apple releasing their Pro Vision headset in 2024, we also expect to see some manufacturers trying to compete with a cheaper product. Apple are excellent at design and are sometimes seen as a trend setter, but in this case are quite late to the party With Meta already well-established leaders. However, Apple have a history of knocking out incumbent leaders so this could be an interesting space to watch.

How much of a consumer appetite there is for this type of technology, however, remains open to question.

AI-enabled Integrated Circuits

We’re likely to see the greater emergence of AI on integrated circuits from companies such as Altered Carbon.

Computer chip manufacturers, Intel, for example, are incorporating AI cores into their CPUs.

AI algorithms in our view are mostly used for detection/categorisation. The classic example is using AI to detect whether an image contains a cat or a dog. However, even the way that the likes of Tesla use AI is similar – detecting images of signs for speed limits, or an image of the lines of the road – but the output is different in that it translates it into braking, accelerating or turning.

One of the projects we’ve worked on at ByteSnap sent accelerometer data into the cloud to detect people falling over. We see a scenario where a fall detector algorithm could be generated by AI and embedded within the sensor device, so that the huge amount of data does not need to be sent, allowing the product to consume less power.

Greater AI in Supply Chain Management

AI-powered forecasting is providing businesses with intelligence to prevent mishaps in the future, overcoming demand-supply mismatches to prevent overstock or understock of inventory.
This minimises costs and improves customer experience. We expect to see more of this across 2024. Additionally, AI-based algorithms are automating goods retrieval from warehouses for smooth order fulfilment, and AI-powered autonomous vehicles are reducing driver costs for delivery.

AI in software and electronics design

Software development and electronics design are both areas that AI vendors are targeting, as developers are expensive and timescales can be long. We can see initially the AI could be best at optimising PCB layouts in the hardware side and writing generic functions within software, albeit with dubious copyright infringement.

The work of translating very abstract requirements into real electronics still seems a very long way off though. This is partly due to lack of freely available models to train against in what is a fast-moving industry and little way for the circuits that are available to be assessed.

In addition, electronic engineering is actually quite a person-centred job; dealing with suppliers, customers, manufacturers, colleagues. Software AI trainers have raided github and ChatGPT was able to train linguistic models against the huge wealth of the World Wide Web.

However, for electronics, it will take another generation of AI development before engineering jobs are threatened.

The post ByteSnap Electronic Industry Predictions for 2024 appeared first on IoT Business News.

IoT Analytics published an analysis based on the “Generative AI Market Report 2023–2030” report and highlights the landscape with its top players in the data center GPU, foundational model and platform, and generative AI services markets.

Key insights:

• The generative AI market went from nearly nothing to a hot market within a year, as shown by IoT Analytics’ latest research report.
• IoT Analytics analyzed 3 interconnected markets for generative AI: 1) data center GPUs, 2) foundational models and platforms, and 3) generative AI services. Each has distinct aspects and market players.
• NVIDIA leads the data center GPU segment with a 92% market share, while OpenAI and Microsoft have a combined share of 69% in the foundational models and platforms market. The services market is more fragmented, with Accenture currently seen as the leader with a 6% market share.

Key quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks: “The speed of generative AI innovation with new offerings coming on the market on a weekly basis is fascinating monitor. Nvidia with 92% market share for data center GPUs as well as Microsoft and OpenAI with a combined 69% market share in the models and platforms segment are firmly in the lead in their respective market segments. With hyperscalers developing their own data center chips, with the availability of powerful open-source models and with giants like AWS and Google looking to differentiate with their new offerings, it will be interesting to watch how much the early lead is worth for the current market leaders. I personally do expect both Microsoft and Nvidia to maintain their strong positions in the coming years but the gap to the competition will likely close a bit.”

Philipp Wegner, Principal Analyst at IoT Analytics, adds that:

“The Generative AI market is rapidly evolving, with established leaders and a growing number of startups. In 2024, it’s a make-or-break year for Gen AI vendors, as they navigate a crowded field of competitors.”

The leading generative AI companies

The rise of generative AI

Following its release of ChatGPT in 2022, OpenAI experienced an impressive one-year, zero-to-$1 billion revenue bump—surpassed only by US-based chipmaker NVIDIA, which managed to increase its data center GPU sales from $3.6 billion in Q4 2022 to an expected $16 billion in Q4 2023. When it comes to generative AI companies, these two stand out.

The generative AI foundational models and platforms market is expected to reach nearly 5% of global software spending by 2030

According to IoT Analytics’ Generative AI Market Report 2023–2030 (published December 2023), the generative AI software and services market reached $6.2 billion in 2023. Although it is still very early to forecast where things are going from here, the IoT Analytics research team expects the generative AI foundational models and platforms market to make up nearly 5% of global software spending by 2030 due to its disruptive nature and tremendous value potential.

However, this does not include the market for individual generative AI solutions. The team believes generative AI will become standard within most software in the near future. This also does not include the hardware market, such as for data center GPUs, since this market is looked at separately from software but is discussed below.

In this article, we dive into the data center GPU, generative AI foundational model and platform, and generative AI services markets, discussing what aspects of the generative AI field make up each market and highlighting the leading generative AI companies within them.

Market segment 1: Data center GPU market

a.) Market overview

The data center GPUs market refers to specialized GPUs designed to handle the extensive computation demands of modern data centers, which are the backbone of generative AI. Originally designed for rendering graphics, GPUs excel at parallel processing, which is fundamental for deep learning computations used in generative AI.

The report shows the data center GPUs market reached $49 billion in 2023—a booming increase from 2022 (+182%), mostly driven by one company alone: NVIDIA. Although the market for data center GPUs has seen steep price increases and is undergoing severe supply constraints, there is currently no reason to believe demand will decline in the next two years.

b.) Leading data center GPU companies

The data center GPU market at this point has one very clear leader. However, the market report shows that there are other promising startups and other established companies trying to make inroads.

The data center GPU market at this point has one very clear leader. However, the market report shows that there are other promising startups and other established companies trying to make inroads.

1. NVIDIA

NVIDIA leads the data center GPU market by a long shot, owning 92% of the market share. In 2023, the company’s quarterly revenue jumped 272%, from $4.3 billion in Q1 to a forecasted $16 billion in Q4.

The NVIDIA A100 Tensor Core GPU is the de facto standard for data center GPUs. However, as discussed in the report, hardware is not the only differentiator for NVIDIA. Some consider their developer ecosystem, CUDA, as NVIDIA’s biggest moat, and it is often cited as the key reason why NVIDIA is not set to lose its dominant position anytime soon.

2. AMD

The Data Center segment of US-based semiconductor AMD player, NVIDIA’s first real GPU challenger, grew by 21% from Q2 2023 to Q3 2023 and shared 3% of the market. However, AMD has big ambitions in 2024 to eat into NVIDIA’s market share. In early December 2023, it announced the release of its Instinct MI300 Series accelerators, which are cheaper than NVIDIA’s comparable accelerators and, as AMD claims, faster. AMD’s CEO, Dr. Lisa Su, forecasted at least $1 billion in revenue in 2024 through this chip alone, and Microsoft, Meta, and OpenAI stated they would use the Instinct MI300X in their data centers. AMD also recently launched ROCm 6.0 to provide developers with an ecosystem that is equally attractive to CUDA.

3. Intel and others

US-based chipmaker Intel, the traditional competitor to NVIDIA and AMD, has lagged behind on the data center GPU front. In May 2022, Intel’s Habana Labs released its second generation of AI processors, Gaudi 2, for training and inferencing. Though not as fast as NVIDIA’s popular H100 GPU, it is considered a viable alternative when considering price to performance.

Meanwhile, in July 2023, startup chipmaker Cerebras announced it had built its first of nine AI supercomputers in an effort to provide alternatives to systems using NVIDIA technology. Cerebras built the system, Condor Galaxy 1, in partnership with the UAE, which has invested in AI research in recent years.

Market segment 2: Generative AI foundational models and platforms market

a) Market overview

The foundational models and platforms market comprises two related areas. Foundational models are large-scale, pre-trained models that can be adapted to various tasks without the need for training from scratch, such as language processing, image recognition, and decision-making algorithms.

Generative AI platforms, in turn, refer to software that enables the management of generative AI-related activities outside of foundational models. Notably, IoT Analytics identified six platform types: 1) development, 2) data management/databases, 3) AI IaaS/GPU as-a-service, 4) middleware & integration, 5) MLOps, and 6) user interface and experience (UI/UX).

The foundational models and platforms market exploded with the public release of ChatGPT in late 2022, reaching $3.0 billion in 2023. This is substantial growth over 2022, which saw next to nil in terms of revenue. IoT Analytics’ analysis projects strong market growth in the coming years as enterprises invest billions in—and report real value from—generative AI implementations and continuous improvements.

b.) Leading generative AI foundational model and platform companies

Unsurprisingly, the foundational model and platform market are currently led by OpenAI, with several well-known technology companies trying to catch up.

1. OpenAI

With the November 2022 launch and subsequent success of ChatGPT, OpenAI leads in the share of the foundational model and platform vendors market with 39%. Since the release of ChatGPT, OpenAI’s generative pre-trained transformer (GPT) models went from GPT-3.5 to GPT-4 to GPT-4 Turbo, showcasing the continued development of the model. OpenAI’s models continue to impress in independent model assessments and rankings—often coming out in the top three of all tested models. Although many experts expect the foundational model space to become a commodity over time, at this point, OpenAI’s flagship models remain the top foundational model on the most common benchmarks.

According to IoT Analytics’ What CEOs Talked About series, in 2023, ChatGPT skyrocketed in boardroom discussions in Q1, but as other foundational models and generative AI applications became available, mentions of ChatGPT steadily declined as “generative AI” separated and continued to rise. (The What CEOs Talked About in Q4 2023 report and blog is expected to be released mid-December 2023.)

2. Microsoft

On OpenAI’s heels at 30% market share is Microsoft, its largest shareholder. Microsoft’s platform, Azure AI, offers Azure OpenAI, which uses OpenAI’s LLMs but goes beyond the public ChatGPT offering by promising greater data security and custom AI apps. This is suited for enterprises who want to secure their proprietary data when leveraging the benefits of generative AI since ChatGPT’s terms of use state that they can store and use content (both input and output) to improve their services. In November 2023, Microsoft reported over 20,000 active paying customers for its Azure AI platform, adding that 85% of Fortune 100 companies used it in the past year.

Despite Microsoft’s strong partnership with OpenAI, Microsoft also heavily promotes the usage of other models, such as Llama 2, via its platform, thereby enabling customers to freely choose and test different models and providers. Another key priority for Microsoft is integrating AI capabilities into its existing product portfolio, such as Azure, Microsoft/Office 365, and Bing.

3. AWS

AWS has an 8% share of this market. Its Bedrock service, publicly released in September 2023, provides access to models from several AI companies, such as Anthropic, AI21 labs, and Cohere (each with a 2% share of this market), and combines them with developer toolsets to help customers build and scale generative AI applications.

AWS has quickly claimed the third spot in this market because the company is the market leader in public cloud services and quickly got its existing customer base excited about its differentiated approach to Generative AI. In contrast to Google and Microsoft, AWS Bedrock focuses on providing a platform service that gives users access to a number of both general and domain-specific foundational models from a variety of vendors—providing choice, flexibility, and independence.

4. Google

In 2022, most experts credited Google as being the one tech company at the forefront of AI. Many experts interviewed by the IoT Analytics team continuously praised Google for its AI and its data products and innovations. In 2023, the picture is different, and Google is fighting to defend its position as an AI leader.

Vertex AI is Google Cloud’splatform focused on machine learning (ML) ops. It is integrated with other Google Cloud services, such as BigQuery and Dataproc, and offers a Jupyter-based environment for ML tasks. In early December 2023, Google released a preview version of its new multi-modal flagship model, Gemini. The related technical report states that the largest of the Gemini family outperformed other existing models in 30 out of 32 common ML benchmarks. Initially, the announcement of Gemini was widely received as positive, but a popular demo video released by Google later turned out to be staged.

Market segment 3: Generative AI services market

a) Market overview

The generative AI services market represents a specialized segment dedicated to consulting, integration, and implementation support for organizations aiming to integrate generative AI capabilities. With generative AI having risen as one of the top discussion points in boardrooms, services companies are sensing a large opportunity in helping companies formulate their generative AI strategies (e.g., what use cases to implement), advising them on technical architecture choices (e.g., which models to use) and helping them implement and build individual solutions.

IoT Analytics assesses that the generative AI services sector’s opportunity is now. Due to the novelty of generative AI, organizations often lack skills and experience, and the only option is to look for professional services firms that have or are in the process of building up the required expertise.

b) Leading generative AI services companies

The generative AI services market is more dispersed than the other two markets highlighted here.

1. Accenture

Accenture is estimated to have the largest generative AI services market share at 6%. The company announced in June 2023 that it is investing $3 billion in data and AI practice over three years to double its AI talent and develop new capabilities. Additionally, Accenture disclosed in its Q4 2023 earnings press release that its revenue for generative AI projects grew to $300 million for 2023.

In November 2023, Accenture announced plans to launch a network of generative AI studios in North America where companies can explore ways to integrate generative AI applications. These studies are expected to be at Accenture Innovation Hubs in Chicago, Houston, New York, San Francisco, Toronto, and Washington, DC.

2. IBM

US-based technology corporation IBM makes up 2% of this market. To position itself for the opportunities that generative AI brings, the company announced it had established a “Center of Excellence (CoE) for generative AI,” which as of May 2023, already had over 1,000 consultants specialized in generative AI. The CoE operates alongside IBM’s AI and Automation practice, which includes over 21,000 data and AI consultants.

3. Capgemini

France-based IT services company Capgemini also has a 2% share in this market, offering consulting services intended to help clients adopt key technologies such as the cloud and AI. In July 2023, Capgemini announced the launch of a portfolio of generative AI services, including in the following areas:

• Strategy
• Customer experience
• Software engineering
• Custom solutions for enterprise

One of Capgemini’s current customers is London Heathrow Airport which aims to improve traveler experiences through its “Generative AI for Customer Experience” offer. Heathrow’s Director of Marketing and Digital, Pete Burns, stated that the project is intended to “assist, empower and delight passengers” with tailored customer service solutions.

4. The many others

Past this point, the remaining 86% of the market becomes a cornucopia of specialized generative AI services providers and larger general consulting and system integration companies, each taking a bite of the rapidly growing segment.

As an example, in April 2023, UK-based professional services company PwC announced plans to invest $1 billion over three years to not only grow its AI offerings but also transform how it works by using generative AI. Additionally, in July 2023, global consultancy firm McKinsey & Company announced it partnered with AI startup Cohere to provide customized AI solutions to its enterprise clients.

Generative AI company landscape outlook

The enterprise generative AI market is roughly a year old, and already, the generative AI companies landscape appears vast.

IoT Analytics released its first generative AI report, the Generative AI Trend Report, in March 2023. Since then, more foundational models and platforms have emerged, e.g., OpenAI’s GPT4 Turbo, Google’s Gemini, or Microsoft’s Phi-2. At the same time, the demand for data center GPUs exploded, which is also mirrored in NVIDIA’s stock performance (+231% year-to-date as of 14 December 2023). Finally, consulting giants have made investments to position themselves in the generative AI services market, such as Accenture’s $3B investment in AI and its pledge to double “AI talent.”

As part of this research, we talked to 30+ experts in the field and gathered information on 270+ generative AI projects and analyzed which industries and departments are fastest to adopt and which vendors are most selected today.
The coming months will reveal how many of those projects will deliver value besides just being a marketing coup or how many of those currently in the proof-of-concept stage will move forward. Most companies are only now forging their generative AI strategies and considering whether to build foundational models from scratch based on industry-specific data, use an out-of-the-box propriety model, or fine-tune open-source models. All of this comes as generative AI companies release new products at unprecedented speed.

There is still a lot of movement in the generative AI company landscape, and there will be more in the foreseeable future. IoT Analytics will stay on top of this space, with a follow-up report expected in 2024.

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The Internet of Things (IoT) landscape in 2024 is set for transformative changes, driven by advancements in cybersecurity, artificial intelligence (AI), and a plethora of emerging technologies, as IoT systems become increasingly integrated into critical infrastructure.

In this article, I shall delve into the various aspects of this transformation, exploring the impact of AI and machine learning (ML) in creating intelligent IoT systems, the rise of edge computing, the integration of blockchain for enhanced security, the introduction of ultra-thin smart shipping labels, the incorporation of the SGP.32 standard, and IoT’s burgeoning role in sustainability.

Increased focus on IoT cybersecurity

In 2024, the integration of IoT devices into vital systems like Smart Cities, coupled with the increased adoption of technologies such as 5G, eSIM, iSIM, and satellite connectivity, has emphasised the importance of robust cybersecurity measures. These advancements have made IoT devices more versatile and efficient, but they also necessitate enhanced focus on safeguarding data integrity and device security.

To address these needs, there’s a growing emphasis on deploying advanced encryption and rigorous security protocols. These measures ensure the protection of data transmitted between IoT devices and central systems. Additionally, continuous monitoring and real-time threat detection, powered by AI and ML, may well become standard practices. They help in promptly identifying and responding to potential security breaches, maintaining the integrity and reliability of IoT networks.

AI and ML enabling intelligent IoT systems

AI and ML are revolutionising almost everything, including IoT. By analysing massive amounts of data instantaneously, AI enhances IoT applications such as predictive maintenance and energy management. This synergy, combined with centralised IoT management platforms, leads to unparalleled operational efficiency.

In 2024, the integration of AI and ML will become much more embedded in IoT infrastructures. The blend of AI’s analytical capabilities with IoT’s data collection and monitoring functions creates an ecosystem where operational insights are gathered more efficiently and effectively, leading to smarter, more responsive IoT systems.

Edge computing enhancing IoT performance

Edge computing is revolutionising IoT performance by processing data closer to its source. This method significantly reduces latency, crucial for real-time applications such as autonomous vehicles, industrial automation, and augmented reality. These advancements are particularly pertinent in smart cities, healthcare, manufacturing, and retail, where they facilitate immediate data analysis and improve service quality.

Looking forward, the integration of AI and machine learning with edge computing is expected to increase, enabling edge devices to independently make complex decisions. The expansion of 5G networks will enhance communication between these devices, promoting faster, more efficient data processing. Furthermore, edge computing’s role in reducing energy consumption and carbon emissions underscores its significance in fostering a more sustainable IoT ecosystem.

Blockchain for IoT security

As IoT devices increasingly handle sensitive data, the role of blockchain in bolstering IoT security is becoming more prominent. Blockchain’s decentralised nature offers enhanced data integrity, making it a key player in protecting against the growing cybersecurity threats in the IoT landscape. Its integration with AI and ML is particularly noteworthy, representing a significant leap forward in building a resilient IoT infrastructure.

This combination promises to shape a stronger, more secure IoT ecosystem for 2024 and beyond, especially as the attack surface of IoT expands. Blockchain’s ability to ensure the authenticity and security of data transactions across the network is vital in this context, presenting a robust solution to the evolving challenges in IoT security.

Ultra-thin, low-power smart shipping labels

The ultra-thin, low-power smart shipping labels, first seen in early 2023 with our very own Smart Shipping Label, which is equipped with a printed, eco-friendly battery, features an eSIM, and supports up to 1000 messages across LTE-M, NB-IoT, and 2G networks.

Such labels will become much more prolific in 2024, due to their function as advanced tracking devices for items both large and small. They are capable of real-time monitoring of location, temperature, and package integrity, ensuring secure and efficient transit.

Thanks to their adaptability for various logistical needs, from tracking small documents to larger assets, these smart labels not only enhance supply chain efficiency but also align with sustainability goals, representing a significant advancement in IoT-driven asset management.

Integrating SGP.32 into the IoT ecosystem

The integration of the SGP.32 standard into the IoT ecosystem in 2024 heralds a significant advancement in device capabilities and application efficiency. SGP.32 is pivotal for use cases that demand high location accuracy, like precision agriculture, by providing superior geolocation services.

Moreover, the incorporation of SGP.32 plays a key role in the expanded use of eSIMs within IoT devices. This is particularly beneficial for global IoT deployments, as it simplifies the complexities associated with device management across different regions. Features like remote provisioning and profile swapping inherent in eSIM technology are instrumental in enhancing operational efficiency.

This development is not just a technological leap; it’s a strategic enabler for more efficient, globally connected, and responsive IoT ecosystems. The impact of integrating SGP.32 will be felt across various sectors, significantly contributing to the overall evolution and effectiveness of IoT applications.

IoT’s sustainability drive intensifies

Finally, in 2024 IoT will continue playing its pivotal role in driving sustainability across various sectors. Advanced, energy-efficient sensors, coupled with AI, are revolutionising resource management by enabling precise monitoring and control. This technological synergy is significantly reducing waste and optimising energy use.

In industries like manufacturing, IoT adoption is being accelerated by tightening global regulations, which are mandating more sustainable practices and better ecological footprints. IoT technologies are not only enhancing operational efficiencies but also promoting environmental stewardship. The implementation of smart systems in areas such as energy management and waste reduction are evidence of IoT’s growing influence in creating a more sustainable future.

As the world grapples with environmental challenges, the integration of IoT in sustainability efforts is becoming increasingly crucial, marking a new era where technology and ecology harmoniously intersect.

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In the face of escalating climate challenges, technology has emerged as a beacon of hope. The Internet of Things (IoT) stands out as a particularly powerful tool in the global effort to promote environmental sustainability. With its network of interconnected devices and sensors, IoT offers innovative solutions to monitor, understand, and address environmental issues, contributing significantly to the fight against climate change.

IoT: A Game-Changer for Climate Monitoring

Climate change is a complex beast, with a multitude of variables that must be tracked and analyzed. IoT technologies offer unprecedented granularity in environmental monitoring, with sensors capable of providing real-time data on everything from atmospheric CO2 levels to the health of ocean ecosystems. This data is invaluable for researchers and policymakers alike, offering up-to-the-minute insights that can inform responsive and effective environmental policy.

Energizing Renewables with IoT

Renewable energy sources like solar and wind power are crucial in the transition away from fossil fuels. IoT is instrumental in optimizing the performance of these energy sources. Smart sensors can track wind patterns and sunlight exposure, adjusting the positioning of turbines and solar panels to maximize energy capture. Moreover, IoT systems help in predicting maintenance needs, reducing downtime, and enhancing the overall efficiency of renewable energy infrastructures.

Smart Agriculture: Growing More with Less

Agriculture consumes a vast amount of our planet’s resources, but IoT is helping to change that. Precision farming techniques, underpinned by IoT, enable farmers to monitor soil moisture levels and crop health with pinpoint accuracy, leading to more judicious use of water and pesticides. This not only helps in conserving precious resources but also results in higher yields and better-quality produce.

Waste Not: IoT for Waste Reduction

Waste management is another area where IoT shines. Smart waste bins can signal when they are full, optimizing collection routes and frequencies. IoT systems also play a crucial role in the recycling industry, where they can sort materials more efficiently and identify contaminants that can hinder the recycling process.

The Smart Grid: An IoT-Enabled Energy Network

One of the most significant applications of IoT in sustainability is the development of smart grids. These intelligent energy distribution networks can balance supply and demand in real time, reduce energy wastage, and integrate a higher percentage of renewable energy sources. Consumers can play an active role in energy conservation through smart meters that provide real-time feedback on energy consumption, encouraging more responsible usage patterns.

