• Vision AppKit utilizes the Alif Semiconductor Ensemble® MCU family of industry-leading Edge ML MCUs and Telit Cinterion’s power-efficient LTE-M and low-power Wi-Fi and Bluetooth® wireless technology modules
• Ultra-compact camera design can perform on-device AI use cases like face and object detection, image classification, and more at a significantly lower power consumption than previously possible for these use cases

Telit Cinterion, an end-to-end IoT solutions enabler, and Alif Semiconductor, a supplier of the most secure, power-efficient Edge AI-enabled MCUs and fusion processors in the market, today announced the Vision AppKit — the world’s smartest and most efficient connected camera reference design.

The Vision AppKit combines Telit Cinterion’s Wi-Fi and Bluetooth wireless technology or LTE-M communication modules in an ultra-compact camera design together with Alif Semiconductor’s Ensemble E3 series MCU, capable of performing on-device AI use-cases like face and object detection, image classification, and more at significantly lower power consumption than previously possible.

The Vision AppKit is a reference design for ultra-low power, small form factor AI-enabled camera that can capture images and/or video, perform AI-based processing in real-time on captured data, and deliver the results wirelessly to a display or other external system. Alif’s E3 Series MCU — known for its EdgeAI capabilities in battery-operated IoT devices — powers this groundbreaking design. Telit Cinterion supports communication in the Vision AppKit with the ME310 LTE Cat-M and WE310 Wi-Fi and Bluetooth Low Energy 5.0 modules.

The Alif Ensemble E3 series features a distinct High-Efficiency MCU core and a separate High-Performance MCU core, along with microNPUs that can be promptly enabled when a device needs additional AI/ML compute performance, to keep the overall system power footprint as low as possible. Alif Semiconductor’s aiPM technology dynamically powers only the logic that is in use at any given time thus achieving the lowest overall system power consumption. This combination delivers a performance uplift of at least two orders of magnitude compared to traditional 32-bit MCUs at a power consumption that is two orders of magnitude lower, drastically reducing inference times for AI camera vision-based tasks like object detection, face recognition, and image classification.

Reza Kazerounian, Co-Founder and President at Alif Semiconductor, said:

“The Vision AppKit AI camera reference design incorporates highly efficient processing capability to deliver small form factor camera solutions for object detection and image classification in a secure way.”

“This technology enables battery-operated cameras to perform computations previously only possible in the cloud.”

The ME310 empowers enterprises to implement novel, compact designs across a diverse range of applications, such as asset tracking, healthcare monitoring, smart metering, portable devices, industrial sensors, home automation, and more. This module facilitates enhanced power efficiency in IoT applications through Power Saving Mode (PSM) and extended Discontinuous Reception (eDRX), catering to devices requiring optimization in cost, size, and power consumption. Additionally, the WE310 is a fully integrated module combining single-band Wi-Fi (802.11 b/g/n) and Bluetooth Low Energy (BLE5). It offers a cost-effective and high-speed wireless connection to an embedded microcontroller via UART, SPI, and SDIO interfaces.

“Integrating Alif’s cutting-edge edge AI technology with Telit Cinterion’s industry-leading secure modules opens the path to a new category of intelligent, compact, ultra-low power, and cost-efficient IoT devices,” said Manish Watwani, Chief Marketing and Product Officer at Telit Cinterion. “This integrated design reduces adoption barriers and guarantees optimal AI and communication performance.”

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Innovative Wi-Fi HaLow Router