Challenges to Overcome

Despite its vast potential, the widespread adoption of IoT for environmental sustainability is not without challenges. The energy consumption of IoT devices themselves is a concern; thus, it is imperative that these devices are designed to be as energy-efficient as possible. Additionally, the production of IoT devices must become greener, employing sustainable materials and minimizing waste.

Data privacy and security are also critical issues. The vast amounts of data collected by IoT devices must be kept secure to protect against breaches that could undermine public trust in these technologies.

Policy Implications and the Path Forward

To fully harness the potential of IoT for environmental sustainability, collaborative efforts are needed. Policymakers must create frameworks that encourage the development and deployment of sustainable IoT solutions. This includes investing in infrastructure, funding research and development, and setting industry standards that prioritize sustainability.

Cross-sector partnerships are equally important. The technology sector must work with environmental scientists, urban planners, and agricultural experts to create IoT solutions that are both technologically advanced and environmentally sound.

Conclusion

IoT offers a powerful arsenal of tools in the fight against climate change, from optimizing renewable energy to enabling smarter agriculture and waste management. However, the journey to a sustainable future requires more than just technology; it demands a collective commitment to innovation, responsible usage, and global cooperation. As we continue to harness the potential of IoT, we move closer to a more sustainable world where technology and the environment exist in harmony, combating climate change one smart solution at a time.

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The Global AI Safety Summit 2023, held at Bletchley Park and chaired by the UK, was a ground-breaking event that brought together 150 global leaders from various sectors to discuss the future of Artificial Intelligence (AI).

The agreement on the Bletchley Declaration marked the Summit, emphasising collaborative action for AI safety and the need for a shared understanding of AI risks and opportunities. A significant development was the initiation of the State of the Science Report, led by Turing Award-winning scientist Yoshua Bengio, aimed at providing a science-based perspective on the risks and capabilities of frontier AI.

During the Summit, there was a strong focus on the necessity of state-led testing of AI models, and the importance of setting international safety standards was highlighted. The UK’s announcement of launching the world’s first AI Safety Institute underlined its commitment to leading in AI safety research and testing. Summit participants also recognised the need to address current and future AI risks, emphasising standardisation and interoperability to mitigate these risks effectively.

While the majority of current conversations surrounding the impact of AI remain broad and high-level, it’s crucial to acknowledge the significant influence it will have in the realm of IoT. As we delve into this specific area, it is evident that not only will AI play a pivotal role in shaping IoT’s evolution, but the reverse is also true.

The data generated from IoT applications will not only feed into AI systems, enhancing their capabilities, but also emphasise the importance of data integrity. This mutual influence underscores a dynamic relationship where both IoT and AI will significantly shape each other’s development, making it imperative to recognise and address the intertwined futures of these technologies.

In fact, the evolution of AI’s capabilities in processing the vast data generated by IoT devices is propelling a transition from reactive to proactive and predictive operations across various sectors. This paradigm shift is not only about efficiency and reliability but also about establishing trusted and authentic data sources, which is where the Identity of Things (IDoT) comes into play.

Moving from basic identifiers to unique digital identities, IDoT ensures the authenticity of data and strengthens the trust in IoT ecosystems. Implementing technologies such as embedded SIM (eSIM) and integrated SIM (iSIM) is instrumental in this process. They enable better security through robust access control, enhanced data integrity, and reduced vulnerabilities while also addressing privacy concerns.

By ensuring compliance with regulatory standards, eSIM and iSIM contribute to standardisation and reliability, which are critical for scalable and interoperable IoT networks. These technologies support personalisation and accountability, leading to enhanced traceability and the capacity for more advanced predictive analytics.

As AI and IoT continue to converge, the focus on unique digital identities through IDoT will become a cornerstone in achieving a secure, reliable, and adaptable technological ecosystem, ready for the future of interconnected devices.

However, a critical aspect of integrating AI with IoT is ensuring the data integrity of the inputs. The data sourced for AI processing must be not only authentic and secure but also trustworthy. This is because the decisions made by AI are only as reliable as the data upon which they are based. Any security vulnerabilities at the point of data collection or transmission could lead to significant, potentially catastrophic, consequences.

It is, therefore, essential for multi-party IoT ecosystems to establish and maintain data integrity to prevent such risks. Technologies such as SIGNiT by G+D are addressing this critical need by employing digital signing of data generated by IoT devices, coupled with blockchain technology, to create a secure and trustworthy data environment. Ensuring the fidelity of data at its source is fundamental to building AI systems that can be trusted to make sound decisions.

The path forward is fraught with challenges, particularly concerning data privacy, AI’s decision-making transparency, and the reliability of AI algorithms. A significant concern is ensuring that AI integration does not inadvertently create vulnerabilities within IoT systems. To significantly mitigate these risks, we can harness advanced cryptographic techniques.

For instance, elliptic curve cryptography (ECC) is one such technique that provides high levels of security with smaller key sizes, making it more efficient for IoT devices which often have limited computational power. By incorporating blockchain technology and employing advanced cryptography like ECC, we can establish robust security protocols to protect data integrity and maintain the trustworthiness of AI-driven IoT systems.

In essence, the integrity of the entire data stream can be maintained by securing data right at the source – the IoT sensor – and using private keys on secure elements like SIM cards. However, integrating AI into existing IoT systems presents issues beyond data integrity alone. Such integration is a complex endeavour that demands a multifaceted and sophisticated approach to tackle various technical and operational challenges.

On the technical front, it involves ensuring compatibility between AI algorithms and diverse IoT devices, managing the vast data streams generated by these devices, and maintaining the responsiveness and reliability of the systems. The integration must be seamless, ensuring that AI algorithms can effectively interpret and act on the data from IoT devices without causing system lags or errors.

Moreover, this integration significantly impacts business models and operational workflows. For businesses, incorporating AI into IoT systems often means rethinking how they collect, analyse, and utilise data for decision-making. It requires shifting from traditional business processes to a more dynamic, data-driven approach.

Operationally, there’s a need for continuous monitoring and maintenance of these integrated systems, ensuring they operate efficiently and effectively. This shift also necessitates training and upskilling of staff to manage and leverage these advanced systems.

The overarching goal is to ensure that AI acts as a catalyst for enhancing IoT functionalities, not as a barrier. It should streamline operations, provide deeper insights, and open new avenues for innovation and efficiency rather than complicate or hinder existing processes. Thus, integrating AI into IoT systems is not just a technological upgrade but a transformative process that reshapes how organisations operate and interact with technology.

The successful implementation of this integration hinges on a careful balance – leveraging the advanced capabilities of AI to enhance IoT functionalities while also adapting to the new challenges and opportunities this fusion presents, with a clear and necessary focus on data integrity.

As we stand at the cusp of a technological revolution with AI and IoT at its core, balancing the immense opportunities with the inherent challenges is imperative. Ensuring data integrity, securing IoT ecosystems, and maintaining a controlled integration of AI are essential steps towards harnessing the full potential of these technologies.

The AI Safety Summit is just the beginning of a critical journey. The real challenge lies ahead in our industry’s hands. In the IoT sector, we must actively drive the development of responsible and effective strategies for AI integration. While the Summit set the stage, it’s now our responsibility to act.

The post AI and IoT: Post-AI Summit reflections on safe integration and data integrity appeared first on IoT Business News.

IoT Analytics published an analysis based on the “Predictive Maintenance & Asset Performance Market Report 2023–2028” report and highlights 5 key insights related to the $5.5 billion predictive maintenance market.

Key insights:

• The global predictive maintenance market grew to $5.5 billion in 2022–a growth of 11% from 2021—with an estimated CAGR of 17% by 2028, according to the Predictive Maintenance and Asset Performance Market Report 2023–2028.
• With median unplanned downtime costs larger than $100,000 per hour, the importance of accurately predicting failures of large assets has never been higher.
• This article shares 5 key highlights of the predictive maintenance market: 1) The market is valued at $5.5 billion, 2) there are 3 different types of predictive maintenance, 3) predictive maintenance software tools share 6 features, 4) predictive maintenance is commonly being worked into the maintenance workflow, and 5) successful standalone solutions vendors specialize in an industry or asset.

Key quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks: “Predictive Maintenance continues to be one of the leading use cases for Industry 4.0 and digital transformation, especially in process industries where asset failures can quickly go into the hundreds of thousands of dollars. It is great to see that the market is moving ahead with AI integration into existing APM and CMMS systems and that prediction accuracies are improving. Nonetheless, we still have a long way to go as false alerts remain commonplace.”

Fernando Brügge, Senior Analyst at IoT Analytics, adds that “Predictive maintenance is reaching new heights of maturity and sophistication thanks to the rapid advancements in artificial intelligence, hardware, and data engineering. We are at the point where these technologies enable us to collect, process, and analyze massive amounts of data from multiple sources, and use them to build more accurate and reliable models of machine health and behavior, as well as to determine potential courses of action to fix machine issues. In this way, predictive maintenance is not only a smart way to optimize equipment performance and lifecycle, but also a strategic way to enhance operational efficiency and competitiveness in a rapidly evolving industrial space.”

Predictive maintenance market: 5 highlights for 2024 and beyond

One accurately predicted failure of a large asset is worth more than $100,000 in many industries.

Our latest research highlights, among many other things, that the median unplanned downtime cost across 11 industries is approximately $125,000 per hour. With critical unplanned outages in facilities in industries such as oil and gas, chemicals, or metals occurring several times a year, an investment into predictive maintenance can amortize with the first correct prediction.

Unfortunately, there is a flip side: the accuracy of many predictive maintenance solutions is lower than 50%. This creates headaches for maintenance organizations that often run to an asset to find it is perfectly fine, eroding trust in the entire solution.

That said, vendors have been making strides to increase prediction accuracy, with more data sources and better analysis methods becoming available, including AI-driven analysis. There are positive signs that this determination for better prediction accuracy is helping end users: our research indicates that 95% of predictive maintenance adopters reported a positive ROI, with 27% of these reporting amortization in less than a year.

General search interest in predictive maintenance and related concepts has been on the rise for the last 12 years. Online searches for the term have grown nearly threefold since we initiated coverage on the topic in 2017 and have outgrown condition-based maintenance and asset performance management (APM) related searches.

Indeed, predictive maintenance appears to be well on track to be the must-have killer application we made it out to be in 2021.

In this fourth installment of our predictive maintenance market coverage, we look at 5 important highlights to note about the market going into 2024:

1. The predictive maintenance market is valued at $5.5 billion (2022)
2. There are 3 different types of predictive maintenance, with anomaly detection on the rise
3. Predictive maintenance software tools share 6 common features
4. Integration into the maintenance workflow is becoming important
5. Successful standalone solutions vendors specialize in an industry or asset

Highlight 1: The predictive maintenance market is valued at $5.5 billion

The predictive maintenance market reached $5.5 billion in 2022. Uncertain economic conditions and other manufacturing priorities in the last 2 years resulted in 11% market growth between 2021 and 2022. With companies reinvesting in efficiency, safety, and operational performance, we expect the market for predictive maintenance to grow to 17% per year until 2028.

Our research indicates that industries with heavy assets and high downtime costs are driving the adoption of predictive maintenance solutions (e.g., oil & gas, chemicals, mining & metals).

Highlight 2: There are 3 different types of predictive maintenance, with anomaly detection on the rise

As the market has evolved, 3 noticeable predictive maintenance types have developed:

1. Indirect failure prediction
2. Anomaly detection
3. Remaining useful life (RUL)

The difference between these largely comes down to the objectives, methods of data analysis, and type of output/information they provide. RUL is the hardest to achieve due to resource demands and environmental factors that make it difficult to scale. Indirect failure prediction has been the most used approach, but our research indicates that anomaly detection is on the rise.

1. Indirect failure prediction

The indirect failure prediction approach generally takes a machine health score approach based on a function of maintenance requirements, operating conditions, and running history. This approach often relies on general analysis to yield this score, though supervised machine learning methods can be used if a significant amount of data is available.

Benefits:

• Scalability – Indirect failure prediction can be more easily scaled since they rely on equipment manufacturers’ specifications that are more or less the same across machines of the same type.
• Cost effective – Indirect failure prediction can use existing sensors and data, reducing the need for additional instrumentation.

Limitations:

• Failure time-window accuracy – Indirect failure prediction does not give a timeline of when machines will fail. This can be a problem for organizations with very costly downtimes (e.g., heavy equipment industries).
• Dependent on historical data – Indirect failure prediction’s effectiveness relies on the availability of extensive historical data for accurate modeling.

2. Anomaly detection

Anomaly detection is the process of finding and identifying irregularities in the data (i.e., data points that deviate from the usual patterns or trends). While the indirect failure prediction and RUL approaches use failure data to predict future failures, anomaly detection uses the “normal” asset profile to detect deviations from the norm. These deviations can indicate potential problems, such as faults, errors, defects, or malfunctions, that need to be detected and addressed before they cause serious damage or downtime.
This approach makes it easier when there is not a good repository of failure data, and it often relies on unsupervised machine learning.

Benefits

• Low data and hardware requirements – Anomaly detection models can identify issues without being trained on failure data. Further, since these models need less data, they do not demand high computing power.
• High scalability and model transferability – Anomaly detection models are trained on normal operation data, so they can easily be applied to different machines without retraining or adaptation.

Limitations

• Failure time-window accuracy – As with indirect failure prediction, anomaly detection models do not give a timeline of when machines will fail, which can be a problem for organizations with very costly downtimes.
• Presence of false positives – While most solutions in the market can distinguish between critical and noncritical anomalies, the choice of unsupervised machine learning models is still important as it can affect how well this distinction can be made (e.g., autoencoders and generative adversarial networks do not capture the complexity of normal operations).

3. Remaining useful life (RUL)

RUL is the expected machine life or usage time remaining before the machine requires repair or replacement. Life or usage time is defined in terms of whatever quantity is used to measure system life (e.g., distance traveled, repetition cycles performed, or the time since the start of operation).

This approach relies on condition indicators extracted from sensor data—that is, as a system degrades in a predictable way, data from the sensors match the expected degradation values. A condition indicator can be any factor useful for distinguishing normal operations from faulty ones. These indicators are extracted from system data taken under known conditions to train a model that can diagnose or predict the condition of a system based on new data taken under unknown conditions.

Predictions from these RUL models are statistical estimates with associated uncertainty, resulting in a probability distribution.

Benefits

• Failure prediction time-window – RUL is especially useful for industries where maintenance is very costly and needs advanced planning, such as heavy-equipment industries.
• Output robustness – Since RUL estimates rely on high-quality and detailed data, they tend to be more robust and reliable.

Limitations

• Resource demand – Training large models requires powerful computing hardware, especially if done on-premises.
• Model transferability and scalability – Different environments and usage patterns can cause different failure modes for the same type of equipment. This means the model needs to be retrained for each specific case, reducing its scalability and generalizability.

Highlight 3: Predictive maintenance software tools have 6 common features

Software is the largest segment of the predictive maintenance tech stack, making up 44% of the predictive maintenance market in 2022.

Our report shows that even though most successful predictive maintenance software vendors specialize in industries or assets, there are 6 common features between their various solution software suites:

1. Data collection
2. Analytics and model development
3. Pre-trained models
4. Status visualization, alerting, and user feedback
5. Third-party integration
6. Prescriptive actions

We will delve further into these features and offer an example snapshot for each from various software vendors. The examples are to help readers understand some approaches to these common features.

Feature 1: Data collection

Data collection tools within predictive maintenance software collect, normalize, and store data on asset health/condition parameters. They also collect other data types needed to identify and predict upcoming issues, such as business and process data.

Snapshot:

US-based predictive maintenance software vendor Predictronics offers PDX DAQ, an application that allows users to synchronize data collection from multiple sources for any given period of time. The solution creates a database that harmonizes all the timestamps from different sensors, which Predictronics claims yields the necessary information for analysis and producing real-time, impactful results.

Feature 2: Analytics and model development

Analytics and model development tools within Predictive Maintenance software analyze, interpret, and communicate data patterns, including analytics discovery (e.g., RCA, AD modules) and modeling (e.g., feature engineering and model selection and testing).

Snapshot:

US-based predictive maintenance software vendor Falkonry (recently acquired by IFS) offers Workbench within its Time Series AI platform, a low-code ML-based solution aiming to help users—specifically, operational practitioners, including production, equipment, or manufacturing engineers—discover patterns such as early warning or stages of deterioration in complex physical systems. It also aims to enable users to analyze large amounts of data and build predictive models.

Feature 3: Pre-trained models

Pre-trained models are just that: ready-to-use models typically designed for specific assets in specific industries. These models include capabilities and references for specific assets or failure modes (e.g., fouling for heat exchangers, wear and corrosion for fans, or valve leakage for compressors). These are meant to help end users see examples of models so they can build on them or develop custom predictive maintenance algorithms.

Snapshot:

US-based asset management software vendor AspenTech (recently acquired by Emerson), offers Mtell, an application that includes pre-populated, industry-specific asset templates to help users select sensors for common asset categories and AI functionality to create and deploy models quickly for PdM applications (e.g., for specific compressors, turbines, and blowers).

Feature 4: Status visualization, alerting, and user feedback

Status visualization, alerting, and user feedback tools within predictive maintenance software automatically communicate asset-related data/insights for various personas. These insights often include status dashboards and automatic alerts that trigger work orders or corrective actions, maintenance planning, and optimization. These tools also enable users to provide feedback concerning the accuracy of alerts.

Snapshot:

US-based analytics software vendor SAS Institute offersAsset Performance Analytics, which includes status dashboards and automatic alerts intended to notify operations staff and managers of impending failure so that organizations have time to identify and fix issues before they become costly problems.

Feature 5: Third-party integration

Third-party integration enables users to connect their predictive maintenance software to other software systems and workflow management tools, such as ERP, MES, CMMS, APM (more on APM integration in Highlight 4), and Field Service.

Snapshot:

SKF, a Swedish bearing and seal manufacturing company also offering maintenance products, offers a condition monitoring and predictive maintenance solution that interfaces with existing plant control systems (e.g., MES or SCADA) and other external dashboards (e.g., ERP). It also provides insights to operators in the field via alarms and visualization on handheld devices.

Feature 6: Prescriptive actions

Prescriptive action features typically suggest the optimal actions to take in case of an (upcoming) failure. These actions are typically prioritized by criteria that are set when the algorithm is designed.

The actions that are prescribed by the software vary depending on the nature and urgency of the issue. They may require multiple steps or interventions. For instance, some actions may involve automatically adjusting the equipment parameters or informing the maintenance and operation teams about the necessary procedures to ensure equipment efficiency.

Snapshot:

Marathon, a predictive maintenance software solution from Norway-based Arundo, provides a feature known as Investigations that aims to provide the workflow and instructions to resolve equipment problems according to prescribed corporate standards.

Highlight 4: Integration into the maintenance workflow is becoming important

In its early days, predictive maintenance was mostly a standalone solution developed by startups to address specific customer needs. However, our report highlights a notable trend of sophisticated predictive maintenance solutions integrating into larger APM and computerized maintenance (CMMS) solutions.

APM is a strategic equipment management approach designed to help optimize the performance and maintenance efficiency of individual assets and entire plants or fleets. APM aims to improve asset efficiency, availability, reliability, maintainability, and overall life cycle value.

Various APM vendors are introducing predictive maintenance software tools within their APM offerings. The solutions aim to tie the different capabilities into 1 thread:

• Knowing when a machine will fail and mapping how failures could affect production or output
• Estimating how much fixing or preventing an issue will cost
• Making recommendations on whether it is worth fixing or preventing a problem

By including a sophisticated predictive maintenance solution in an end-to-end asset flow, APM players are trying to become the main partners for their customers’ digitalization journeys.

Our report lists 9 key components of APM:

1. Asset health monitoring
2. Maintenance optimization
3. Reliability analysis
4. Integrity management
5. Performance optimization
6. Failure prediction <- Predictive maintenance resides here 7. Digital asset twin 8. Sustainability management 9. Energy optimization

We assess in our report that improving the failure prediction module of APM solutions is currently one of the key initiatives of leading APM vendors.

Highlight 5: Successful standalone solutions vendors specialize in an industry or asset

Our research found that 30% of predictive maintenance vendors offer standalone, industry- or asset-specific solutions. By tailoring their efforts to specific niches in which they have acquired domain knowledge, they can discern the types of equipment and industries in which their solutions offer the most end-user benefits.

Snapshot:

Israel-based data science company ShiraTech Knowtion uses its equipment expertise in its offering of Predicto, an industrial IoT platform focused on industrial maintenance teams. The platform enables reading and processing of sensor data from production plants, ideally based on its own multisensing devices (iCOMOX). The company has developed specific offerings for motors, pumps, conveyors, and pipes. These asset-tailored offerings enable the company to scale.

6 considerations for predictive maintenance vendors

Six questions that predictive maintenance vendors should ask themselves based on insights in this article:

1. Market growth and strategy: Given the market’s growth to $5.5 billion and the projected increase to $14.3 billion by 2028, how can our company align its strategy to capitalize on this market expansion?
2. Accuracy improvement: Considering the current lower-than-50% accuracy of many predictive maintenance solutions, what innovative approaches or technologies can we adopt to enhance the accuracy of our predictions?
3. ROI communication: How can we better communicate the positive ROI of predictive maintenance to potential customers, especially those who are skeptical due to past experiences with inaccurate solutions?
4. Industry specialization: Given that the most successful vendors are specialized in specific industries, assets, or use cases, should we consider narrowing our focus, and if so, in which areas?
5. Data collection and integration: Are we effectively collecting the right kinds of data (including business and process data) and integrating it into the right IT systems for optimal predictive maintenance?
6. Software tool features: Do our software tools encompass the 6 common features identified in the report (data collection, analytics and model development, pre-trained models, status visualization, third-party integration, prescriptive actions), and are they competitive in the current market?

8 considerations for those looking to adopt or update predictive maintenance solutions

Eight questions that those looking to adopt or update predictive maintenance solutions should ask themselves based on insights in this article:

1. Solution type suitability: Which type of predictive maintenance solution (indirect failure prediction, anomaly detection, or RUL) best aligns with our specific maintenance needs and operational goals?
2. Integration with existing systems: How easily can predictive maintenance solutions integrate into our existing maintenance workflows and asset management systems?
3. Vendor specialization: Should we look for a vendor specialized in our industry, specific assets, or use cases, and how would that benefit us over a generalist provider?
4. Data collection and analysis: Do we have the necessary infrastructure for effective data collection and analysis to support a predictive maintenance system?
5. Accuracy and trustworthiness: How can we evaluate and ensure the accuracy of the predictive maintenance solution to build trust within our maintenance team?
6. Scalability and future growth: How scalable are the predictive maintenance solutions, and can they accommodate our future growth?
7. Software features and functionality: Do the software tools offered by vendors have all the key features we need, such as data collection, analytics, and third-party integration?
8. Market trends and innovation: Given the evolving nature of the predictive maintenance market, how can we stay informed about the latest innovations and ensure that our solution remains cutting-edge?

The post Predictive maintenance market: 5 highlights for 2024 and beyond appeared first on IoT Business News.

Edge computing has emerged as a transformative technology for the Internet of Things (IoT), fundamentally altering how data is processed and managed within IoT ecosystems. By enabling data processing closer to the source, edge computing significantly enhances IoT infrastructure, leading to improved efficiency, reduced latency, and enhanced security. This article delves into the intricacies of edge computing in the IoT domain, exploring its impact and the potential it holds for the future of IoT.

Introduction to Edge Computing in IoT

The Internet of Things, a network of interconnected devices capable of collecting and exchanging data, has seen exponential growth in recent years. IoT devices range from simple sensors to complex industrial machines. Traditionally, IoT devices would send all collected data to centralized cloud-based services for processing and analysis. However, this approach often leads to high latency and increased bandwidth usage, which can be detrimental in scenarios requiring real-time data processing. This is where edge computing comes into play.

Edge computing refers to data processing at or near the source of data generation, rather than relying solely on a central data-processing warehouse. This means that data can be processed by the device itself or by a local computer or server, which is located close to the IoT device.

Enhanced Efficiency and Reduced Latency

One of the primary advantages of edge computing in IoT is the significant reduction in latency. By processing data locally, the need to send all data to a central cloud for processing is eliminated, thereby reducing the time it takes for the data to be processed and the response to be sent back. This is particularly crucial in applications where real-time processing is essential, such as autonomous vehicles, industrial automation, and smart grids.

Moreover, edge computing reduces the bandwidth required for data transmission, which is particularly important given the growing number of IoT devices and the massive volume of data they generate. By processing data locally and only sending relevant or processed data to the cloud, edge computing alleviates the strain on network bandwidth.

Improved Security and Privacy

Another critical aspect of edge computing in IoT is the enhancement of security and privacy. By processing data locally, sensitive information does not have to travel over the network to a centralized cloud, reducing the exposure to potential security breaches during transmission. Local data processing also means that in the event of a network breach, not all data is compromised, as some of it remains on the local device or edge server.

Furthermore, edge computing allows for better compliance with data privacy regulations, as data can be processed and stored locally, adhering to the legal requirements of the region in which the IoT device is located.

Enabling Advanced IoT Applications

Edge computing unlocks the potential for more advanced IoT applications. For instance, in the field of healthcare, wearable devices can monitor patient health data in real-time, processing and analyzing data on the spot to provide immediate feedback or alert healthcare providers in case of an emergency. In industrial settings, edge computing allows for predictive maintenance of machinery, where sensors can process data on the machine’s performance and predict failures before they occur.

Challenges and Considerations

Despite its advantages, implementing edge computing in IoT comes with its own set of challenges. One of the primary concerns is the management and maintenance of edge computing nodes. Unlike centralized cloud servers, edge devices are distributed and may be located in remote or hard-to-reach areas, making management and maintenance more challenging.

Additionally, ensuring the security of edge computing devices is crucial, as these devices could become targets for cyber-attacks. Unlike centralized data centers, which typically have robust security measures in place, edge devices may not have the same level of security, making them vulnerable.

The Future of Edge Computing in IoT

Looking ahead, the future of edge computing in IoT appears promising. With advancements in technology, edge devices are becoming more powerful, capable of handling more complex data processing tasks. This evolution is expected to drive further adoption of edge computing in various sectors.

In conclusion, edge computing represents a paradigm shift in how data is processed within IoT infrastructures. By enabling data processing closer to the source, it addresses the challenges of latency, bandwidth usage, and security. Although there are challenges in implementing edge computing, its benefits are significant, paving the way for more efficient, secure, and advanced IoT applications. As technology continues to evolve, edge computing is set to play an increasingly pivotal role in the IoT landscape, driving innovation and enabling new possibilities.

The post The Impact of Edge Computing on Data Processing and IoT Infrastructures appeared first on IoT Business News.

IoT Analytics released a new analysis, based on the ’Industrial IoT & Industry 4.0 Case Study Report 2023’.

Key insights:

• Digitalization has become essential for industrial companies worldwide, as IoT Analytics expects the industrial IoT market to reach $145 billion in 2023.
• The Industrial IoT and Industry 4.0 Case Studies Report 2023 delves into 22 case studies, exploring their project objectives, technologies deployed, lessons learned, challenges, and outcomes.
• In this article, we share five learnings from the report: 1) Upgrading the ERP is often the first step in digital transformation, 2) cloud-native data storage and streaming are coming up, 3) first successful implementations of private 5G use cases, 4) digitalization becoming a prerequisite to achieving sustainability, and 5) the continued journey toward predictive maintenance.

Key quotes:

Rajini Nair, Analyst at IoT Analytics, remarks: “Upon analyzing the case studies outlined in the report, it becomes evident that digitalization is a key catalyst driving technological progress in industrial settings. This includes the enhancement of ERP systems and the migration of data to cloud platforms equipped with real-time streaming capabilities. Furthermore, digitalization proves essential in harmonizing companies with their sustainability objectives. Strategies such as the implementation of predictive maintenance algorithms and the adoption of private industrial 5G use cases are leveraged to improve operational efficiency. Looking forward, the technological landscape is transforming with the rise of AI, poised to shape the future of manufacturing.”

Digitalization has become crucial to manufacturers globally

Digitalization has become crucial for manufacturers in their respective competitive landscapes. IoT Analytics estimates the industrial IoT market to reach $145B in 2023, with a CAGR of 17.9% until 2030, as more and more companies undertake digital transformation initiatives.

For many, digitalization has already become a game changer:

• Carmaker Mercedes has achieved 25% greater efficiency in its S-class assembly after optimizing the value chain and introducing innovative technologies at its Germany-based Factory 56 facility.
• Energy giant TotalEnergies aims to generate $1.5 billion annually in savings with its digital solutions by 2025.
• Chemical company Covestro increased efficiency and reduced unnecessary downtime by shifting from calendar-based to condition-based maintenance.

These are just some of the benefits of the digital transformation initiatives our research uncovered as part of the 255-page Industrial IoT and Industry 4.0 Case Studies Report 2023. The report delves into 22 recent industrial digital transformation case studies, looking at initiatives related to digital transformation, data architecture, predictive maintenance, AI, and industrial 5G.

Benefits of case studies for digitalization journeys

Case studies by peers or companies in other industries are a great way to learn about digitalization, identify common challenges, develop a view of best practices, and understand how companies manage to scale.

The 22 case studies in our report offer readers a diverse set of manufacturing examples of current IIoT and Industry 4.0 projects, along with project objectives and takeaways from each. Our analysis of these takeaways yielded many trends in these companies’ digital transformation journeys, five of which we will delve into in this article (including selected highlights from the report):

1. ERP: An upgraded ERP is often the first step to digital transformation.
2. Cloud: Cloud-native data storage and streaming are increasingly accepted.
3. 5G: First manufacturers have successfully implemented private 5G use cases.
4. Sustainability: Digitalization is becoming a prerequisite to achieving sustainability objectives.
5. Maintenance: The journey toward predictive maintenance and remote monitoring continues.

Learning 1: An upgraded ERP is often the first step to digital transformation

Our analysis found that many manufacturers elect to prioritize upgrading their ERP systems to ensure their various data sources are connected before prioritizing other digital transformation initiatives. Since ERP systems are often the central nervous system of a business, prioritizing an updated ERP allows different departments to share and operate on the same data, reducing errors and improving efficiency.

Selected highlight: Celanese

In recent years, US-based chemical manufacturer Celanese has acquired several businesses or divisions from other companies. Celanese had been operating on a legacy SAP ERP system, and the acquired assets had different legacy ERP environments, making cross-section integration difficult. Celanese’s global CIO, Sameer Purao, did not want the company to invest in integrating the new acquisitions into older technology. Given this situation and Celanese’s adoption of a strategic, long-term approach to a scalable digital transformation plan, an ERP upgrade became necessary.

“Our previous ERP system had been a backbone, but it was close to 20 years old. Given its age, we didn’t want to invest in transforming until we upgraded that piece first. The acquisitions underscored that it wouldn’t make sense to invest in integrating them into older technology, so we opted to upgrade.” – Sameer Purao, senior vice president and global CIO, Celanese Corporation

In May 2023, Celanese announced that it had completed its upgrade to SAP S/4HANA. Further, since Celanese acquired DuPont’s Mobility & Materials (M&M) business just six months prior, it quickly cutover to upgrading M&M’s legacy ERP system as well, which is expected to be completed in the first half of 2024.

While there are other major aspects of Celanese’s digital transformation journey detailed in our report, this upgrade to its ERP provided a stronger backbone from which other digitalization solutions could be built. Another benefit of this upgrade is improved visibility and collaboration, enhancing transparency and teamwork and allowing for efficient data access across the enterprise.

Learning 2: Cloud-native data storage and streaming are increasingly accepted

The cloud market nearly doubled between 2020 and 2022, growing from $109 billion to $206 billion, based on our analysis of global cloud projects. While the COVID-19 pandemic certainly played a major role, it was not the only growth factor. Our analysis found that large-scale enterprise digitalization efforts and strong SaaS adoption also helped fuel this growth.

Cloud storage and data streaming allow companies to centralize and share their data with a smaller footprint than running their own on-premises servers, which comes with footprint and maintenance costs. Moving these services to the cloud also allows companies to scale without the need for significant capital investment in physical hardware.

Selected highlight: Michelin

In 2019, tire manufacturer Michelin started using Apache’s Kafka event streaming platform on-premises in its data centers to gain real-time insights and process data as continuous streams. However, as its operational footprint expanded, so did the resources it had to dedicate to maintaining the solution. By Q4 2019, Michelin’s IT department initiated its migration to the cloud, with Microsoft Azure as the cloud partner.

“One of the challenges with [streaming technology] Kafka was its operational complexity, especially as the footprint expanded across our organization. It’s a complex, distributed system, so we had to allocate a lot of our valuable technical resources and expertise to babysit it and keep it running.” – – Olivier Jauze, now CTO of Experiences Business Line, Michelin

By 2021, Michelin migrated its services to Confluent Cloud for Azure, a Kafka-based platform, to support its multi-cloud environment. Soon after, the company began exploring use case projects and has since migrated one of its most critical projects, online order management, to the cloud—replacing its on-premises orchestrator. By 2023, Michelin expanded its cloud-based event streaming architecture into several departments, including supply chain management, customer services, manufacturing, and R&D.

Through its adoption of cloud-native data storage and streaming, Michelin achieved the following benefits (among other things):

• Cost savings: Estimated 35% in cost savings in the cloud compared to on-premises operations
• Improved uptime: 99.99% uptime

Learning 3: First manufacturers have successfully implemented private 5G use cases

As 5G continues its public rollout globally, some manufacturers have successfully deployed private 5G networks to enable new use cases within their facilities. While faster speeds and lower latency may seem like key adoption drivers, our analysis found that improved reliability over Wi-Fi, enhanced cybersecurity, and the ability to access data locally are the core motivating factors.

Our analysis also found that during the public rollout of 5G, some companies did not simply dive into integrating 5G-specific technology. Instead, many integrated robust LTE solutions that were upgradable to 5G with relative ease (or so-called 4.9G solutions) once the technology evolved or became approved for industrial use.

Selected highlight: Airbus

To increase aircraft production and validation efficiency, European multinational aerospace corporation Airbus partnered with Ericsson, a Swedish multinational telecommunications company, in 2021 to implement private industrial 5G networks at 11 aircraft assembly manufacturing sites in Europe. The approach began with implementing 4G networks that either already had 5G capabilities or could seamlessly upgrade to 5G.

However, Airbus is not limiting this deployment to its European facilities. During a Q&A at the 5G Manufacturing Forum in November 2022, Hakim Achouri, the 5G and IoT solutions expert for digital aviation at Airbus, noted, “Airbus is going way beyond 11 networks at 11 sites, expanding beyond its core European manufacturing bases in France and Germany, to also deploy private 5G in Canada, China, Spain, the UK, and the US.”

With its implementation of private 5G networks at its production and assembly facilities, Airbus has realized the following benefits:

• Ability to implement advanced use cases: This includes site surveillance, efficient flight-to-ground data offloads, quality inspections, and the operation of automated guided vehicles (AGVs).
• Enhanced user experience: With increased speed, bandwidth, and reliability, employees at the production sites have access to more data, making operations smoother, more efficient, and more secure.
• Scalability through reusability: By developing a pattern in its strategy, Airbus was able to roll out private 4G/5G networks across its many sites with consistent quality and performance.

Learning 4: Digitalization is becoming a prerequisite to achieving sustainability objectives

We recently noted a trend of companies deploying digital twins to help realize their sustainability goals. But it is not simply digital twins assisting companies on this front—digitalization projects overall are helping companies monitor energy consumption, optimize resource usage, and reduce their environmental footprint in the manufacturing process.
Backing this awareness and trend toward sustainability are data points from our latest What CEOs Talked About report, where “sustainability” and related terms remained among the most discussed topics in boardrooms.

Selected highlight: TotalEnergies

French multinational energy and petroleum company TotalEnergies has publicly declared its ambition to achieve carbon neutrality by 2050. To meet this goal, the energy company has leveraged digital solutions to advance the implementation of sustainability measures on its offshore platforms.

For instance, TotalEnergies retrofitted their pipes with LoRaWAN-connected temperature sensors to detect gas leaks along their flare networks. As hydrocarbons are released, the temperature of the pipes significantly changes. When this change is detected, operators are alerted via emails for immediate action. This not only helps limit the release of hydrocarbons but also saves TotalEnergies money by reducing the loss of product.

Learning 5: The journey toward predictive maintenance and remote monitoring continues

According to our Predictive Maintenance and Asset Performance Market Report 2023–2028 (published in November 2023), the predictive maintenance market reached $5.5 billion in 2022. While the report notes several tailwinds supporting this interest and market growth, such as skill shortages and interest in reducing energy usage and CO2 emissions, costs are a major driver, as noted in our case studies report as well.

Equipment failure, especially during core operational hours, reduces productivity and adds repair expenses. To avoid these costs, companies often use preventative maintenance procedures, such as time-based inspections and repairs or condition criteria from sensors or physical measurements to trigger preventative intervention. However, intervening based on time can be inefficient since the equipment may not be in need of repair at that time, and data collection/monitoring requires personnel to conduct these tasks.

By implementing digital solutions, companies can remotely monitor the condition of critical equipment and establish conditions in which intervention is actually needed well before failure occurs.

Selected highlight: Battalion Oil Corp

US-based Battalion Oil Corp partnered with Novity, a US-based predictive maintenance solutions company, to pilot a predictive maintenance solution to detect valve leaks within their compressors and reduce unexpected compressor downtime. Initially, Battalion would sporadically measure valve cap temperatures using handheld devices to identify potential gradual leaks that could lead to a failure. While the checks were intended to be conducted daily, varying daily maintenance tasks and priorities often disrupted these important checks.

“Predictive automation is a game-changer for the oil and gas industry. By analyzing data in real-time and making accurate predictions about future events, drilling companies can optimize their operations to maximize efficiency, reduce costs, and improve safety. This technology has the potential to transform the way we do business and stay competitive in today’s market.” – John Smith, CEO of Oil and Gas Exploration Company

An initial step in the solution was to use a crank angle sensor and pressure transducers. However, physical crank angle sensors are usually the most difficult and expensive sensors to install, so the engineers developed a virtual crank angle sensor based on physics-based and data-driven methods using data from the pressure sensors.

After validating that the rotational position calculated by the virtual sensors matched the position provided by the physical sensors, engineers applied prognostic methods to the data from the virtual crank angle sensor and physical pressure sensors. The result was predicted gradual valve failures several weeks in advance—five to seven days on average before temperature checks indicated a gradual leak.

The digital transformation journey carries on

The Industrial IoT and Industry 4.0 Case Studies Report 2023 delves further into the above-mentioned and 18 other case studies of ongoing digital transformation projections. While these companies and many others are advancing in their digital transformation journeys, there is still a long road ahead for many companies, some of which still rely on analog, pen-and-paper methods in their facilities. Even still, many companies are already experiencing real value, e.g., Mercedes’ achieving 25% greater efficiency and Battalion observing signs of gradual valve failures several weeks in advance.

Digitalization has become more than a nice-to-have for manufacturers today—it has become crucial for them in their respective competitive landscapes. The market reflects this assessment: according to our enterprise IoT market dashboard, the IIoT market size in 2023 is approximately $145 billion, with a forecasted CAGR of 17.9% between 2023 and 2030.

Looking ahead, AI continues to become a major theme in companies’ digital transformation initiatives. According to our continual series What CEOs Talked About, the topic and its related terms have already been of high and growing interest in boardrooms throughout 2023. We see a plethora of generative AI projects across the board, even in the industrial space (which we will report on soon). We will continue to monitor this space and highlight interesting case studies from adopters.

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IoT Business News: Can you explain how the transition from ‘scan it’ to ‘sense it’ technologies will impact global retailers and supply chains? How will these changes impact end-users/ consumers?

Steve Statler: For global retailers and their employees, the transition from ‘scan it’ to ‘sense it’ will save precious resources. The traditional ‘scanning’ method retailers have used is implementing handheld scanners. This takes up employee time, a scarce resource that could be better spent elsewhere. Additionally, manual scanning is inherently inaccurate given its reliance on workers – if workers are not performing perfectly, which is typically the case in the real world, these results break down – and upon fixed infrastructure, which limits information to one moment or location. By using ambient IoT, radios don’t require expensive people or expensive readers. Ambient sensors are already pervasive and visibility is comprehensive across a number of buildings and delivery vehicles, meaning it has a low barrier of entry for global retailers.

What role will the ambient IoT play in this transition?

For the transition to a ‘sense it’ culture, the ambient IoT is essential. The technology builds on the learnings and automations used in 1st generation UHF RFID, adding cloud intelligence and utilizing commodity radios that are already pervasive. The pervasive nature of the UHF RFID, in combination with the ever emerging standards, will allow for a smooth transition.

How can businesses successfully transition their business from manual inventory scanning to sensor technology?

For businesses to successfully transition their business, it will require adoption of battery free Bluetooth tags, readers, and cloud applications that are designed and built for a serialized, digital product passport. A benefit of the transition is that there is hardly any transition labor for employees, as they do not need to be retrained for scanning.

How does ambient IoT improve upon traditional methods of supply chain management?

Ambient IoT improves upon traditional methods of supply chain management in a number of ways. The technology enables a transition to real-time visibility that is end to end across all spaces, including items in transit. This alone is an improvement given that traditional ‘scanning’ methods of supply chain management lack the opportunity to provide real time information. Ambient IoT allows for much higher accuracy. Simultaneously, ambient IoT allows for sensing of additional conditions, such as humidity levels.

What are the larger implications for business owners when implementing ambient IoT?

For business owners, improvements in visibility from ambient IoT allow for optimized business models. The technology allows for lower labor costs, given the lack of employees scanning and less capital tied up in inventory. Additionally, the technology allows for better intelligence and insights, meaning less out-of-stocks, better service levels, and higher sales. Finally, the product can allow for smaller store footprints, as it limits the necessity to order high stock of items.

In addition to these labor and inventory benefits, businesses that have already implemented the technology can capitalize upon the opportunity to transition to product as a service as well. Finally, for all companies, the tech allows for a better ability to comply with upcoming regulations that require visibility across supply chains for customers.

Are there any other use cases for ambient IoT within supply chain management?

As we transition from supply chains to demand chains, the ambient IoT will show, in real time, demand signals from stores or even homes. As sustainability becomes top of mind for many companies, the tech allows for circular product usage, meaning businesses can reuse and resell products in the aftermarket. With better tracking and a source of authentication and provenance for resellers, resellers can sell second hand products for higher costs with proof of validity, and producers can participate in the revenue from resale.

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Since the founding of our company, SAM has welcomed efforts by government agencies and regulators worldwide to raise consumer awareness about cybersecurity in the IoT space. These efforts benefit both consumers and the network operators connecting them to the digital world. Consumers benefit by being better informed about an IoT product’s security attributes at the “point of sale” and operators benefit as this increased awareness amongst consumers will make it easier to develop and sell new network-based security services.

The latest development comes from the United States, where the White House has introduced the “Cyber Trust Mark” program. This program aims to certify IoT devices bearing the label, ensuring they meet essential security attributes safeguarding consumers’ networks and device data. While voluntary, this initiative, led by the Federal Communications Commission, is set to begin implementation in 2024. This is part of an initiative that includes a collaboration between the White House and the National Institute of Standards and Technology (NIST) to establish cybersecurity standards tailored to routers.

These moves will have a positive impact on the IoT ecosystem on a variety of levels. Yet, while product labels will increase consumer awareness and education, they cannot address the ongoing evolution and fragmentation of IoT devices. Thousands seemingly hit the market each year, making “constant” security unattainable. Even a seemingly secure device could falter over time without proper software updates, which in reality, the average consumer does not do.

This fact is part of a trend that has led to a situation where most home and small business devices and networks lack adequate protection. This vulnerability arises due to various reasons, including the widespread use of consumer electronics devices that have become connected IoT devices through home routers. While some vulnerabilities may only be an inconvenience for some users, other can open the door to malicious activities. One of the most pressing challenges in the realm of IoT is the sluggish discovery-to-patching process by firmware vendors, leaving users exposed indefinitely. This issue highlights a critical gap in home security, where the timely resolution of IoT vulnerabilities should be a requirement, not a “luxury.”

However, for consumer electronics in general, it takes time to create a fix, to test it in the field and then to distribute it. And for IoT devices, it’s a different matter altogether, as numerous devices have minimal security and no ongoing security patch program. Or the devices are no longer on the market at all. This condition creates a significant window of opportunity for hackers who are well aware of these vulnerabilities and often have ample time to exploit them before the vendors issue a remedy, leaving end users vulnerable to attacks. Even when the patch is ready for deployment, there is still the question of how it will be deployed onto the users’ devices. Some devices can be updated via the corresponding app on the smartphone. Others, however, need to be updated manually – a lengthy and quite complicated process for even those who are tech savvy.

Katherine Gronberg, Head of Government Services at NightDragon, who works frequently with NIST and the White House on matters relating to IoT security, has commented: “With the explosion of IoT devices available from a wide variety source, consumers have until now not had any help in deciding what to buy or even to be mindful of security. The Cyber Trust Mark will allow consumers to identify products that have been designed and manufactured according to secure development guidelines and that offer some basic security features, most of which will likely not require any actions by the device user. While this program doesn’t apply to IoT devices that are already in use today, it will create a more informed customer and may make other parties in the ecosystem such as retailers or ISPs more conscious of the problem and might motivate them to take action.”

One action that the industry has seen recently is a renewed focus on routers, as seen in a recent security advisory issued by the US NSA, in which one of its recommendations was for consumers to exchange ISP-issued routers for ones they would purchase themselves. And there is another router-focused technique that more and more ISPs are using to help their customers with IoT network security, namely the “hot patching” measure, which uses a router-based software agent to provide protection for the router itself and every device connected to it.

Hot patching is designed as a “one stop” protection program in which an ISP would download an agent to a router to provide constant real-time monitoring and alerts. Hot patching is based on what is known as “deep packet inspection,” or DPI, which is a well-known and long-standing technique wherein the payload of packets traversing a data network is inspected and analyzed. The result empowers consumers with comprehensive router and device security, eliminating vulnerability monitoring and patching complexities.

While security labeling undoubtedly enhances consumer awareness and overall IoT security, the quest for constant security calls for a gateway-based solution. Such a solution can act as the ultimate backstop to industry and government initiatives, securing IoT devices and the connecting network.

Therefore, we believe the “Cyber Trust Mark” program will certainly be a great benefit for the consumer or “end user” and the increased awareness about IoT security it will raise gives ISPs an excellent opportunity to play a more proactive role that will be welcomed by their customers and which will increase IoT network security in meaningful ways.

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In the ever-evolving landscape of technology, the Internet of Things (IoT) and Digital Twin technology stand out as pivotal innovations. IoT, with its network of interconnected devices, and Digital Twins, which are virtual replicas of physical systems, are revolutionizing industries from manufacturing to urban planning.

This article presents a comprehensive exploration of IoT and Digital Twin technology, detailing their individual characteristics, the benefits of their integration, and the challenges faced. It offers insights into current trends and future directions, providing a well-rounded perspective on these transformative technologies.

The Convergence of IoT and Digital Twin Technology

IoT’s network of sensors and devices captures real-time data from the physical world. This data is crucial for creating Digital Twins, which are dynamic virtual models of physical objects or systems. By integrating IoT data, Digital Twins can simulate real-world conditions, predict outcomes, and optimize processes.

Understanding IoT

IoT involves a network of physical objects (‘things’) embedded with sensors, software, and other technologies to connect and exchange data with other devices and systems over the internet. These devices range from ordinary household items to sophisticated industrial tools.

1. IoT in Industry: In industrial settings, IoT devices monitor and optimize manufacturing processes, improve supply chain logistics, and enhance product lifecycle management.

2. Consumer IoT: In the consumer segment, IoT encompasses smart home devices, wearable health monitors, and connected vehicles.

Digital Twin Technology Explained

A Digital Twin is a virtual representation that serves as the real-time digital counterpart of a physical object or process. It is used for simulation, analysis, and control.

1. Application in Manufacturing: In manufacturing, Digital Twins are used to create and test prototypes, predict equipment failure, and optimize production lines.

2. Urban Planning and Infrastructure: Digital Twins simulate entire cities or infrastructure systems, aiding in urban planning and management.

The Synergy of IoT and Digital Twins

1. Real-Time Data and Analysis: IoT feeds real-time data into Digital Twins, allowing for accurate simulations and analyses. This integration helps in predictive maintenance, reducing downtime in industrial settings.

2. Enhanced Decision-Making: By leveraging IoT data, Digital Twins enable better decision-making. They provide insights into system performance, potential failures, and maintenance needs.

3. Customization and Innovation: The combination allows for customization of products and services, driving innovation in various sectors like healthcare, automotive, and aerospace.

Benefits of IoT and Digital Twin Integration

1. Operational Efficiency: This integration enhances operational efficiency by enabling real-time monitoring and predictive maintenance.

2. Cost Reduction: It reduces costs by identifying potential issues before they become critical, minimizing downtime.

3. Sustainability: It promotes sustainability by optimizing resource usage and reducing waste.

Challenges and Solutions

1. Data Security and Privacy: The vast amount of data generated poses security and privacy risks. Implementing robust cybersecurity measures is essential.

2. Interoperability and Standardization: Ensuring interoperability among diverse IoT devices and systems is challenging. Developing universal standards and protocols is key.

3. Complexity and Scalability: Managing the complexity and ensuring the scalability of these systems require continuous innovation and investment.

Future Prospects and Emerging Trends

1. Advancements in AI and Machine Learning: Integrating AI with IoT and Digital Twins will enable more advanced predictive analytics and automation.

2. 5G Integration: The rollout of 5G will enhance IoT capabilities, offering faster, more reliable connections for Digital Twins.

3. Sustainable Development: There’s an increasing focus on using IoT and Digital Twins for sustainable development, particularly in smart city projects and environmental monitoring.

Conclusion

The integration of IoT and Digital Twin technology is a formidable force driving innovation and efficiency across multiple industries. While challenges exist, the potential benefits are immense. As these technologies continue to evolve, their combined impact on industry, urban development, and sustainability will be profound, marking a new era of digital transformation and innovation.

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The advent of the Internet of Things (IoT) has revolutionized how we interact with technology, integrating the physical and digital worlds in unprecedented ways. However, the proliferation of IoT devices also brings challenges, particularly in managing their lifecycle effectively. IoT Device Lifecycle Management (DLM) is crucial for the efficient and secure operation of these devices. This article delves into the various stages of IoT DLM, the challenges involved, and the strategies for effective management.

Understanding IoT Device Lifecycle Management

IoT DLM refers to the processes and methodologies used to manage an IoT device from its inception to retirement. It encompasses the entire spectrum of an IoT device’s existence, including design, development, deployment, operation, maintenance, and eventual decommissioning. Effective DLM is vital for ensuring device security, functionality, and overall network health.

1. Design and Development Stage

The lifecycle of an IoT device begins with its design and development. This stage involves defining the device’s purpose, functionality, and the environment in which it will operate. Key considerations include:

• Security by Design: Implementing security measures at the design phase is critical. This includes hardware and software security features, data encryption, and secure communication protocols.
• Scalability and Compatibility: The device should be designed to be scalable and compatible with different platforms and technologies.
• Energy Efficiency: Many IoT devices are deployed in locations where power sources are limited, making energy efficiency a crucial design factor.

2. Manufacturing and Deployment

Once designed and developed, the next stage involves manufacturing and deploying these devices. This phase must ensure that the devices are built according to the specified design and that they are deployed correctly in their intended environment.

• Quality Assurance: Rigorous testing for quality and compliance with standards is essential.
• Deployment Strategy: The deployment should be planned to minimize disruptions and ensure seamless integration with existing systems.

3. Operation and Maintenance

This is the longest phase in the lifecycle of an IoT device. Key activities include:

• Monitoring and Management: Continuous monitoring for performance and security is vital. IoT devices generate vast amounts of data, and their performance must be consistently managed.
• Software Updates and Patch Management: Regular software updates and patches are essential to address security vulnerabilities and enhance functionality.
• Remote Troubleshooting and Support: Given the often remote and distributed nature of IoT devices, remote troubleshooting capabilities are crucial.

4. Data Management and Analysis

IoT devices collect and transmit data, necessitating effective data management strategies.

• Data Storage and Analysis: Efficient storage solutions and advanced analytics capabilities are required to derive meaningful insights from the data collected.
• Data Privacy and Compliance: Adhering to data protection regulations and ensuring user privacy is paramount.

5. Decommissioning and End-of-Life Management

Eventually, IoT devices reach their end of life, whether due to technological obsolescence, wear and tear, or other factors. This stage involves:

• Safe Decommissioning: Ensuring that devices are decommissioned safely, without posing risks to the environment or data security.
• Data Sanitization: Properly erasing stored data to prevent unauthorized access or data breaches.
• Recycling and Disposal: Adhering to environmental standards for recycling and disposing of electronic waste.

Challenges in IoT Device Lifecycle Management

Managing the lifecycle of IoT devices presents various challenges:

• Scalability: As IoT networks grow, managing an increasing number of devices becomes complex.
• Diverse Device Ecosystem: IoT encompasses a wide range of devices with different functionalities, requiring diverse management strategies.
• Security Risks: IoT devices are often targeted by cyberattacks, making security a continuous concern.
• Regulatory Compliance: Compliance with various regional and industry-specific regulations can be challenging.

Strategies for Effective IoT Device Lifecycle Management

To address these challenges, several strategies can be employed:

• Implementing Standardized Protocols: Adopting industry-standard protocols can help in managing devices efficiently.
• Automated Tools and Platforms: Leveraging automation for device management can significantly reduce the complexity and improve efficiency.
• Regular Security Audits: Conducting regular security audits and assessments can help in identifying and mitigating risks.
• Training and Awareness: Ensuring that staff are trained and aware of best practices in IoT DLM is crucial for its success.

The Future of IoT Device Lifecycle Management

Looking ahead, IoT DLM is set to become more complex and crucial. The integration of AI and machine learning can provide more intelligent and automated management solutions. The adoption of edge computing can also enhance the efficiency of IoT operations. Moreover, as IoT continues to evolve, there will be a greater emphasis on sustainable practices in device lifecycle management, focusing on minimizing environmental impact.

Conclusion

Effective IoT Device Lifecycle Management is critical in ensuring the efficient, secure, and sustainable operation of IoT devices. As IoT continues to grow and permeate various sectors, the challenges in managing these devices will

also increase. However, with the right strategies, tools, and awareness, these challenges can be addressed, leading to more robust, efficient, and secure IoT ecosystems. The future of IoT DLM lies in its ability to adapt to evolving technologies and requirements, ensuring that IoT devices continue to be a driving force in the connected world.

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In the landscape of modern technology, the Internet of Things (IoT) stands out as a revolutionary paradigm, embodying a network of interconnected devices that communicate and exchange data seamlessly. This burgeoning web of devices, ranging from smart thermostats to autonomous vehicles, has the potential to reshape our daily lives, enhance efficiency, and open new avenues for innovation. However, the swift advancement and integration of IoT into various sectors also raise profound ethical questions that warrant careful consideration.

Navigating the Complexities: A Deep Dive into the Ethical Implications of IoT Proliferation

Privacy Concerns

The very essence of IoT is to collect, analyze, and transfer data across devices, which inherently raises significant privacy concerns. Smart devices in homes, offices, and public spaces can track individual movements, habits, and even predict future behavior. This granular level of data collection poses a threat to individual privacy. It is essential to question who owns this data, how it is used, and what measures are in place to protect it from misuse.

Companies and policymakers must ensure that individuals retain autonomy over their personal information. This includes transparent policies on data collection, the option to opt-out, and robust security measures to prevent unauthorized access. The European Union’s General Data Protection Regulation (GDPR) is an example of a legislative framework aimed at protecting user privacy, which could serve as a model for other regions.

Security Risks

The interconnected nature of IoT devices means that they are often part of a network, and if one device is compromised, it could potentially lead to a chain reaction affecting the entire system. IoT devices have already been used to launch massive Distributed Denial of Service (DDoS) attacks, and the potential for more sophisticated cyber threats looms large.

Ensuring the security of IoT devices is not just a technical challenge but an ethical imperative. Manufacturers are morally obligated to implement stringent security measures before releasing products into the market. This includes regular updates, secure software by design, and prompt responses to vulnerabilities.

Autonomy and Human Agency

IoT devices are increasingly capable of making decisions without human intervention, which raises concerns about human agency. For instance, smart home systems can regulate temperature and lighting, and autonomous vehicles can navigate roads with minimal or no human input. While this automation can offer convenience and efficiency, it also raises questions about the degree of control humans should relinquish to machines.

The ethical dilemma revolves around finding the balance between harnessing the benefits of automation and maintaining human decision-making power. There should be clear boundaries on the autonomy of IoT devices, and humans should have the ultimate say in critical decisions, especially those with moral ramifications.

Inequality and Accessibility

The proliferation of IoT has the potential to widen the socio-economic divide. Access to the benefits of IoT technology is often contingent on economic status, with the affluent having more opportunities to integrate IoT into their lives. This digital divide could lead to a society where there is an inequality of convenience, efficiency, and even health outcomes, as IoT continues to expand into areas like healthcare and city planning.

To address this ethical concern, it is vital to advocate for inclusive technology policies that ensure IoT devices are accessible and affordable. Governments and organizations can play a role in subsidizing costs or providing IoT solutions in public services to bridge the gap.

Environmental Impact

The production, operation, and disposal of billions of IoT devices have significant environmental implications. The demand for rare earth elements and the energy consumption of IoT devices contribute to ecological degradation. Additionally, electronic waste is a growing concern, as many IoT devices have short lifespans and are not designed to be recyclable.

Ethically, there is a responsibility to consider the environmental footprint of IoT. This includes designing products with longer lifespans, energy-efficient operations, and sustainable materials. Furthermore, recycling programs for IoT devices should be standard practice, and consumers should be educated about their environmental responsibilities.

Conclusion

The Internet of Things heralds a new era of technological integration, but with it comes a complex web of ethical considerations that must be addressed proactively. Privacy, security, autonomy, inequality, and environmental impact are just some of the ethical challenges that the IoT presents. As we move forward, it is imperative for stakeholders, including technologists, policymakers, and the public, to engage in open dialogue and develop a framework that prioritizes ethical considerations in the evolution of IoT.

The conversations about the ethics of IoT are as essential as the technology itself. They will shape the norms, laws, and regulations that guide the development and deployment of IoT technologies. Only with a concerted and collaborative effort can we ensure that the IoT serves as a tool for positive change, enhancing lives while respecting individual rights and ecological boundaries. As we advance into this connected future, our moral compass must navigate the course as much as the innovative spirit that drives the IoT forward.

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In the ever-expanding universe of the Internet of Things (IoT), security is not just a feature but a foundational necessity. With billions of devices connected and communicating, the potential for data breaches, unauthorized access, and other cyber threats grows exponentially. In this context, IoT security protocols are essential to ensure that the communication between devices, and from devices to servers, remains confidential and tamper-proof. Here, we explore the current landscape of IoT security protocols, the challenges they face, and the future direction of securing IoT networks.

The Current State of IoT Security Protocols

IoT devices, ranging from consumer products like smart thermostats to industrial sensors monitoring critical infrastructure, are often built with convenience and cost-effectiveness in mind. However, this focus can sometimes come at the expense of robust security measures. The protocols governing the security of these devices are as varied as their applications.

1. Transport Layer Security (TLS) and Secure Sockets Layer (SSL): TLS and its predecessor, SSL, are cryptographic protocols designed to provide secure communication over a computer network. In the IoT space, TLS/SSL is commonly used to secure the connection between a device and a cloud server, ensuring that data remains private and integral.

2. Datagram Transport Layer Security (DTLS): For IoT devices that rely on UDP, which is common in real-time applications, DTLS offers a way to secure these communications. It is similar to TLS but adapted for datagram protocols.

3. Extensible Messaging and Presence Protocol (XMPP): XMPP is an open standard for message-oriented middleware based on XML. It offers a set of protocols for message-oriented communication with mechanisms for security.

4. Constrained Application Protocol (CoAP): CoAP is a specialized web transfer protocol for use with constrained nodes and networks in IoT. It can be used with DTLS to provide a secure communication channel.

5. Z-Wave and Zigbee: These are communication protocols for low-energy radio waves often used in home automation, with built-in security layers to encrypt messages between devices.

6. Message Queuing Telemetry Transport (MQTT): MQTT is a popular IoT publish-subscribe network protocol that can be secured with TLS.

Challenges Facing IoT Security Protocols

The challenges in IoT security are manifold, stemming from both the variety of devices and the complexity of the network architectures. Here are the key challenges:

1. Resource Constraints: Many IoT devices have limited computational resources and cannot support traditional web-grade encryption methods.

2. Diversity of Devices: The IoT ecosystem is vast, with a wide range of devices that have different capabilities and security needs.

3. Scalability: Security protocols must be able to scale effectively as billions of new devices come online.

4. Lifecycle Management: IoT devices often have long lifecycles, and security protocols must be updatable to respond to new threats over time.

5. Interoperability: With so many different protocols and manufacturers, ensuring that security measures are interoperable across devices and systems is a challenge.

Advanced Security Protocols for IoT

As the IoT industry evolves, so do the strategies to secure it. Here are some advanced protocols and techniques being developed and implemented:

1. Lightweight Cryptography: NIST is working on standards for lightweight cryptography intended for constrained devices, which will be more suitable for the IoT environment.

2. Public Key Infrastructure (PKI): PKI provides a scalable method for secure device authentication and encryption key distribution.

3. Elliptic Curve Cryptography (ECC): ECC provides the same level of encryption as RSA but uses smaller keys, which are more suitable for IoT devices.

4. Quantum-resistant algorithms: With the potential threat of quantum computing, there’s a growing focus on developing security algorithms that would be resistant to quantum attacks.

5. Secure Software Updates: Ensuring that devices can be securely updated is crucial for responding to vulnerabilities as they are discovered.

Implementing IoT Security Protocols

The implementation of robust security measures is as critical as the development of the protocols themselves. Here are key considerations for implementation:

1. Default Security: Devices should come with security features enabled by default, requiring little to no configuration from the user.

2. Regular Updates: Manufacturers must provide regular firmware updates to address security vulnerabilities and ensure devices stay secure over their lifespan.

3. User Education: Users should be informed about the importance of security and how to manage their devices securely.

4. Multi-layered Security: Security should be implemented in layers, including secure boot, transport layer security, secure storage, and intrusion detection systems.

The Future of IoT Security

Looking forward, the IoT industry must continue to prioritize security to protect against evolving cyber threats. Here are potential future developments:

1. AI and Machine Learning: These technologies can be used to detect anomalies in network behavior, potentially identifying and neutralizing threats in real-time.

2. Blockchain for IoT Security: Blockchain technology could enable secure, tamper-proof systems for IoT device authentication and firmware updates.

3. Integration of Security in IoT Standards: As new IoT standards are developed, integrating security as a core component will be crucial.

4. Government Regulation and Compliance: We may see more government regulation aimed at improving IoT security, similar to the GDPR for data protection.

5. Universal Security Standards: Efforts may be put toward creating universal security standards that can be applied across devices and industries.

Conclusion

The complexity of IoT security is significant, and the stakes are high. As the IoT continues to grow, effective security protocols must be developed and implemented to protect privacy and ensure the safe and reliable operation of connected devices. The future of IoT depends not just on innovation in connectivity and functionality but equally on the strength and adaptability of its security protocols. The journey toward a secure IoT ecosystem is ongoing, and it requires the concerted effort of manufacturers, software developers, security experts, and regulatory bodies.

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IoT Analytics, a leading provider of market insights and strategic business intelligence for the Internet of Things (IoT), AI, Cloud, Edge, and Industry 4.0, today released the results of the quarterly company earnings call analysis.

This analysis is based on a comprehensive dataset of Q3 2023 earnings calls from 4,000+ leading US-listed firms. The findings from Q3 2023 show that three pivotal themes are currently trending in CEO discussions: 1. AI and Generative AI, 2. AI chips, and 3. sustainability. These influential topics have captivated boardrooms worldwide and are shaping the future investment priorities for companies across various industries.

KEY INSIGHTS:

• According to the latest “What CEOs talked about” report, three themes gained noticeable traction in earnings calls in Q3 2023: 1) AI and most of its related application topics, 2) AI chips, and 3) sustainability.
• ChatGPT is losing traction as the AI keyword of choice, and economic worries and uncertainty appear to be easing.

KEY QUOTES:

• Knud Lasse Lueth, CEO at IoT Analytics, comments: “I’m encouraged to see that as concerns of a potential recession fade, the emphasis on sustainability remains robust. AI, particularly generative AI, is emerging as the dominant theme of 2023. It’s evident that generative AI isn’t just another passing trend like blockchain or the metaverse. CEOs must take decisive action now to be at the forefront of this technological revolution.”
• Philipp Wegner, Principal Analyst at IoT Analytics, adds that “The surge in discussions around AI strategy and AI chips in Q3 2023 is a clear indicator that businesses are moving beyond the ‘what’ and ‘why’ of AI to the ‘how.’ CEOs are now grappling with the logistical challenges of integrating AI into their existing infrastructures, and that’s where the real transformation begins.”

The big picture

In Q3 2023, topics related to economic worries remained prominent in boardroom discussions globally but continued to decline quarter-over-quarter (QoQ). Mentions of the most talked about topic, inflation, dropped 5% QoQ to 47% of calls. Supply chain worries saw the smallest drop, 2% QoQ, while recession experienced the largest drop of 38% QoQ, now appearing in 11% of earnings calls.

Key quote on the macro environment: “Our base case today assumes a mild recession.” – Mark Mason, CFO, Citigroup, July 14, 2023

Key rising themes in Q3

AI : In Q3 2023,the broader topic of AI rose fairly steeply to 29% (+37% QoQ), and its subfield generative AI continued to rise in importance to 10% (+56% QoQ). During their quarterly earnings calls, CEOs appeared to be separating AI and many of its subfield topics from discussions about ChatGPT, which declined this QoQ (as discussed below in “Declining themes”).

Our analysis also shows a marked incline in the mentions of AI strategy and AI infrastructure, rising 128% and 103% QoQ, respectively.

Key CEO quote on generative AI: “Generative AI is at the forefront of customer conversations. However, enterprises are also realizing that they cannot have an AI strategy without a data strategy to base it on.” – Frank Slootman – CEO, Snowflake, August 23, 2023

AI chips : Mentions of GPUs greatly outpaced mentions of CPUs, with the former climbing 102% QoQ to 2.8% in Q3 2023 earnings calls and the latter climbing 47% QoQ to 1.7%. Coinciding with this trend is GPU chipmaker Nvidia climbing 146% in mentions to 1.9% compared to Intel only climbing to 1.2% (+48% QoQ) and AMD to 0.8% (+11% QoQ) in reference to AI chips.

“We’ll actually take the Nvidia hardware as fast as Nvidia will deliver it to us. Tremendous, tremendous respect for Jensen and Nvidia. They’ve done an incredible job. And frankly, I don’t know if they could deliver us enough GPUs.” – Elon Musk – CEO, Tesla, July 19, 2023

Sustainability, energy transition, and renewable energy : Mentions of sustainability, energy transition,and renewable energy slightly rebounded to 21.4% (+9.2% QoQ), 5.3% (+15.4% QoQ), and 5.2% (+10.9% QoQ), respectively. These topics are lower than their peaks in Q1 2022; however, since Q3 2022, each topic has remained generally consistent in its percentage of earnings call mentions.

“We don’t talk a lot about grid … but increasingly here in the U.S., people appreciate how critical grid modernization will be to the energy transition.” – Larry Culp – CEO, General Electric, August 28, 2023

Declining themes in Q3

ChatGPT: As mentioned, ChatGPT experienced a decline in its number of mentions in the earnings reports, dropping 34% QoQ to 2.5%. Last quarter, we saw a transition from CEOs specifically discussing ChatGPT to them discussing enterprise-wide applications of generative AI. That transition still appears to be occurring, as the other AI topics saw significant rises in mentions:

• LLM rose 98% QoQ to approximately 2%.
• Computer vision rose 60% QoQ to 1%.
• Chatbot rose 58% to 1%.

Remote work: The topic of remote work dropped the most of the key topics we tracked, falling 48% QoQ to 0.5% of earnings calls. This decline comes amid new concerns of a COVID-19 infection surge due to a newly detected SARS-CoV-2 variant. Early studies show that antibodies from vaccinations and past infections may enable immune systems to detect and combat the variant sufficiently, which could be easing boardroom concerns of a significant surge (of note, mentions of Corona dropped 8% QoQ to 0.9% of earnings calls).

This decline in discussions around remote work also comes amid increasing so-called return-to-office mandates, with 90% of US companies expected to require employees to work in person at least a few days a week by the end of 2023.

Deep-Dives

#1 AI

In the first quarter of this year, ChatGPT was the hot new (AI) topic for CEOs. In Q2, generative AI shared the stage with ChatGPT as AI proponents discussed other generative LLMs. Now, in Q3, ChatGPT is no longer the hot topic, but rather, generative AI, AI strategy, AI infrastructure, and AI Chips—with companies realizing that ChatGPT is just one tool of many in the new AI portfolio. However, AI is an evolving topic, and OpenAI continues to conduct research and development into ChatGPT, so there is no reason to suspect ChatGPT will completely fade from discussions in the near term.

AI use cases like coding and chatbots are starting to climb

AI use cases like coding and chatbots are also starting to climb, but they have not yet reached the same level as the aforementioned topics. These use cases will likely roll out in the next 6–12 months, so we assess these (and other) topics to grow in Q4 and into 2024.

Of the AI umbrella (not counting AI chips, which we dive into more below), generative AI was the most talked about by CEOs. LLMs, AI infrastructure, and AI strategy saw significant rises in discussions, though they only appear within 1%–2% of the earnings reports overall.

Technology and communication services sectors lead in AI discussions

The technology and communication services sectors lead most AI and AI subfield discussions. 67.6% of technology sector earnings calls mentioned the general topic of AI, while 50.8% of the communications services sector earnings calls discussed it. Generative AI was also fairly high for both sectors, with it mentioned in 33.9% of technology sector earnings calls and 24.2% in communications services sector calls. Of note, the industrial sector showed the third highest interest in generative AI, with 6% of their earnings calls mentioning the topic.

Coinciding with the topic of AI and subfields is a rise in AI strategy and infrastructure discussions. Though hovering between 0.5% and 1% of earnings calls, they rose 128% and 103%, respectively. This could be a sign that as companies look to AI to increase productivity and assess future investments, they are also considering AI implementation strategies.

Key CEO quotes on AI and its subfields

“Generative AI really [instantiated] AI in software, which means developers play a bigger role rather than data scientists. And that’s where you really see the business impact, and I think that impact will be large over the next three to five years…” – Dev Ittycheria – CEO, MongoDB, September 01, 2023

“The application of AI has both horizontal and vertical components. Horizontal is building out the AI infrastructure.” – Anirudh Devgan – CEO, Cadence, July 24, 2023

#2 AI Chips

Also coinciding with the rise in AI discussions are GPUs, most noteworthy Nvidia, the leading GPU vendor in the market. While general discussions of GPUs rose approximately 102% QoQ to 2.8% of earnings calls, Nvidia rose 146% QoQ to 1.9%. On the chipset front, CPUs only saw a 47% climb QoQ to 1.7% of earnings calls. Meanwhile, on the vendor front, Intel saw a decent climb in discussions (~48% QoQ), and AMD a subtle climb (~10% QoQ).

GPUs are becoming the core of choice for AI applications largely due to their architectural advantages over CPUs. While CPUs are optimized for task switching and fast sequential execution with only a few, large cores, GPUs often have many more, smaller cores designed for parallel processing. Deep learning involves performing many mathematical operations, including floating-point arithmetic (often used in rendering graphics) simultaneously, making GPUs a natural fit.

GPUs also offer better cost efficiency for scaling. Though more expensive than CPUs, the time savings they offer for training large language models can more than compensate for that cost. Given that the race is on for AI market dominance, time can actually be money in this case.

Key CEO quotes on AI chips

“I think you can look at 2023 as really a year of planning for AI, because as I said, there’s tons and tons of GPUs being purchased.” – Jayshree Ullal – CEO, Arista Networks, July 31, 2023

#3 Sustainability

Since their peaks in Q1 2022, the rate of discussions of sustainability, emissions, energy transition, and renewable energy in earnings calls have remained generally consistent each quarter, with each topic generally ticking up and down QoQ. This consistency signals a continued awareness of climate impact considerations in the sectors without it being a growing discussion topic in general.

That said, our analysis showed a general trend of CEOs emphasizing real-world projects for energy transition and renewable energy. It is worth highlighting that this emphasis comes amid a year (even quarter) of record temperatures. The US National Oceanic and Atmospheric Administration (NOAA) found that July 2023 had the warmest monthly sea surface temperature of any month and declared August 2023 the warmest August since NOAA started keeping global climate records 174 years ago.

Basic materials, utilities sectors and EMEA companies lead discussions on sustainability

The basic materials and utilities sectors led the discussions on sustainability—45.7% and 40.9% of their respective earnings calls—with utilities taking the general lead in discussions of emissions, climate, batteries, and renewable energy (including solar power). Of note, the energy sector had the second-highest mentions of emissions (39.3%) and the fourth-highest mentions of sustainability (30.8%).

By region, our analysis showed that European, Middle Eastern, and African (EMEA) companies talked about sustainability the most in Q3 2023, with mentions rising approximately 17% QoQ to 38.5%. Meanwhile, Asia-Pacific (APAC) companies continued to increase their number of mentions over North American companies—the former reaching 28.1% in Q3 2023 while the latter remained stagnant around 17%.

Key CEO quotes on sustainability, energy transition, and renewable energy

“The main areas of investment will be in generative AI, next-generation social infrastructure, and renewable energy.” – Junichi Miyakawa – CEO, SoftBank, August 06, 2023

“The downturn in the global economy is not—across the board—even, and consequently, there [is] still a lot of capital being moved […] And it holds for the energy transition, which is almost a given that we have to move forward one way or another.” – Tom Erixon – CEO, Alfa Laval Corporate AB, July 20, 2023

What it means for CEOs

5 things CEOs should ask themselves based on findings in this report:

1. Generative AI and data strategy: How is our company planning to leverage generative AI? Are we strategizing how to deal with this technology breakthrough and do we have a solid data strategy to ensure its effective application? Are we willing to play the “waiting game” and become a smart follower as other companies charge ahead with their generative AI plans.
2. AI chips: With GPUs seemingly becoming the hardware of choice for AI processes, how are we positioned in terms of hardware infrastructure? Do we have access (directly or through strategic partners) to the compute capacity we may need?
3. Macroeconomic concerns: With inflation, supply chain worries, and recession discussions seemingly on the decline, are we prepared for a potentially better than anticipated 2024?
4. Remote work and in-person mandates: With some companies calling employees back to the office and others continuing to operate remotely or flexibly hybrid, what are we communicating to our teams?
5. Sustainability: With an increasing focus on sustainability, what is our strategy to achieve net-zero? Are we really doing enough and have we baked in unforeseen challenges into our calculations, such as high electricity demand through the upcoming AI wave?

What it means for those serving CEOs

There is an opportunity for employees, service providers and other stakeholders to help CEOs excel at the topics they care about. Here are 5 questions those people serving CEOs could ask themselves based on findings in this report:

1. Generative AI implementation: How are we helping the CEO prioritize and implement generative AI projects? Are we helping in aligning the teams behind the opportunity and help secure the infrastructure that may be needed?
2. Making the most of generative AI: Beyond ChatGPT and obvious use cases such as chatbots or coding, what other generative AI technologies are on the horizon that might be relevant to our operations? How can we ensure that our CEO is briefed on these potential opportunities?
3. Economic pulse: While macroeconomic concerns like inflation and recession are declining in discussions, how are we ensuring that our CEO gets regular, concise, and clear updates on these topics and their potential impacts on our operations?
4. Work model adaptation: How can we help the CEO formulate a remote/hybrid work policy that ensures productivity, ongoing learning, employee happiness and safety for the employees?
5. Sustainability initiatives: Do the company’s current projects and investments reflect the increasing importance of sustainability and renewable energy? How can we help the CEO better communicate these initiatives to shareholders and the public?

The post What CEOs talked about in Q3/2023 appeared first on IoT Business News.

It’s become common for providers in the IoT industry to claim that they can make deployments easy for their customers. But as we move beyond the inflated expectations of the last decade and into the real work of deploying connected solutions that deliver meaningful value, it’s time to acknowledge what leaders in the space already know: getting IoT right is hard.

As an architect of cloud-native IoT connectivity systems with experience supporting million-scale deployments around the world, I’ve seen this first-hand. Even today, every new deployment uncovers new challenges.

IoT technology holds the potential to transform operations and industries, but unlocking that potential requires expertise, effort, and honesty. Success in IoT demands broad expertise in hardware design, software development, connectivity management, hyperscale platform integration, along with deep ecosystem understanding and strong project management capability. And that only accounts for the whiteboard environment. Deployments take place in the real, physical world and add environmental challenges like extreme heat and cold, dust, vibration, and condensation. Few organizations can claim mastery in more than a handful of these functional areas, and even fewer can add experience with IoT deployment. In this environment, it’s easy to underestimate technical challenges and capital requirements, and to see ROI horizons extend well past initial expectations.

Connectivity stands as one of the foundational technologies underlying today’s IoT, with more providers to choose from than ever before. And IoT MVNOs have served the ecosystem well over the past decade by packaging connectivity in a way that works for IoT. But a reliable connection is only the beginning of the journey. Organizations with IoT experience have learned that attractively-priced mobile data and a coverage map alone won’t address the technical and commercial challenges of a full-scale IoT deployment.

Armed with the lessons learned from previous deployments, experienced organizations are now demanding broad expertise in hardware design, software development, connectivity management, hyper-scale platform integration, ecosystem understanding, and project management capability. These organizations have first-hand experience of the hurdles and challenges that must be overcome to succeed in IoT and have effectively shifted their focus from component cost to total cost of ownership.

As an example, they’ve learned that what works for a few hundred devices may not work as a project scales into the thousands or millions. This is especially true when it comes to the connectivity management portal, which must include not only the necessary tools for granular network management but also the automation capabilities to ensure effective management at scale. For example, if a connectivity provider can offload encryption and authentication protocol overhead to reduce both total data usage and minimum hardware spec, TCO will be lower even if the per-MB cost for data is higher than that of a bare-bones provider.

Scale also plays a huge role when it comes to provisioning devices and integrating data with hyperscale platforms like AWS and Microsoft Azure. Assigning credentials manually is easy enough at the prototyping stage, but the move to full production requires a flexible, central approach to device provisioning, with the ability to update credentials remotely if needed. Hard-coded credentials represent not only a manufacturing challenge but a data security risk in the field.

These are just a few examples of the challenges that organizations may encounter as they progress toward a large-scale IoT deployment. When working with partners, companies eager to take advantage of IoT capabilities should be skeptical of any claims to “make it easy.” Instead, they will be well-served to look for partners who know it’s hard and have the experience to look around corners and address issues before they arise. IoT MVNOs in particular will distinguish themselves in a crowded field by demonstrating that their service offering goes well beyond simple device connectivity to help control total project cost and ensure success in the field.

With deployment complexity and total cost of ownership in mind, companies seeking to create value with connected solutions should consider these factors to help identify the technology partners who can truly serve them on their journey:

Adding real value: Commodity providers may offer attention-grabbing rates to bring new customers in, but their service to the customer needs to go deeper. Enterprises should look for solutions beyond connectivity and demand evidence of capability regarding troubleshooting, data collection automation, device operation, and connection management.

Accelerating speed to market: Many deployments get stuck somewhere between the whiteboard, the prototype, and the marketplace, and commodity-level solutions may not be well-equipped to provide value in the present era of IoT. Will a technology provider accelerate testing with automated functions where appropriate? Does the connectivity provider still answer your calls after devices are connected? Can you count on a robust API to help automate management of large-scale deployments, along with full-featured network management capability to keep operations running smoothly? Most organizations don’t have massive teams dedicated to IoT deployments. They should expect to work with partners who can accelerate deployment with active troubleshooting, experienced solutions architecture guidance, and a full set of tools for managing connections, minimizing data use, and integrating with hyperscale cloud platforms.

Supporting success at scale: Is the solutions provider scaling active deployments to meet IoT device connectivity metrics? Some providers boast about how many IoT connections they are adding. A more meaningful metric is how many connections their existing customers are adding. This metric shows that the provider is working with customers to overcome technical challenges. Furthermore, existing customer growth indicates a provider’s willingness to provide more capabilities, experience, and expertise to promote successful deployments.

Delivering strong partnerships: No single provider can address every technical challenge because issues in IoT require different areas of expertise, with technology deployed in devices on the edge, core networks, cloud operations, and in-house IT environments. Credibility within a specialized provider ecosystem gives differentiated vendors access to experts who can solve their customers’ most serious challenges. Does the solutions provider offer relationships with ecosystem partners? Do they have a good track record of completed projects? Can they call on an existing to address use cases that would otherwise require a customer to seek multiple point solutions outside of their normal area of expertise?

Providing expert support: The questions that tend to matter most in IoT are simply beyond the scope of standard tech support. Will a prospective partner offer IoT-first support with the knowledge and experience to answer questions and provide consultative guidance? Do they see the value in protecting your company’s IT investment? Can they anticipate the challenges specific to e.g. securing IoT-connected devices from data breaches? Can they guide your team through advanced techniques to reduce data overhead, offload credentials management, or establish data peering with hyperscaler VPCs?

As we look ahead to 2024, IoT has moved well beyond the hype of the past decade and is now delivering real value across industries and organizations. Acknowledging that the path to value isn’t easy is the first step to Providers differentiate themselves with connectivity and hardware design, software development, connectivity management, hyper-scale platform integration, deep ecosystem understanding, and strong project management. When we look beyond one-dimensional contracts that promise fast and convenient device connectivity, we can see how to truly reduce total cost of ownership. Diving deeper reveals the value of each provider’s entire portfolio of solutions, experience, and ecosystem alliances. Customers can start looking deeply into their organization and determine how a dynamic IoT partner will offer efficient, reliable, and meaningful deployments.

The post Let’s Stop Pretending IoT Is Easy appeared first on IoT Business News.

IoT Analytics, a leading provider of market insights and strategic business intelligence for the Internet of Things (IoT), has published its latest research on the global cellular IoT module and chipset market for Q2/2023.

The report reveals that 66% of IoT modules shipped in Q2 2023 had no dedicated hardware security and 29% had no security features at all, exposing them to potential risks and vulnerabilities.

The research analyzes the security features of 772 unique modules from 36 vendors and 150+ chipsets from 13 vendors that IoT Analytics tracks. It shows that only 30% of the modules available on the market, had dedicated hardware security features. Additionally, the article highlights the differences between the global and North American markets, where the latter has a higher share of non-dedicated hardware security features, such as TrustZone or secure boot.

KEY QUOTES:

Commenting on the importance of IoT security, Principal Analyst Satyajit Sinha noted:

“As cybercrime operates much like a business, criminals invariably opt for the path of least resistance. Implementing multiple layers of security increases the time and cost required for hackers to breach a system, thus making it more likely for them to abandon the effort and seek out less well-protected targets.”

Mr. Sinha added, “Cellular IoT modules are crucial for connectivity in IoT devices across industries. They provide a vital connection to the internet and are managed remotely. Ensuring their security is vital for safeguarding the broader IoT ecosystem.”

KEY INSIGHTS:

• The cellular IoT module market was stagnant in Q2’23 according to IoT Analytics latest data.
• Although IoT modules with dedicated security features are increasingly adopted, 66% of IoT modules shipped in Q2’23 had no dedicated hardware security and 29% had no security features at all.
• Recent demonstrations of vulnerabilities in non-dedicated hardware security features should drive the market further towards hardware-based security. Post-quantum cryptography is also an important consideration in IoT module security.

Updated cellular IoT module market

29% of cellular IoT modules shipped in Q2 2023 had no dedicated security features and only 34% had hardware-based security. Overall, the shipment and revenue of the $6.7 billion market (2022) remained generally flat in Q2’23 quarter-over-quarter, with 0% shipment and 0% revenue growth. Reasons for this stagnation include a weakened demand environment, which we discussed in our Q1’23 analysis of the cellular IoT module market.

IoT module security at the center of attention

With markets stagnating, we are putting a spotlight on cellular IoT module security by looking at the security features of 772 unique modules from 36 vendors and 150+ chipsets from 13 vendors that we track. IoT module security is of particular interest right now in light of the US Congress’ 7 August 2023 letter to the US Federal Communications Commission (FCC) regarding potential security risks of using Chinese cellular IoT modules.

Our analysis of the updated tracker and forecast shows the following breakdown of IoT module security features out of the aforementioned modules/chipsets available on the market in Q2’23:

• 30% had dedicated hardware security features, often embedded in chipsets or standalone components implemented through hardware security modules
• 42% had non-dedicated hardware security features, or features used to either create secure environments for processes to run or ensure only authorized firmware is loaded on the device
• 28% had no security features

However, the share of purchased/shipped modules with these security classifications in Q2’23 differs, with a significant difference between the global and North American markets as well:

While the global market shows a relatively balanced share of these three categories, the North American market skews heavily toward non-dedicated hardware security features. The low share of cellular IoT modules without security features in the North American market indicates that module security is a concern for its consumers, though there appears to be a reliance on non-dedicated hardware security features, such as TrustZone or secure boot.

This indication is consistent with recent concerns that the US Congress expressed to the FCC regarding the security of Chinese-made cellular IoT modules within US infrastructure (either directly or as part of the manufacturing supply chain), such as FirstNet Authority networks and devices used by first responders across the country (Quectel and Fibocom have published press releases responding to the US Congress’s concerns in early September 2023).

Why dedicated hardware security is the way forward amid supply chain concerns

Software and network security solutions have historically overshadowed dedicated hardware security features in IoT since they are more visible and easier to address, while dedicated hardware security features can be more complex and costly to implement. An alternative to software and network security solutions are non-dedicated hardware security features, such as ARM’s TrustZone, which creates a secure environment for processes to run, and secure boot, which ensures systems boot without intrusions.

Unfortunately, researchers recently demonstrated side-channel attacks against TrustZone during the Black Hat Asia 2023 conference. For their part, ARM has responded to this demonstration by stating that the attack is not unique to ARM’s Cortex-M architecture or TrustZone; rather, it’s a failure in application code—such attacks “may apply to any code with secret-dependent control flow or memory access patterns.” However, such attacks, no matter the core system they possess, demonstrate that adding dedicated hardware security solutions to these non-dedicated hardware security solutions can enhance the overall security of a module.

Shahram Mossayebi, Ph.D., founder and CEO of Crypto Quantique, explained the following to IoT Analytics when asked about cellular IoT module security:
“[W]e rely on security features such as TrustZone, but to achieve trust, we need to go beyond them. A root of trust is a set of cryptographic features (which soon must be quantum secure) for encryption, digital signature, and device identity. The hardware root of trust is the foundation for building trust with any IoT [device] and it is a crucial part of hardware security.”

With a hardware-based root of trust, manufacturers and consumers can ensure the authenticity of the modules—helping to address cloning and counterfeiting—and protection of the device’s keys. Once manufacturers can guarantee the authenticity and security of these keys, they can add additional security components like TrustZone and secure boot.

Where hardware security should be implemented

Implementing security measures at the device level during manufacturing is a foundational step, aiding in establishing device authenticity and partially curbing the infiltration of counterfeit components in the supply chain. However, this strategy only offers a partial solution since vulnerabilities still exist, particularly in the potential theft and cloning of device identities within supplier factories. Thus, an even more nuanced approach is required to bolster the defenses against such nefarious activities that seek to undermine the system from its very core.

To combat these risks more effectively, embedding hardware security at the MCU level within typical modules is highly recommended. This strategic positioning not only presents a formidable barrier against cloning and counterfeiting issues but also fosters the establishment of secure authentication protocols and the creation of unique device identities. Secure MCUs can provide a seamless integration of essential security features, such as robust authentication processes, potent encryption capabilities, and secure boot functionalities. These functionalities come together to create a fortified environment, essential for the optimal functioning of connected IoT applications, thereby ensuring a safer, more reliable network where devices can communicate and operate with an enhanced level of security and trust.

IoT module security outlook: Post-quantum security is becoming crucial for IoT

Currently, the general life span of most IoT devices is 8–12 years, with automotive 5G module applications lasting 10–15 years. With these long life spans, when building cellular IoT modules, it is essential that manufacturers look beyond current threats; specifically, they should start planning for the commercialization of quantum computing and the potential for state actors and cybercriminals to crack complex, commonly used encryption methods.

In October 2019, Google announced quantum supremacy in the journal Nature with its 54-qubit Sycamore processor, which Google claims was able to perform a complicated task in 200 seconds that would take the world’s most powerful supercomputer 10,000 years to perform. Many countries and companies are also advancing with quantum computing, such as the Chinese Academy of Sciences and QuantumCTek, a quantum information technology developer. Other Google competitors, such as IBM, Microsoft, Amazon, and Intel, along with several new startups, have all invested heavily in developing quantum computing hardware in recent years.

While quantum chips have not reached widespread commercialization yet, manufacturers can start considering quantum security solutions today. Governments are already looking at standards and quantum-proofing solutions for their agencies and companies, and the following are just some examples:

• In January 2022, the French National Agency for IT Systems Security (ANSSI) published its views and recommendations for PQC transition, offering a 3-phase process expected to last at least until 2030.
• In July 2022, the US Department of Commerce’s National Institute of Standards and Technology (NIST) announced its selection of four quantum-resistant cryptography algorithms, constituting “the beginning of the finale of the agency’s post-quantum cryptography (PQC) standardization project,” which NIST expects to complete and publish in 2024.
• In August 2023, the US National Security Agency (NSA), Cybersecurity and Infrastructure Security Agency (CISA), and NIST published a PQC migration readiness sheet to help the government and private sector start planning their quantum readiness.

Further, some companies are already developing post-quantum solutions. For example, Thales Group offers 5G security solutions with end-to-end encryption and authentication to safeguard organizational data as it moves across front-haul, mid-haul, and back-haul operations. These solutions rely on Thales’ 5G Luna Hardware Security Modules (HSMs). Further, in February 2023, Thales Group announced that it successfully piloted what it called a post-quantum resilient, end-to-end encrypted call using its Cryptosmart mobile app and its 5G SIM.

What it means for cellular IoT module manufacturers

5 key questions that cellular IoT module manufacturers should ask themselves based on the insights in this article:

1. Product strategy and security implementation: How can we realign our product strategy to prioritize the implementation of dedicated hardware security features without significantly escalating costs?
2. Response to political and legislative changes: How are we positioning ourselves to address the potential political and legislative changes affecting the market, particularly concerning the US Congress’s concerns regarding Chinese cellular IoT modules?
3. Security standards and compliance: Are we in line with the recent security standards and guidelines issued by agencies like ANSSI, NIST, and NSA, and are we preparing for the expected security transitions in the coming years?
4. Consumer education and advocacy: How can we educate consumers on the importance of dedicated hardware security features and advocate for a broader shift towards these in the market?
5. Post-quantum security solutions: Are we collaborating with communications companies and other stakeholders to develop and pilot post-quantum security solutions that can safeguard organizational data across various operations effectively?

What it means for users of cellular IoT modules

5 key questions that device/equipment makers and end users that adopt cellular IoT module should ask themselves based on the insights in this article:

1. Security implementation: Given the demonstrated vulnerabilities in non-dedicated hardware security features, what strategies should we adopt to integrate dedicated hardware security features without escalating costs significantly?
2. Compliance and legislation: In light of the concerns raised by the US Congress regarding the use of Chinese cellular IoT modules, how can we ensure compliance with evolving regulations and maintain the trust of our North American consumers?
3. Post-quantum security: Given the advancements in quantum computing, what steps should we take to incorporate post-quantum security solutions in our cellular IoT modules, keeping in mind the projected long life span of these devices?
4. Research and development: How can we foster innovation in our R&D department to develop unique hardware security features that offer robust protection against present and future threats?
5. Customer education: How can we educate our customers on the security features we use, developing trust into the security of the devices they use?

The post Cellular IoT module market Q2 2023: 66% of IoT modules shipped without dedicated hardware security appeared first on IoT Business News.

A new study conducted by IoT Analytics, a leading provider of market insights and strategic business intelligence for the Internet of Things (IoT), has uncovered the best practices and common pitfalls of IoT software go-to-market and commercialization strategies.

While IoT continues to create winners and losers, excelling at IoT software commercialization is key to being amongst the winners. The study identified seven crucial factors for successfully commercializing IoT software, including running own webinars, deploying social media tactics and helping customers with their most pressing challenges such as IoT integration. Not only this, it also highlights the importance of learning from both successful and unsuccessful IoT commercialization ventures.

The study aims to help IoT software vendors excel at IoT software commercialization and be among the winners in the competitive IoT market. It is part of the IoT Software Go-to-Market & Commercialization Report 2023, highlighting insights from 60 IoT software executives on their current go-to-market strategies, including their views on what works and what does not.

KEY QUOTES:

Knud Lasse Lueth, CEO at IoT Analytics, comments that “The complex nature of IoT makes marketing and selling IoT software challenging. Reflecting on past IoT marketing mishaps, such as Android Things, and learning from those that are successful in the market can help avoid repeating mistakes and instead focus on the things that work.”

Dimitris Paraskevopoulos, Senior Analyst at IoT Analytics, added that

“Webinars have emerged as the #1 key factor for a successful go-to-market strategy for IoT software vendors. By leveraging the power of webinars, vendors can simultaneously educate customers and their own teams, improve their professional social media presence, and support customers through the complex IoT integration process. When managed effectively, webinars are a valuable asset for IoT software vendors looking to succeed in the market.”

KEY INSIGHTS:

• 7 factors are considered important for a successful IoT software commercialization according to the IoT Software Go-to-Market & Commercialization Report 2023.
• Running own webinars and deploying social media tactics but also helping customers with their most pressing challenges like IoT integration play a key role.

Successful IoT software commercialization: 7 important considerations according to IoT executives

Learning from IoT software failures and successes

Not every IoT software venture marks a success story, as illustrated by Google’s Android Things. Google launched the IoT OS platform in 2018 with the vision to empower a nexus of connectivity through an array of smart devices. However, it could not foster community engagement and support. Ultimately, Google ended new device registrations and projects in January 2021 and shut down Android Things in January 2022.

While some of the criticism for Android Things relates to the product itself, such as Android Things devices being bigger and more expensive than typical IoT form factors or exhibiting integration challenges with various different types of hardware, it apparently faced IoT software commercialization issues as well. For starters, there seems to have been an awareness issue. As one Android developer in a public forum stated, “I have Android Studio installed and do a little Android development. I should have heard about Android Things. I had not.” Another IoT marketing-related issue that users brought forward was the lack of community support, especially for specific IoT use cases and common IoT devices in various verticals.

Experiences like this showcase why it is important to learn from successful and unsuccessful IoT commercialization ventures. IoT Analytics’ 160-page IoT Software Go-to-Market & Commercialization Report 2023 (published in August 2023) highlights insights from 60 IoT software executives on their current go-to-market strategies, including their views on what works and what does not.

Here are the 7 important considerations which, based on our research, improve a company’s chances of being successful in the IoT software market.

1. Creating own portfolio of webinars

The number 1 tool that IoT software executives said worked for their companies is running their own webinars (Note: We queried for 19 different marketing tools). 81% of surveyed vendors indicated that this tool brought success in terms of ROI, with 50% of the respondents marking “very successful.”

Webinars offer a great opportunity for a company’s technical experts to present information about the products and answer technical questions, making the webinars engaging and interactive. By managing their own webinars, companies get to own the script and format, providing them with the time they need to offer demonstrations or run a thorough Q&A.

Further, companies can build a portfolio of webinar recordings that customers can access later. With a portfolio, the marketing teams can decide on general categories that are of interest to their company’s target industries and provide content within those categories. Examples of such categories include introduction to IoT topics, IoT in business topics, IoT case studies, and hands-on workshops.

IoT software commercialization success example: The What is IoT? webinar that Israel-based Friendly Technologies ran in November 2017 has led to widespread awareness for the company. As of September 2023, the YouTube upload of the webinar—which discusses various IoT standards and IoT device management practices—has been viewed by more than 176,000 people.

2. Improving professional social media presence

76% of the IoT executives indicated that managing their own social media content for professionals (e.g., LinkedIn) brought success in terms of ROI (18% “very successful” and 58% “somewhat successful”). Though not as high in the “very successful” category, its overall performance suggests that IoT software vendors can find success with this tool. This can be especially true if a vendor pairs this channel with another marketing tool—e.g., marketing own webinars on LinkedIn.

Managing professional-oriented social media content can be as simple as having the main company account discuss new technology and software, but several vendors found higher engagement by leveraging employee and advocate posts, especially when using third-party communications management platforms. Employee advocacy is a cost-effective approach that can establish trust and credibility, as people in an employee’s network are more likely to trust what their employee connection says, and it can help with reach and visibility.

IoT software commercialization success example: Microsoft is known to run a global employee advocacy program, allowing employees to access content tailored either to their region or to a global scale and expand the reach of social media campaigns. The former CMO of Microsoft France, Sébastien Imbert, estimates that with 10,000 employees in the program, Microsoft could see 350 million–500 million impressions per year and achieve $5 million–$10 million in equivalent advertising value.

3. Educating own team and sales force

The (perhaps surprising) top success factor for driving customer adoption, according to IoT software executives, is companies educating their own teams (not their customers), especially their own sales forces, about their products (rated 4.3 out of 5, with 1 being not important at all and 5 being extremely important). The following are 3 best practices identified in the report to support team/sales force education:

1. Tailored training: Tailor training sessions based on each team’s involvement and needs. For instance, sales teams require training on how new software works and how customers can get engaged.
2. Access to resources: Ensure their teams have access to resources like eBooks, white papers, toolkits, and internal experts that can assist their learning journeys.
3. Hands-on, cross-functional workshops: Run interactive sessions with practical learning and real-life scenarios specific to the new IoT software.

IoT software commercialization success example: The product manager of one IoT software vendor noted that communication and education between their sales team and the product management team, including their leadership was crucial. Further, the product manager added that service-level agreements for product managers to respond to sales inquiries quickly, a library of documentation for repetitive inquiries, and a “single source of truth” to capture customer requests and feedback proved to be very valuable.

4. Providing excellent, dedicated support

Overall, IoT software executives marked providing dedicated support as the second most important factor for customer adoption (4.2/5 on average). It is interesting to note that while North American and European IoT software executives remained aligned on their top three customer adoption factors, APAC executives only aligned with them on providing dedicated support, marking it as their most important adoption factor (4.4/5 on average).

This point of regional alignment is not surprising. As customers adopt and implement IoT solutions, having direct access to excellent, knowledgeable, and specialized support becomes an important factor for them. Dedicated customer support ensures customers receive tailored, specialized support for their needs, especially when the support team is well-trained and educated on the products. Further, since customers are likely making a significant investment in their IoT solutions, having the vendor–customer relationship feel more like a partnership can help with overall customer satisfaction.

IoT software commercialization success example: An example of excellent customer support, discussed in the report, is from Red Hat, with 5 key elements identified:

1. Customer portal: An online portal offering access to product documentation, knowledge base articles, and video classrooms.
2. Technical account managers: A service for customers requiring a more tailored level of support, offering a dedicated technical advisor who understands their customers’ unique environment and needs.
3. Support policies: Policies detailing the lifecycle and levels of support so customers know how long the products will receive updates and support.
4. Global reach: With a global presence, Red Hat offers timely support regardless of where a customer is located.
5. Certified ecosystem: Red Hat works with a vast ecosystem of certified partners, ensuring that third-party applications and hardware work seamlessly with Red Hat products, and if an issue arises, Red Hat can often collaborate with these partners to find solutions.

5. Supporting customers with technical integration

The number 1 customer adoption roadblock, according to IoT software executives, is “complexity of technical integration.” The VP of manufacturing/operations in a North American company provided that their “customers report that they often must deal with multiple vendors with overlapping service, [which] usually results in significant slowdowns.”

The following are 4 things IoT software vendors can do to help their customers master technical integration:

1. Provide comprehensive documentation and support: Along with offering excellent, dedicated customer support, detailed user manuals can help customers through the integration process. Further, community forums enable customers to gain insights from others who have faced and overcome similar integration challenges.
2. Offer dedicated training and education: Provide on-site training and online courses specifically addressing integration into common tools and frameworks.
3. Build integration-specific customer feedback loops: Customer support teams can establish feedback loops with customers to learn about their integration challenges and work with development teams to improve integration capabilities based on the feedback.
4. Offer IoT integration services: Build internal teams or partner with reliable system integrators so that various “integration” services can be offered to customers.

IoT software commercialization success example: German IoT platform provider Software AG offers a number of IoT professional services to help companies adopt, learn, and manage their digital transformation solutions, and it also offers a readiness guide that explains the typical digital transformation journey, the pain points, and other considerations.

6. Helping customers overcome internal resistance to change

IoT software executives marked “resistance to change” as the second-ranked customer adoption roadblock. A core theme in the survey responses revolved around familiarity with old and new software. The VP of a company in the UK noted “there is a tendency to not want to adopt new solutions, especially by relatively older employees.”

The following are 5 things IoT software vendors can do to help their customers reduce internal resistance to change:

1. Provide data that supports the value proposition: Offer data-driven evidence on how the software can improve efficiency, save time, reduce costs, or provide any other tangible benefits.
2. Engage key stakeholders early: Identify and engage decision-makers and influencers within the customer’s organization from the onset. If they see value in the software, they can become champions who drive adoption.
3. Offer pilots: Allow customers to run pilot programs. This lets them test the software in a limited capacity, reducing risk and giving them a taste of the potential benefits.
4. Share case studies: Share success stories from other similar enterprises. When potential users see that their peers have benefited, they may be more inclined to adopt.
5. Provide communication templates: Provide the enterprise with communication templates or tools they can use to convey the importance and benefits of the software to their teams.

IoT software commercialization success example: The IT manager of an IoT software vendor in the Philippines shared the following successful strategy: “Usually, the customer’s concerns are around familiarity. They are so used to an old model that when we introduce new software, they are afraid/overwhelmed. We can alleviate that by having highly trained individuals who can explain the new systems thoroughly and point out similarities of the new software to the one that the customer is already familiar with so that they can better understand.” This goes back to vendors putting emphasis on training and educating their own teams as well as building up knowledge to understand other software and how it compares.

7. Considering regional adoption rates and strategic rollouts

When designing a go-to-market strategy, IoT software vendors should consider in which region they roll out their software first. 70% of the surveyed vendors indicated that North America had “very quick” or “quick” adoption of software, with Central Europe and East Asia following at 58% and 54%, respectively.

Regions with faster IoT software adoption typically have a higher demand for the software and a willingness to invest in IoT solutions. Focusing on these regions that have faster adoption rates can help vendors realize ROI faster. However, this does not mean that other regions should be ignored. Rather, when going to market, focusing on regions with faster adoption rates affords vendors more customer success stories that they can reference for potential customers in other, slower regions.

10 questions IoT software vendors should ask themselves now

This article highlights 7 of many insights that IoT software executives in the IoT Software Go-to-Market & Commercialization Report 2023. Based on the insights presented here, executives at IoT software vendors should discuss these questions to help avoid becoming the next Android Things:

1. How can we expand our portfolio of IoT webinars and ensure that they address the specific needs and interests of potential and existing customers more effectively?

2. Are we utilizing the potential of our own employees to enhance our presence on professional social media platforms like LinkedIn?

3. How can we improve our team education strategies to make sure every member is not only informed but also adept at communicating the unique selling points of our products?

4. Are our support teams sufficiently equipped with the resources and training to offer exceptional, dedicated support to our customers?

5. How can we innovate our integration tools to reduce complexity and facilitate a smoother transition for customers shifting from legacy systems?

6. What strategies are we implementing to lessen resistance to change among customers (e.g., helping customers involve key stakeholders)?

7. Are we considering the regional adoption rates adequately while strategizing our market rollouts?

8. How can we foster a community where customers can share their experiences and learn from each other, potentially reducing the resistance to change?

9. How effective are the mechanisms we have put in place to gather continuous feedback from our customers, ensuring that our products are constantly evolving to address their pain points (e.g., technical integration)?

10. How can we leverage customer success stories more effectively to build trust and accelerate adoption (e.g., in regions with slower uptake rates)?

The post New study reveals the hidden secrets of successful IoT software commercialization appeared first on IoT Business News.

This article is based on insights of the 82-page Digital Operation Signals – Industrial IoT Solution Spotlight (July 2023) report, published by Microsoft with research conducted by IoT Analytics. The report takes a deeper look at 300 recent IIoT initiatives, their challenges, their successes and particularly how they are implemented. IoT Analytics surveyed key stakeholders working on their respective employers’ IIoT initiatives between October and December 2022 and conducted in-depth interviews with a subset of them.

State of Industrial IoT in 2023

Industrial IoT (IIoT) in 2023 is on the way to becoming mainstream:

• Two-thirds of industrial organizations reported they are executing an IoT strategy.
• IoT projects have a 14% higher success rate than five years ago.
• The median time for IIoT projects to break even has decreased from 24 to 20 months over the same period.
• Common challenges such as budget availability, project complexity, and data management have diminished by approximately 50%.

While IIoT is becoming more mainstream, a core question persists: should companies try to buy a readily available IIoT solution, should they custom-build an IIoT solution, or is there a middle option?

The research shows that there is no universally best approach to IIoT implementation. However, there is often a right approach for individual projects.

Build, buy, or buy and integrate?

Finding the right approach for individual IIoT projects depends on several organizational factors, including:

• Available budget
• Desire to differentiate from competition
• Break-even / ROI goals
• Use cases at hand

The research found a growing trend toward the buy approach: 30% of projects that began in 2021 or 2022 used the buy approach compared to only 9% of projects completed in or before 2020. This growth has come at the expense of a reduced preference for the buy-and-integrate approach, which dropped from 50% to 28%.

“We clearly see a trend that people prefer proven standardized solutions”

IoT solutions providers have recognized this trend as well. David Shook, Chief Data Officer of US-based Uptake, which helps customers realize IoT solutions, explains, “We clearly see a trend that people prefer proven standardized solutions rather than building something from scratch.”

However, many organizations are still opting for the build and buy-and-integrate approaches. The report offers insight into the decision-making process for each approach.

1. The build approach to IIoT solutions

47% of respondents stated their IIoT initiatives used the build approach, with 40% stating that their custom-built solutions exceeded their expectations. Different industries have different solution footprints. For example, 89% of respondents from the buildings industry reported using the build approach, while 33% from the machinery industry reported the same.

“If you build it, you own it.”

Key reasons to build

In the words of a US cloud integration advisory firm VP, “If you build it, you own it.” This sentiment captures the core benefit of the build approach: organizations that build their IIoT solutions own the intellectual property rights, and they can incorporate any features they desire. The approach provides the freedom to customize the entire IIoT solution, from how it integrates with new and current equipment to the user interface/user experience. In addition to this, organizations don’t have to lock in with a vendor.

Key reasons not to build

The decision to build an IoT solution comes with costs in terms of money, effort, and time. First, organizations either need a robust IT department or a reliable partner to not only build and integrate the solution for them but also to maintain and update the solution.

Next, developing a solution can come with high or unexpected costs, with large funding often taking time to get approved. 20% of respondents experienced budget constraints, the largest challenge faced with this approach, and the strategy director for a Spanish electronics company explained, “Huge funds were required for this initiative, and it took a long time for us to collect such a large sum of money.”

Finally, building an IIoT solution typically leads to the longest project timeline out of the three approaches, from start to large-scale roll-out. The report shows that the stakeholders reported a median of 9 months just to develop the business case—versus 6 months for the buy approach. The other phases of development and launch, including median time to amortize investments, were also longer than the buy approach.

2. The buy approach to IIoT solutions

With IIoT becoming more commonplace and IoT developers from various industries sharing lessons learned, more off-the-shelf solutions are coming to market, though availability is still limited. As organizations standardize their operations with standard industrial hardware or IT architecture, tried-and-tested IIoT solutions can integrate with these tools for better operability and analysis. For new companies, starting with a ready-made solution allows them to build IT/OT architecture around their IIoT solution rather than trying to integrate after the fact—a ground-up approach.

“It is rarely necessary to go for a custom-made solution in the first stages”

Key reasons to buy

Newer or smaller organizations with limited budgets and resources can benefit from ready-made solutions. A director of logistics for a European machinery equipment company noted that “it is rarely necessary to go for a custom-made solution in the first stages of [a business] journey” since companies need to learn and understand their customers’ needs, adding that “starting with standard products accelerates projects and reduces overall cost.”

Reduced implementation time is another benefit of the buy approach. The research found that with this approach, the median time required to complete the first two phases of an IIoT project was 9 months, compared to 16 months for the build approach. Since the buy approach offers ready-made, reliable solutions, organizations and vendors only need to customize the solution to the organization’s needs and equipment.

The research also found that 43% of respondents that used the buy approach experienced reduced break-even points, with a median time of 12 months from the first project-related expense to reaching the commercial break-even point compared to 20 months for other approaches.

Finally, the buy approach allows an organization to remain slim. The external developers of ready-made solutions will often offer continued support and updates for the solutions, meaning the organization does not need to maintain an internal team just for this purpose.

Key reasons not to buy

While the buy approach limits necessary resources and shortens implementation time, only 13% of respondents stated that the approach exceeded their expectations. The report shows that 30% of projects using ready-made solutions were missing end-user capabilities, and 20% faced challenges with addressing cyber threats.

13% of projects experienced both internal cooperation and resistance to change issues as well. Because commercial IIoT solutions come with their own processes, workflows, and (sometimes) equipment, organizations may find it difficult to integrate the solution into their own processes and architectures. A senior engineer of a German chemicals company stated, “To support the maintenance team for IoT devices, the change management team had to do several hands-on trainings.”

3. The buy-and-integrate approach to IIoT solutions

38% of IIoT initiatives in the dataset use the buy-and-integrate approach. Buying and integrating an IIoT solution allows organizations to combine proven technology and product support with the freedom to customize the solution to their IT/OT and user experience needs. It also helps address some of the limitations of the build and buy solutions, such as implementation time and end-user capabilities.

The report shows that the electronics and machinery industries saw the most build-and-integrate solutions (57% and 52% of projects, respectively).

“Buying and integrating allows to combine proven technology with the freedom to customize”

Key reasons to buy and integrate

Opting for the buy-and-integrate approach can often come out of necessity, including some of the following factors:

• Lack of off-the-shelf solutions that meet an organization’s IT/OT architecture
• Limitations on capability in fully developing and/or maintaining a solution
• Budgetary constraints
• Cyber security needs not met by available solutions

The decision to use this approach can also include preferential reasons, including:

• The freedom to customize a majority of the solution
• Creating a defendable competitive advantage that a widely available off-the-shelf solution cannot provide
• Limiting reliance on vendors, balancing necessary support and updates with the ability to adapt to changes in-house.

One respondent, the chief experience officer of a US pharmaceuticals company, stated a combination of necessity and preferential factors: “We did not have adequate in-house knowledge in IT or operations to consider building from scratch. We did not want to be entirely dependent on third parties for maintenance. A tailored solution was essential to ensure operator confidence, so a hybrid model was best.”

Complex integration projects can also benefit from the buy-and-integrate approach, where a custom-built solution would be too costly and ready-made solutions are not designed to handle the complexity. An engineering manager for an FMCG manufacturer said, “Because the size of our plant is too large, it became really difficult for us to sync our machines with the various sensors and software tools in use.” Opting for the buy-and-integrate approach allowed the company to integrate the various commercial software tools and their various OT equipment.

Key reasons not to buy and integrate

While the buy-and-integrate approach finds a positive middle ground between the other two approaches, it also absorbs some of the disadvantages of those approaches. First, ready-made solutions are often built with specific applications in mind, so engineers and developers need to understand the solutions and work with vendors to develop additional integration services. This requires additional management of internal and external stakeholders.

Additionally, though the buy-and-integrate approach may be ideal for complex projects, the complexity of implementation and integration was the top challenge faced by adopters of this approach (19% of projects). Second to this was organizations lacking IT capabilities to customize and integrate the solutions (15% of projects), requiring further dependency on vendors to develop and maintain solutions.

Finally, the development of additional integration services becomes an additional project for the organization, meaning uncertain costs can come into play.

These reasons are some of the factors leading to this approach having the longest break-even time of the three options.

Which approach to choose

While there is no universal best solution, the report shows that organizations should carefully weigh which approach best fits their budget, their desire to differentiate themselves from competitors, their break-even goals, and the use cases.

The post IoT solution development: Build, buy, or a bit of both? appeared first on IoT Business News.

According to IoT Analytics, a leading global provider of market insights and strategic business intelligence for the Internet of Things (IoT), the industrial IoT gateways market accelerated significantly from 2021 to 2022, growing ~14.7% to reach $860 million (38% of the overall IoT gateways market).

KEY QUOTES:

Knud Lasse Lueth, CEO at IoT Analytics, comments that:
“Industrial IoT gateways are critical for connecting legacy systems with modern technology. They also play an important role to support the migration of manufacturing applications to the cloud. In the future I also expect a number of smaller applications to sit directly on IIoT gateways, leveraging containerization technology, more powerful storage and computation and in some cases even AI chips for ML inference.”

Kalpesh Baviskar, Analyst at IoT Analytics, added that:

“IoT gateways have emerged as a highly cost-effective solution for deploying IoT systems with multiple sensors. They play a crucial role in connecting legacy equipment and devices that were previously unconnected.”

“In recent years, we have seen the integration of high-performance processors and AI chipsets into IoT gateways, transforming them into edge IoT gateways. These edge gateways can perform local data processing and analytics, significantly reducing the amount of data that needs to be sent to the cloud. This can lead to significant cost savings and performance improvements for IoT applications.”

KEY INSIGHTS:

• According to the IoT Gateway Market Report 2023–2027, the $0.9B industrial IoT (IIoT) gateway market experienced accelerated growth between 2021 and 2022, which is set to continue on the back of several favorable tailwinds.
• IIoT gateways are enabling IT and OT convergence by securely and efficiently sharing data between floor-level OT equipment and IT equipment or the cloud—with implementation typically as part of one of four broader IoT architectures.
• There are several IIoT gateway advancements e.g., in security, edge computing, and storage.

The vital role of industrial IoT gateways in bridging IT and OT

IIoT gateways: Market and role

The industrial IoT gateways market accelerated significantly from 2021 to 2022, growing ~14.7% to reach $860 million (38% of the overall IoT gateways market), and we forecast continued growth at least through 2027. The factors driving this growth include the following:

• Connecting the unconnected. Many companies are retrofitting their legacy equipment with sensors and controllers, using IIoT gateways to perform necessary protocol and data transformation and transfer the data to an IT endpoint.
• Software applications are migrating. Companies with connected equipment are moving some key applications to the cloud, with IIoT gateways emerging as the main nexus point for information flow in and out of industrial premises. Some applications are also now run locally on the gateway itself.
• More powerful hardware. New, enhanced gateways with embedded multi-core processors, AI chipsets, and secure elements are enabling faster and more secure data processing and transmission (to an IT endpoint or cloud).

These factors reflect our assessment that IIoT gateways are becoming the juncture for IT and OT convergence.

Note: When we refer to IIoT gateways, we refer to (ruggedized) hardware that connects sensors, IIoT devices, and industrial equipment to cloud or on-premises servers or PLCs/IPCs operating on distinct industrial networks. For an exact definition, refer to our report.

How IIoT gateways connect the IT and OT worlds

Many companies maintain legacy equipment that does not have sensors or control devices. Even if the legacy equipment has sensors or controllers that connect locally, such as to a human-machine interface or panel PC on the factory floor, it may not offer connectivity options or use messaging protocols that end equipment (like an IT server or cloud) uses. Meanwhile, companies that possess IoT-enabled equipment may desire to move data off-premises (e.g., to remote IT equipment or the cloud) or enhance local data computation for automated responses before transmitting the data.

In cases like these, IIoT gateways can connect with standalone or integrated sensors—either wirelessly or wired through I/O module masters—to transmit data to IT or cloud servers. As described below, they fit within many architectures found in industrial IoT solutions.

IIoT gateways within IoT architectures

Whether a company builds or buys an IoT solution, the solution will align with an IoT architecture to collect and transmit data to the endpoint. While a direct sensor-to-cloud architecture does not require the use of an IIoT gateway, IIoT gateways are commonly found in 4 general types of IoT architectures.

1. Sensors/devices to PLC/IPC to IIoT gateway to cloud

In industrial environments with existing automation hardware, the sensors/devices to PLC/IPC to IIoT gateway to cloud architecture is very common. Field sensors or actuators are connected to I/O module masters. These I/O module masters transmit data to the on-premises PLC or IPC. The PLC/IPC is then connected to the IIoT gateway, which serves as a bridge between the PLC/IPC and the cloud.

This architecture can be very powerful but also potentially dangerous. The IIoT gateway can technically be configured to remotely access the entire architecture that sits below the PLC/IPC. While this setup enables any data to flow between IT and OT and thus any imaginable use case, it also has the biggest potential attack surface (potentially the entire facility), e.g., in case of a misconfigured security architecture.

2. Sensor to I/O modules to IoT gateways to cloud

In the sensor to I/O module master to IoT gateways to cloud architecture, simple sensors connect to I/O module masters. The I/O module master then uses wired or wireless connectivity standards to transfer data to IIoT gateways, bypassing any PLC or IPC. This architecture proves to be highly effective in scenarios where multiple sensors are arranged into clusters—the I/O module master acts as the central node for each cluster of sensors, efficiently gathering and transmitting data to the cloud via an IIoT gateway.

3. Sensors in devices to IoT gateway to cloud

In the sensors in devices to IoT gateway to cloud architecture, devices equipped with single or multiple onboard sensors are connected directly to the IIoT gateway. This architecture is often deployed where non-standalone IoT devices are used (i.e. devices that cannot connect to the internet by themselves).

4. Sensors to IoT gateway to cloud

In the sensors to IoT gateway to cloud architecture, IIoT gateways enable connections between sensors and cloud servers directly. This architecture can for example be found when retrofitting specific sensors on an asset (e.g., for condition monitoring) with the desire to bypass all other existing networks (to not interfere with them and create a new security risk).

Advancements in the capabilities of IIoT gateways

As IIoT gateways have become more common in IIoT solutions, they have become capable of offering more for their users. In general, IIoT gateways typically offer 8 key functions:

• Protocol translation
• Data management
• Device management
• Computation
• Communication
• Resource management
• Security management
• Managing quality of service

As the IIoT gateway market has grown, these functions have advanced. The following are just some examples of advancements.

“A growing number of customers [are] requiring proof of security level from manufacturers for their industrial IoT equipment.”

Security management

As the number of connected devices continues to increase, the risk of cyberattacks and unauthorized access becomes more significant. This is especially true for companies looking to connect factory equipment to external IT or cloud servers. Fortunately, to address these risks, IIoT gateway vendors are proactively incorporating security features into their products and adhering to industry-specific regulations and standards, allowing OT monitoring and control to reside securely behind layers of policies and access controls.

A notable series of standards is IEC 62443, approved in 2021, which directs all IEC 62443-certified products to adhere to specific product development requirements from the early stages of design. This set of standards has become mandatory technical requirements in many countries, and according to Pascal LeRay, Head of Cyber Security at Bureau Veritas, “a growing number of customers [are] requiring proof of security level from manufacturers for their industrial IoT equipment.”

In our research, IIoT gateway companies noted the importance of incorporating security standards in their products. Teltronic’s CEO, Juan Ferro, stated that “the sudden irruption of cybersecurity in the industry has been interpreted by Teltronic as an opportunity to improve both [our] products and associated processes,” adding that the pre-emptive adoption of security standards placed them ahead of other companies in their sector.

Along with standards, IIoT gateways are increasingly incorporating hardware security, using embedded secure elements either within processors or on the PCB/modules as trusted platform modules (e.g., TPM 2.0). Ultratronik’s A1 IoT gateway integrates NXP’s EdgeLock SE051 secure element, and Eurotech’s RELIAGATE 10-14 series maintain IEC 62443-4-1, -4-2, and PSA Level 1 certifications and have a TPM 2.0 security chipset—OPTIGA TPM SLM 9670 from Infineon Technologies.

“The milliseconds of latency [between] an industrial robot and many real-time systems can be the difference between a safety hazard and a productive assembly line.”

Computation

IoT gateways in general have trended toward more processing power. In industrial solutions, this has helped companies move data processing and computation toward the solution’s edge—nearer to the data collection point—saving them bandwidth and communications power and freeing their IT and cloud servers to manage other tasks. Additionally, there has been a trend of integrating AI chipsets into some IoT gateways to facilitate edge computing. A noteworthy example is AAEON’s AIOT-AVID IoT Video Analysis Gateway, which incorporates Intel’s Myriad X vision processing unit (VPU).

The ability to process and automate data in real time can mean much for a company’s bottom line, as one senior VP stated, “The milliseconds of latency [between] an industrial robot and many real-time systems can be the difference between a safety hazard and a productive assembly line.”

Data and resource management

Local data storage helps enable data processing at the edge. Further, some industrial use cases may call for data sorting and analysis before being transmitted to an IT or cloud server, either due to limited network connectivity or the desire for more efficient use of IT equipment.

The need for local data storage has led to eMMC flash memory and SSD solutions on IoT gateways in general. In mid-2022, Robustel launched three ARM-based IIoT gateways with varying DDR and eMMC sizes to meet application needs. A few months later, Compulab announced its IOT-GATE-RPI4, a Raspberry Pi-based IIoT gateway that offers up to 128 GB of eMMC memory and mPCIe slots for SSD storage expansion up to 256 GB. Other examples include MOXA’s AIG-301 series IIoT gateway with 16 GB of eMMC and Belden’s Hirschmann OpEdge-8D with 64 GB of SSD flash memory.

Device management

With integrated storage comes the ability to containerize applications for deployment on IoT gateways, including device management software. Traditionally, deploying applications on IoT gateways involved installing them directly on the equipment’s operating system, which had limitations in terms of scalability, flexibility, and ease of management. However, companies are increasingly using containerization as a deployment strategy for applications on IIoT gateways, offering platforms like Kubernetes and runtimes like Docker. These technologies provide a way to create lightweight and isolated runtime environments, known as containers, where applications can run consistently across various platforms and environments.

Many gateway OEMs are also building app stores with hundreds of ready-made applications that end users can deploy to their gateways (and in the cloud), such Bosch Rexroth’s ctrlX store, Siemens’s Industrial Edge Marketplace, and Advantech’s WISE-Marketplace.

Key Benefits of IIoT Gateways

In our analysis of 65 case studies, we identified numerous benefits of IIoT gateway implementations based on various use cases. The following are 3 of the main benefits companies cited.

1. Better IT/OT integration

Indeed, this is a key goal of implementing IIoT gateways, and our analysis shows that many companies have achieved this goal. A common goal of IT/OT integration is remote monitoring and response. As an example, Vitesco Technologies Italy used Zerynth’s 4ZeroBox, an on-premises IIoT system for real-time monitoring and predictive maintenance. This solution enabled Vitesco to predict pneumatic valve malfunctions 24 hours in advance, which reduced assembly downtime and increased productivity.

2. Reduced labor costs

IIoT gateways are often deployed for automation purposes and as such can reduce labor costs, human effort, and human errors. While Vitesco saw a 50% reduction in its manual labor requirement with its 4ZeroBox application, Colombian steel manufacturer Corpacero cut costs associated with repair labor after partnering with Senzary to deploy RotaryIQ and InsightsIQ solutions for predictive maintenance and remote machine management.

3. Energy savings

Enterprise energy management analytics software provider Wattics partnered with Kontron to use Kontron’s KBox A-101 as a central ‘edge node’ for Wattics Sentinel software at the customer’s site. It connects to the local energy grid and the Sentinel grid, facilitating meter configuration, reliable data collection, pre-processing, compression, and secure communication.

IoT gateway market outlook

With many companies seeking to either retrofit their equipment or enhance their IoT solutions, we have seen solid growth in the IIoT gateway market (8.1% CAGR) between 2018 and 2022, with acceleration specifically from 2021 to 2022. We assess that this trend will continue since use cases in manufacturing and certain applications continue to demand real-time processing, low latency, and secure data handling. Further, the following 5 trends, which are discussed in more depth in the IoT Gateway Market Report 2023–2027, support this assessment:

1. IoT gateways are becoming more modular, allowing IIoT gateway vendors to offer a range of options and configurations to meet customer needs and enable easy scalability.

2. IoT gateways are supporting more wireless connectivity options, such as secure cellular solutions with eSIM/iSIM technology, enabling IIoT gateways to handle multiple connected devices in an expanded perimeter of operations.

3. IIoT gateway vendors are collaborating to combine hardware and software solutions, simplifying deployments and reducing costs.

4. OT hardware is starting to consolidate (e.g., I/O module masters shifting to IIoT gateways)

5. Virtualization of workloads (e.g., virtual PLCs) allows IPCs and IIoT gateways to perform tasks that were previously tightly coupled to other pieces of hardware.

IIoT gateways play a pivotal role in bridging the gap between legacy machinery and modern systems, facilitating retrofits and brownfield installations. As industries strive for global connectivity and centralized management of OT devices, IIoT gateways will continue to play a major role in integrating operations across various locations.

What it means for IoT gateway vendors

5 key questions IoT gateway vendors should ask themselves based on the findings of the report:

1. Do our IIoT gateways remain compliant with updated security standards?

2. Do our customers require AI edge capabilities as a general offering?

3. Have we explored local data storage options to increase computation while decreasing latency?

4. Should we containerize our edge IIoT applications? And if so, how?

5. Are our general solutions enabling seamless IT/OT integration for customers? If not, should we focus on tailored solutions for our customers?

What it means for IoT adopters

5 key questions IoT adopters should ask themselves based on the findings of the report:

1. Have we assessed the various available IIoT gateways and their potential impact on our overall IoT strategy?

2. Which IoT architecture(s) are we using? Can an IIoT gateway offer improvements?

3. Do our current IIoT gateways meet current security standards? If not, what updates do we require to meet these standards?

4. Have we assessed the possible benefits of edge computing (e.g., automating controls locally based on the data)?

5. Should we leverage local data storage and containerized applications for better device management and updates?

The post The vital role of industrial IoT gateways in bridging IT and OT appeared first on IoT Business News.

According to IoT Analytics, a leading global provider of market insights and strategic business intelligence for the Internet of Things (IoT), the eSIM/iSIM market is set to surpass 500 million units in 2023 as it brings in the new age of cellular IoT.

More information can be found in IoT Analytics’ new Global IoT eSIM Modules and iSIM Chipsets Market Tracker, which provides quarterly data on worldwide IoT eSIM modules and iSIM chipsets from 2018 to Q1 2023.

KEY QUOTES:

Satyajit Sinha, Principal Analyst at IoT Analytics, comments:

“Looking ahead, it’s evident that eSIMs are positioned to become the primary SIM technology in the next 2-3 years, surpassing their counterparts. This shift is expected to be followed by the emergence of iSIMs, which are predicted to gain popularity due to their advanced security features, including a hardware root of trust. We’ll likely see a gradual transition from eSIMs to iSIM technology, as iSIMs are expected to become the preferred choice in the long term due to their inherent security advantages.”

KEY INSIGHTS:

• eSIM/iSIM is transforming the dynamics of the cellular IoT market, offering increased flexibility, reduced provisioning time, smaller form factor, enhanced security, and sustainability.
• The eSIM/iSIM IoT market is poised to surpass the 500-million-units mark in 2023.
• Two factors will likely drive the growth of eSIM and iSIM in the cellular industry: cybersecurity regulations and GSMA specifications.

Entering the new age of cellular IoT: eSIM/iSIM market to surpass 500 million units in 2023

Market overview

The cellular IoT market is undergoing a significant shift: physical subscriber identity module (SIM) cards as we know them are becoming virtualized in the form of embedded SIMs (eSIMs) or integrated SIMs (iSIMs).

In 2023, the IoT module eSIM/iSIM market is poised to surpass the 500-million-unit mark. Just in the first quarter, over 450 million IoT modules were already capable of supporting eSIM or iSIM—roughly 16% of the 2.8 billion active global cellular IoT connections. This data indicates the growing demand for flexible, fast, and efficient connectivity solutions in the new age of cellular IoT.

eSIM/iSIM is transforming the dynamics of the cellular IoT market

The rise of eSIM/iSIM technology is not just a mere technological advancement; it is a paradigm shift. It is about the seamless integration of IoT devices, the simplification of connectivity, and the enhancement of user experience. The use of eSIMs/iSIMs can help businesses decrease time to market and improve the efficiency of IoT deployments.
This technology offers several advantages over traditional SIM cards, including:

1. Increased flexibility for IoT solutions

eSIM/iSIM technologies offer increased flexibility and reduced provisioning time for IoT devices. By allowing devices to be provisioned with different carrier profiles remotely, they enable seamless switching between networks without the need for physical SIM card changes. This flexibility is particularly beneficial for businesses deploying IoT devices in multiple countries or regions. Additionally, the ability to remotely provision and update eSIM/iSIM saves time and simplifies the deployment process, especially in large-scale IoT deployments, empowering businesses to adapt quickly and optimize their IoT deployments.

Several car companies, including BMW, Audi, Tesla, and Volkswagen, have implemented eSIM technology to offer connected car services. Through their respective platforms, these companies can remotely activate, manage, and switch mobile network profiles. This allows for more flexibility and scalability in IoT deployments.

2. Compact eSIM/iSIM enables smaller IoT devices

eSIM and iSIM technology allows for more compact IoT device designs. This makes devices smaller and more lightweight than traditional SIM-card-based devices. Notably, eSIM/iSIM technology has facilitated the development of smart labels, which are paper-thin devices that offer precise, accurate, and secure tracking of small and lightweight items.

DB Schenker, the logistics arm of Deutsche Bahn, implemented an ultra-thin smart-label solution to track small freight consignments globally. Sensos, an Israeli group company of the Sony Semiconductor Solutions Corporation, developed the solution, which leverages Kigen’s iSIM technology embedded within Sony Semiconductor Solutions’ low-power wide-area (LPWA) chipset.

3. Enhanced security with embedded secure elements in eSIM/iSIM

eSIM/iSIM technology incorporates embedded secure elements, providing advanced security features compared to traditional SIM cards. The secure element acts as a hardware root of trust for asymmetric encryption, ensuring secure end-to-end communication. The GSM Association’s (GSMA) IoT SAFE specifications leverage a single eSIM/iSIM as a hardware root of trust.

Meanwhile, chipset vendor Sony Semiconductor Israel’s ALT1250 and ALT1350 chipsets integrate two secure elements for application security and UICC identity (Sony Semiconductor Israel was formerly known as Altair Semiconductor and is now a group company of Sony Semiconductor Solutions Corporation).

The eSIM in Tesla vehicles enables features such as remote vehicle monitoring, software updates, and over-the-air (OTA) updates for the vehicle’s firmware. The eSIM acts as a hardware root of trust by securely storing cryptographic keys and certificates used for authentication and encryption.

4. Sustainable eSIM/iSIM technology reduces waste and emissions

eSIM/iSIM technology eliminates the need for physical SIM cards, reducing electronic and plastic waste. Additionally, the elimination of physical SIM card shipments to IoT modules or devices helps reduce emissions associated with transportation. These environmental benefits make eSIM/iSIM a sustainable choice for IoT connectivity.

Journey to 500 million: eSIM market penetration increased, iSIM is emerging, and GSMA streamlines deployment

Per IoT Analytics’ Global IoT eSIM Modules & iSIM Chipsets Tracker, eSIM penetration within cellular IoT modules experienced a significant increase, with quarterly shipments of eSIM-capable modules rising from 7% in Q1 2018 to 31% in Q1 2023. Notably, there was a period of stagnation in this growth during 2021; however, from Q1 2022 onward, we observed a consistent adoption rate through Q1 2023.

Meanwhile, iSIM is still an emerging technology in IoT. Sony Semiconductor Israel’s ALT1250 is the only iSIM-based cellular IoT chipset that reached mass shipments.

So far, the implementation of eSIM/iSIM IoT standards has been slower than initially anticipated. This is primarily due to the complexities of remote SIM provisioning and different standards for consumer IoT and machine-to-machine (M2M) technologies.

To address this issue, GSMA developed new eSIM IoT specifications, namely SGP.31 and SGP.32, that are designed to complement the existing M2M (GSMA SGP.02) and consumer IoT (GSMA SGP.22) eSIM standards. The new specifications allow for remote control and configuration of eSIMs via a dedicated management module, adapting from the current eSIM Consumer and IoT Specification.

This approach eliminates the need for user interaction when provisioning, simplifying IoT connectivity and reducing the time to market for IoT deployments. Moreover, this enhancement may eliminate the need for carrier integration, giving enterprises the same flexibility and control as consumers, thus marking a significant advancement for IoT implementations.

The release of GSMA SGP.31 and SGP.32 marked significant changes in eSIM IoT deployment in three ways:

• Introducing the eSIM IoT Remote Manager (eIM)
• Transforming Local Profile Assistance (LPA) into IoT Profile Assistant (IPA)
• Replacing Subscription Manager Secure Routing (SM-SR) with Subscription Manager Discovery Server (SM-DS) and IPA in the architecture

These changes streamline both remote profile management and provisioning processes and eliminate the need for SM-SR in eSIM IoT deployments.

The future of eSIM/iSIM in cellular IoT

Looking ahead, we expect eSIM/iSIM technology to further increase its penetration in the cellular IoT market.

Currently, the market is still dominated by a combination of physical SIM cards, uSIM, and soft SIMs (software-based UICCs in a trusted execution environment), with a combined (shipment-based) market share of approximately 67%. (Note that Soft SIMs are not considered to be secure, as they lack an anchor with a hardware root of trust.)

As the industry progresses, we assess that eSIMs will become the dominant SIM technology in the next 2–3 years. Afterward, with the next cycle of module hardware, iSIMs are likely to start grabbing market share since they offer enhanced security features and are anchored with a hardware root of trust, making them a more secure option. In the long run, it is expected that even the eSIM market will migrate toward iSIM technology, with iSIM then dominating the market.

Two factors will likely drive the growth of eSIM and iSIM in the cellular industry:

1. Cybersecurity regulation

The importance of robust security measures in cellular IoT cannot be overstated. Cybersecurity regulations are playing a pivotal role in strengthening IoT security, particularly through the implementation of hardware-based solutions.

Given the recent activity around EU and US technology regulation, it is likely that the coming years will see new laws mandating stronger IoT security, potentially requiring the use of a hardware root of trust.

In this context, eSIM/iSIM technology emerges as a practical solution for implementing chip-to-cloud security. By leveraging the secure element as a hardware root of trust, it is possible to enable asymmetric encryption, thereby bolstering the overall security framework. It is noteworthy that cellular IoT vendors are already guided by GSMA’s IoT SAFE specifications, which provide valuable insights and guidelines in this area.

2. GSMA specifications

GSMA’s SGP.31 and SGP.32 will play a crucial role in the future of eSIM/iSIM in cellular IoT. By streamlining the remote profile management and provisioning processes, these specifications will simplify the deployment of IoT devices and reduce the time to market. Furthermore, the introduction of eSIM and the transformation of LPA into IPA will provide businesses with greater control over their IoT deployments, enabling them to adapt quickly to changing market conditions and customer needs.

The post eSIM/iSIM market to surpass 500 million units in 2023 appeared first on IoT Business News.

Introduction

The energy sector is transforming as companies embrace green and sustainable solutions to address environmental challenges, reduce pollution, and combat climate change. The Internet of Things (IoT) enables optimized power distribution, efficient utilization of renewable energy, and effective energy consumption monitoring. This article will explore the impact of IoT solutions on the sustainable energy market and discuss key technology trends shaping the industry.

Sustainable Energy Market Size and Growth

The global green energy market is experiencing rapid growth, driven by the shift from non-renewable energy sources to renewable alternatives. According to research by Nester, the market is likely to generate approximately $250 billion in revenue by 2035, with a compound annual growth rate (CAGR) of around 9% from 2023 to 2035. In 2022 alone, this market generated approximately $100 billion in revenue. The International Energy Agency (IEA) reports a 45% increase in annual renewable capacity additions in 2020 – the fastest pace in almost twenty years – and nearly 280 Gigawatts (GW). The International Renewable Energy Agency (IRENA) also indicates that despite the disruption of the COVID-19 pandemic, 2020 saw a 6% increase in total installed renewable power capacity globally.

Growth Drivers and Challenges

Numerous factors are fueling the expansion of the green energy market. These include a heightened public awareness and preference for green energy sources to meet their electricity needs, proactive government steps toward sustainability, the imperative to curb greenhouse gas (GHG) emissions, the growing adoption of electric vehicles, and rapid urbanization. However, significant challenges include the high initial investment required for developing new green energy sources, volatile climate conditions, and skilled worker shortages.

Green Solutions for the Energy Market

The sustainable energy market encompasses various solutions and technologies prioritizing environmental responsibility and sustainability. Let’s delve into several key technologies propelling the ongoing transformation.

Renewable Energy Sources

Sources of renewable energy like wind, solar, hydroelectricity, geothermal, and biomass play a crucial role in the transition to green energy. Solar power has seen significant growth, with falling costs and advancements in solar panel technologies. The wind power sector is growing with the development of ever-larger and more efficient wind turbines.

Sustainable Fuels

Sustainable fuels offer an alternative to fossil fuels. Sustainable fuels come from organic and typically carbon-neutral materials such as plants and algae, reducing GHG emissions. Hydrogen-based fuels and technology, including hydrogen fuel cells, are gaining considerable attention as a promising clean energy alternative.

Energy Storage

Battery technologies like lithium-ion batteries are already widely used for short-term energy storage, but there is growing emphasis on developing extended durability battery solutions. Additionally, long-duration storage solutions, such as pumped hydro storage and compressed air storage, are being explored to address renewable energy sources’ daily and seasonal variability.

Predictive Maintenance and Asset Management

IoT technology revolutionizes the energy sector by empowering predictive maintenance and asset management capabilities. IoT systems effectively monitor asset performance and health by harnessing real-time data from embedded sensors within energy infrastructure, including wind turbines and solar farms. This monitoring allows for proactive maintenance, detecting potential issues before they become critical, and optimizing asset performance for maximum efficiency and longevity.

Case Study: Drone-Based Oil Rig Surveys

As part of the bridge from today’s energy system to tomorrow’s energy system, conventional energy firms are working to make their operations more efficient and sustainable. Softeq worked with Sky-Futures, a company specializing in drone-based industrial inspection services, to develop a workflow management system for oil rig inspections. The goal was to create a solution that would process images and videos captured by drones during inspections, making equipment monitoring more cost-effective for oil and gas companies.

Softeq developed a secure customer portal with a proprietary Machine Learning-powered back end. This system is a versatile platform for automating order management, document handling, and digital asset organization. Key features of the solution include user administration, image and video processing using smart algorithms, electronic document management, collaboration functionality, and dynamic PDF inspection reports.

This workflow automation system saves time and reduces costs associated with oil and gas inspections. The International Business Awards program recognized this solution as Energy Industry Innovation of the Year.

Benefits and Impact of IoT in the Sustainable Energy Sector

The integration of IoT solutions in the sustainable energy sector offers numerous benefits and has a significant impact on the industry. Read on for a few notable advantages:

Enhanced Grid Stability

IoT-enabled smart grids bolster grid stability and reliability. With real-time data on power demand, supply, and grid conditions, operators can quickly optimize power distribution, detect and respond to faults, and ensure a more resilient and secure grid infrastructure.

Renewable Energy Grid Integration

IoT solutions are critical in seamlessly integrating renewable energy sources into the power grid. By constantly monitoring energy production from renewable sources and adjusting power flow in response to changing conditions, IoT-enabled systems ensure a smooth integration of intermittent renewable energy, maximizing its utilization and minimizing curtailment.

Empowering Consumers

IoT solutions empower consumers by providing real-time data on their energy consumption and enabling them to make informed choices. Smart meters and energy management systems allow individuals to track their energy usage, identify areas of inefficiency, and adjust their behavior or implement energy-saving measures accordingly. This IoT-driven insight promotes energy conservation and empowers consumers to participate more directly in transitioning to a sustainable energy future.

Environmental Benefits

The widespread adoption of IoT solutions in the sustainable energy sector contributes to significant environmental benefits. IoT solutions help reduce air pollution, GHG emissions, and our continuing reliance on fossil fuels by promoting renewable energy sources, optimizing energy distribution, and reducing energy waste.

Conclusion

IoT solutions enhance energy efficiency, optimize power distribution, and integrate renewable energy sources with the grid by connecting devices, gathering real-time data, and enabling intelligent decision-making. As the sustainable energy market grows, businesses must embrace IoT technologies and leverage their potential. As the world continues prioritizing sustainability and adopting green energy solutions, the Internet of Things will undoubtedly remain a key enabler in shaping the entire energy sector’s future.

The post How Green IoT Solutions Drive Sustainability in the Energy Sector appeared first on IoT Business News.

In the current era of radical and accelerated technological advancements, the Internet of Things (IoT) is an undeniably transformative force, notably within the area of consumer electronics. With a value of nearly $1.2 trillion by 2028, according to Statista, this global industry is the pulsing heart of an ongoing technological revolution. IoT plays a pivotal role in this dynamic industry and the changes it creates in human lives and daily experiences.

Examining the Consumer Electronics Landscape

Today’s consumer electronics landscape is vast and diverse, encompassing sectors such as TV, Radio & Multimedia, Telephony, Computing, Gaming Equipment, and Drones. Each is experiencing a dynamic transformation to meet continuously growing and shifting consumer tastes and demands. Thanks to the global surge in smartphone sales, the Telephony segment claims the lion’s share of the market. Interestingly, the smallest segment, Drones, reveals the significant disparity in size across the market’s many segments.

Impacting the Consumer Electronics Market through IoT

At the forefront of the industry’s growth is IoT. This branch of technology has crept into every facet of consumer electronics, becoming an integral part of our daily lives. According to Market Research Future, the consumer electronics segment within the IoT market will balloon to an impressive $172 billion by 2030. This remarkable growth will likely result from increased disposable incomes, a growing demand for luxury and convenience to enhance lifestyles, and the widespread adoption of voice assistants and other smart home technologies.

Revolution Robotics: A Case Study in Democratizing Robotics Education

Among the many applications of IoT in consumer electronics, a case in point is the work of Revolution Robotics, a U.S. tech startup. This forward-thinking enterprise sought to democratize robotics education by creating an affordable yet fully functional programmable robot kit. To actualize their vision, they collaborated with Softeq to create an electronic kit that empowered children to assemble, program, and remotely control robots using a smartphone.

The kit included an open-source hardware design, more than 530 plastic parts, a library of 3D printable parts, and firmware. However, the journey to create this pioneering solution was not without challenges.

Smartphone-Controlled Robots: The Power of IoT and Custom Firmware

The crux of Revolution Robotics’ solution was the smartphone-controlled operation of the robots. This smartphone-driven interface was possible because of innovative firmware solutions that enable smartphones to program the robot’s logic and command its actions. The robot’s construction involves linking the robot’s central processing unit to a smartphone using BLE technology and affixing an array of sensors, motors, and assorted plastic components to the robot’s framework. Users can then program the robot’s logic, executing a preprogrammed algorithm or following commands from the smartphone.

The firmware, developed by Softeq, served as the conduit between the smartphone and the robot. This custom firmware facilitated the integration of intricate sensors and motors, and its design fostered scalability, enabling the on-demand addition and programming of extra features.

Maintaining Affordability While Maximizing Functionality

The goal of affordability was a cornerstone of this project. Maintaining cost-effectiveness was not just a goal; it was a central requirement. Through careful planning and iterative development, Softeq optimized the costs of the various plastic parts and electronics without compromising the robot’s functionality.

This collaboration resulted in a groundbreaking product: an affordable, programmable robot kit with the same functionality as its pricier counterparts. It was a shining testament to the potential of IoT, demonstrating how this technology could transform the educational sector by making learning experiences more engaging and interactive without limiting access through prohibitive pricing.

Harnessing Data and AI in IoT

When it comes to IoT, the significant volume of data generated by connected devices is both a challenge and an opportunity. When harnessed correctly using AI and Machine Learning, this data can yield valuable insights, leading to more informed decision-making and increased innovation. Companies are already leveraging this to fine-tune their product performance, anticipate user needs, and troubleshoot issues even before they occur. Therefore, IoT isn’t just about connectivity; it’s about making intelligent use of the data connectivity generates. For businesses, understanding the relationship between IoT, AI, and data analytics is key to realizing their full potential.

Enhancing Customer Experience through IoT

IoT transforms the consumer electronics landscape by offering a degree of personalization and interactivity that was previously inconceivable. Connected devices now adapt to user behaviors, offering custom experiences that enhance user satisfaction. For instance, smart TVs curate content based on viewing habits, and IoT-enabled thermostats learn from daily routines to optimize home temperature settings. This focus on user-centric design, powered by IoT, significantly enhances customer experience and is an area decision-makers should not overlook while planning IoT integrations.

Market Dynamics and the Future of IoT

Though IoT continues to revolutionize the consumer electronics industry, challenges persist. IoT security concerns, the lack of standardization, and increasing data privacy risks present potential impediments to growth. However, with market opportunities such as government support for IoT research and development, improvements in 5G connectivity, and the inevitable digitalization of numerous consumer applications, the future of IoT in consumer electronics appears promising.

The transformative power of IoT is undeniable. Its potential to create interconnected ecosystems of devices, enabling enhanced convenience and usability for consumers, is driving the industry forward. IoT redefines the consumer electronics landscape with technological advancements, shifting consumer preferences, and increased connectivity. As the industry continues leveraging IoT for growth and innovation, the future promises opportunities we can scarcely imagine.

The post Harnessing the Power of IoT in Consumer Electronics appeared first on IoT Business News.

IoT Analytics has conducted an extensive keyword analysis based on a comprehensive dataset of approximately 75,000 earnings calls from leading US-listed firms.

The findings from the second quarter of 2023 reveal crucial discussions led by CEOs, focusing on three pivotal themes: Generative AI applications, bank challenges, and economic uncertainty. These influential topics have captivated boardrooms worldwide, ultimately shaping the future investment priorities for companies across various industries.

KEY QUOTES:

Philipp Wegner, Principal Analyst at IoT Analytics, comments:

“CEOs are now discussing how their companies can use generative AI applications. The discussion shifted to actual deployment of Large Language Models.”

KEY INSIGHTS:

• According to the latest “What CEOs talked about” report, three themes noticeably gained traction in earnings calls in Q2/2023: 1. AI & Generative AI, 2. Bank troubles, and 3. Reshoring.
• Discussions around economic uncertainty, sustainability, and supply chain disruptions lost traction.

The big picture

In Q2 2023, economic uncertainty remained the most discussed theme in boardrooms globally.

There was a notable decline in the number of CEOs discussing inflation, with only 50% mentioning the keyword (a 21% decrease from the previous quarter). Similarly, other related topics also experienced a decrease in prominence, with interest rates being discussed by only 33% (-13%) of CEOs and the term “recession” being mentioned in just 18% (-15%) of all earnings calls in Q2/2023. Despite these slight variations in the focus on economic topics among CEOs, it is important to note that overall, economic uncertainty remains a prevailing concern in boardrooms.

Key CEO quote on the macro environment:

“We expect macro headwinds will continue with the potential for a recessionary environment across both the U.S. and Europe.” – Ian Broaden – Executive Vice President and Chief Financial Officer, McDonald’s, May 2, 2023

Key upcoming themes

(Generative) AI

Generative AI discussions, specifically around use cases and applications, continue to increase.
The mention of Generative AI experienced a significant increase of +129% in the last quarter, with 6% of discussions specifically referencing it. Additionally, the broader topic of AI was discussed in 21% of earnings calls (+21%), while the more technical term, “large language model” (LLM), saw a 229% increase in mentions, and was present in 1% of all earnings calls.

Banks

CEOs discussed banks more frequently in Q2 2023 (+36%). Following the banking turmoil involving several institutions including Silicon Valley Bank (SVB) and Credit Suisse in Q1 2023 companies discussed about a potential fallout as well as stricter lending regulations from some (regional) banks.

Key CEO quote on banks:

“We have seen a number of banks pulling back from auto lending, which is kind of a hallmark of banks through difficult markets, and that’s created a bit of a pricing opportunity for us, as well as improvement in share – financing share for us.” – Marion Harris – CEO, Ford Motor Company Credit Company, 02 May 2023

Reshoring

Discussions around reshoring increased by +30% in Q2 2023. 1.3% of all companies and 5% of industrial companies talked about the topic. Given the ongoing tensions between China and the USA, many US-based companies appear to prioritize enhancing the resilience of their supply chains, and some have concluded that bringing production closer to home is the solution.

Key CEO quote on reshoring:

“Reshoring continues to be a prevalent topic among our customers, and we expect near and longer-term benefits from this trend.” – Frank Dellaquila – CFO, Emerson Electric Co., 03 May 2023

Declining themes

Sustainability and climate change

Despite record temperatures around the world (e.g., temperatures in the North Atlantic Ocean increased to records highs), discussions on climate (-16%), emissions (-25%) and sustainability (-17%) experienced a decline in Q2/2023.

Supply chain disruptions

With supply chains slowly improving and supply shortages easing, discussions regarding supply chains in general (-19%), and supply chain disruptions (-54%) in particular, decreased strongly in Q2/2023.

Deep dives on select themes

#1 (Generative) AI

The release of ChatGPT by OpenAI in November 2022 ignited an unprecedented discussion about the use cases of generative AI in boardrooms. Generative AI was mentioned by 6% of all CEOs in Q2/2023 – a remarkable increase of +129%, compared to the previous quarter. Discussions have transitioned from specifically discussing the tool ChatGPT itself (mentioned by 3.7% in Q2, an increase of 28%) to actual enterprise-wide applications of generative AI. Moreover, an increasing number of CEOs also delved into technical details: The keyword LLM (large language models, the foundation for ChatGPT) was discussed by 1%, representing a substantial increase of +229% compared to the last quarter.

In Q2/2023, numerous companies started to roll out generative AI as part of their core product. For instance, the online flower shop 1-800-flowers.com launched MomVerse, an AI-powered poetry tool to assist individuals in expressing their love for mothers on Mother’s Day. E-commerce giant eBay has also started to use generative AI to support its marketplace sellers in composing suitable product descriptions. Lastly, travel company Booking Holdings has rolled out ChatGPT as a virtual travel assistant. These are just three examples of companies who are infusing generative AI into their business.

Unsurprisingly, the companies that talked most about generative AI in Q2/2023 were from the Technology sector (22.2% of all tech earnings calls) and Communication Services (22.1%). Consumer Cyclical (3.7%), Consumer Defensive (3.4%) and Industrials (2.8%) also had their fair share in debates.

When it comes to the various generative AI and LLM tools mentioned during these calls, OpenAI clearly dominates the field. OpenAI‘s ChatGPT accounted for over ~99% of the mentions among generative AI tools. Other tools such as Google’s Bard, Meta’s LLaMA or Aleph Alpha were only sporadically mentioned as examples.

Key CEO quotes on generative AI:

“With the emergence of generative AI capabilities, we moved quickly to create a fun and playful way to intertwine the emerging AI technology with our gift-giving experience. Just in time for Mother’s Day, we launched the 1-800-FLOWERS MomVerse.” – Chris McCann – Chief Executive Officer, 1-800-flowers.com, 11 May 2023

“Generative AI has a number of exciting use cases outside of descriptions, and we’re exploring numerous potential applications across our marketplace that can enable truly magical customer experiences.” – Jamie Iannone – CEO, eBay Inc, 26 April 2023

“There are current challenges given that current LLMs sometimes produce inaccurate outputs. Nevertheless, we are excited to be exploring how we can make use of these technologies for the benefit of our customers. Some of our brands, like KAYAK and OpenTable, are experimenting regenerative AI plug-ins, while others are building ways to integrate the technology into their own offerings.” – Glenn Fogel – President and Chief Executive Officer, Booking Holdings, 4 May 2023

“We are in advanced stages to apply generative AI across our portfolio, and we are working as an early release partner of OpenAI and together with other vendors. We are planning to announce new disruptive AI use cases.” – Christian Klein – CEO, SAP SE, 21 April 2023

#2 Supply chains

Long lead times and strained supply chains have been a persistent concern for CEOs. We previously highlighted supply chain disruptions as a prevalent topic of discussion, such as in our Q4 2022 analysis, or Q3 2021 analysis.

In Q2 2023, supply chain disruptions were mentioned in 4% of all earnings calls. That is a decrease of -54% compared to Q1 2023. The tone of these discussions has shifted, as many companies now discuss how they have successfully overcome these disruptions and managed the challenges. Although the Global Supply Chain Pressure Index has receded back to pre-pandemic levels, not all earnings calls mirror that sentiment, with some companies still grappling with longer-than-desired lead times.

Key CEO quote on supply chains:

“We have seen now that the supply chain disruption has normalized, and we will see that the inventory level will decrease starting actually in Q3 of this year.” – Yves Mueller – Hugo Boss AG, 4 May 2023

“Last 2 or 3 years have seen extreme swings on inventory up and down, given the supply chain disruptions which we all faced in the industry. I think we now see more normalized trade inventory levels. And from what we see across the board, most trade inventory levels in the Q1 were pretty much normalized.” – Marc Bitzer – Chairman and Chief Executive Officer, Whirlpool Corporation, April 25, 2023

“Our inventories will remain high throughout the year also to preserve our agility in a context where the fluidity of the supply chain is not yet fully restored.” – Antonio Picca Piccon – Chief Financial Officer, Ferrari N.V., 4 May 2023

#3 Climate change

In 2023, global temperatures have risen by 1.1 °C from pre-industrial levels, and news regarding climate-related catastrophes continues to make headlines regularly (e.g., major floodings in Pakistan, record heat in the North Atlantic or severe droughts in the western Mediterranean).

Our analysis reveals a significant increase in discussions related to climate and sustainability during earnings calls from Q1 2019 to Q1 2021. However, since then, the prevalence of these topics has leveled off or even experienced a slight decline. In Q2 2023, 20% of CEOs discussed sustainability (-17% compared to the previous quarter), and 11% focused on the climate (-16%) during their earnings calls.

Economic uncertainty and the emphasis on AI use cases seem to have sidelined the sustainability and climate topic. Amazon, for example, quietly gave up parts of its climate pledge recently. Additionally, several companies, including Yamaha (as seen below), have acknowledged falling short of their previously set targets.

On a positive note, numerous vendors offering sustainability-related products are experiencing significant adoption. Microsoft and Siemens, for example, highlight the considerable customer demand for sustainability-related products.

Key CEO quote on climate and sustainability:

“Our Cloud for Sustainability is seeing strong adoption from companies in every industry, including BBC, Nissan and TCL as they deliver on their respective environmental commitments.” – Satya Nadella – Chairman & CEO, Microsoft Corporation, Apr. 25, 2023

“We are in the sweet spot with automation and digitalization and in particular, with the sustainability offerings we have for our customers to help them transitioning in their business models.” – Ralf Thomas – CFO, Siemens AG, May 17, 2023

“As for the sustainability efforts, in fact, we have made greater progress in some of the areas, but the sustainable timber usage ratio was affected by the change in the model mix. So, we have not achieved much numerical results to mention yet.” – Takuya Nakata, CEO, Yamaha Corporation, 9 May 2023

The post What CEOs talked about in Q2/2023 appeared first on IoT Business News.

IoT Analytics published the Q1/2023 update of their “Global Cellular IoT Module and Chipset Market Tracker & Forecast” – an interactive dashboard and structured market tracker that includes quarterly data on worldwide cellular IoT modules and chipsets from 2018 to Q1 2023, and forecast data from Q2 2023 to 2027.

KEY QUOTES:

Satyajit Sinha, Principal Analyst at IoT Analytics, comments:

“The cellular IoT module market experienced a decline in Q1 2023, due to cautious end-user spending, device makers reducing inventory, and the ongoing semiconductor chip shortage. The slow recovery of the Chinese market and global uncertainties have significantly impacted the market. However, this Q1 market weakness is seen as a bump in the road, with more than 6 billion cellular IoT connections expected by 2027, driven by various sectors and the emergence of 5G technologies.”

KEY INSIGHTS:

• In Q1 2023, global cellular IoT module revenue declined 6% YoY; shipments declined 16%.
• The key reasons for the decline are: 1. Cautious end-user spending; 2. Inventory reductions; and 3. Chip supply issues.
• The top five cellular IoT module companies—Quectel, Fibocom, Telit Cinterion, Sierra Wireless, and LG Innotek—currently account for 66% of the global market.
• The Q1 market weakness is seen as a bump in the road, with six billion cellular IoT connections expected by 2027, driven by various sectors and the emergence of 5G technologies.

Global cellular IoT module market declined 6% in Q1 2023 in a weakening demand environment

The Cellular IoT Module Market: Overview

This month, IoT Analytics updated the in-depth Global Cellular IoT Module and Chipset Market Tracker & Forecast, which provides a quarterly look at the revenues and shipments of the companies providing IoT modules and chipsets for cellular IoT deployments.

According to the latest data, the market declined 6% in Q1 2023 on a revenue basis and 16% on a shipment basis (year-on-year). It is important to note that the cellular IoT module market only accounts for roughly 3% of the global IoT enterprise market, so the impact this segment has on the total IoT market is limited. Yet, it is an indication that the general IoT market weakened in Q1 2023. Earlier this year, we forecasted IoT enterprise spending to grow 19% in 2023.

The decline in the cellular IoT module market in Q1 can be attributed to three main factors:

1. There has been cautious end-user spending on the back of economic uncertainties.

Spending for IoT connectivity modules has slowed on the back of budget constraints in a globally uncertain economic environment characterized by high inflation, trade tensions, political instability, and post-pandemic ripple effects in China. Our data show that the retail industry and the Eastern Europe regions are the biggest reasons for the Q1 decline.

“The primary reasons for this decline were the slow recovery of the Chinese market and uncertainties in the global market.” – – Quectel, Q1 2023 financial results

2. Device makers are reducing their inventories.

In response to the economic uncertainties and fluctuating market conditions, device makers that build IoT modules into their products have focused on reducing costs and managing inventory more efficiently. As these companies deploy a lower-inventory strategy to avoid overstocking and minimize costly excess inventory, orders are getting delayed and order quantities get reduced. This has decreased demand for cellular IoT modules.

“Some of our customers pushed out orders to later this year due to their inventory stock levels.” – A cellular IoT module company, Q1 2023

3. A few companies are still facing a shortage of semiconductor chips.

Although lead times for most standard semiconductors have improved steadily since the peak in early 2022, these lead times remain elevated and particularly high for specific chips, particularly in the automotive industry, e.g., vehicle-to-everything (V2X) connectivity modules. LG Innotek, Adient, Cars.com, and Group 1 Automotive are some of the companies that highlighted the chip shortage in their investor reporting in Q1 2023.

“We are experiencing weaker demand and ongoing tightness in the semiconductor chip supply.” – LG Innotek, Q1 2023 financial results

Competitive landscape of the top five cellular IoT module companies

The top five cellular IoT module companies (based on revenue)—Quectel, Fibocom (including Rolling Wireless), Telit Cinterion, Sierra Wireless, and LG Innotek—account for 66% of the global cellular IoT module market in revenue. Recently, #2 Fibocom and #3 Telit Cinterion boosted their positions in the global ranking due to M&A activities in the space.

Here are some of the Q1 highlights from the top 5 vendors:

1. Quectel: As the market leader in shipment and revenue, Quectel experienced an 11% YoY decline in cellular IoT module revenue, a higher YoY decline than in Q4 2022. According to the company, the primary reasons for this decline were the slow recovery of the Chinese market and uncertainties in the global market.

2. Fibocom: Fibocom completed its full acquisition of Rolling Wireless at the beginning of 2023. This acquisition was the key driver for its cellular IoT module revenue growth, which increased by more than 40% YoY in Q1 2023. The acquisition also expanded Fibocom’s portfolio scope in the automotive, FWA, and other mobility industry verticals.

3. Telit Cinterion: Established in early 2023 as a result of the merger between Telit and Thales’s IoT module unit, Telit Cinterion holds third position in cellular IoT module shipment and revenue. Since it is a newly formed entity, there are no benchmarks for comparing its growth or decline.

4. Sierra Wireless: Semtech completed its full acquisition of Sierra Wireless at the beginning of 2023. Sierra Wireless secured fourth position, but its cellular IoT revenue declined by 25% YoY due to lower market demand.

5. LG Innotek: As a pure automotive communication module provider, LG Innotek ranks fifth with a 5% share of global cellular IoT revenue. The company’s cellular IoT module revenue declined by 6% quarter-on-quarter due to weaker demand and ongoing tightness in the semiconductor chip supply.

Outlook for the broader cellular IoT market

According to IoT Analytics’ Global Cellular IoT Connectivity Tracker & Forecast (Q2/2023 Update), the cellular IoT market grew 27% in 2022. The market is expected to continue to grow significantly, reaching more than 6 billion connections by 2027. Various sectors, such as intelligent metering, transport, supply chain management, logistics, and automotive telematics, drive this growth.

Technology innovation also plays a key role for market growth.

The following three key technology trends are visible:

1. 5G coming up : 5G and 5G Recap technologies are expanding their footprint in the market. 5G shipments, for example, are expected to account for 12% of global cellular IoT modules by 2027. Fixed Wireless Access (FWA) and Automotive CV2X are major use cases for 5G technology.

2. New LPWA cellular modules threatening incumbents : New LPWA cellular modules from MCU-based companies such as ST Microelectronics and Renesas compete with traditional cellular module players such as Quectel or Fibocom. MCU players promise better scalability, reduced bill of materials (BoM), and accelerated time-to-market while maintaining full control over the supply chain.

3. The rise of 3GPP-based satellite connectivity : The implementation of 3GPP-based satellite connectivity is becoming increasingly popular, with major chipset manufacturers such as Mediatek, Qualcomm, and Sony Semiconductor showcasing their latest developments in this area. Sony Semiconductor already launched ALT1350, the first cellular IoT LPWA chipset to offer satellite connectivity, which opens up new possibilities for IoT devices to communicate beyond traditional network boundaries. This integration of satellite connectivity into LPWA chipsets is expected to drive further innovation and growth in the IoT market.

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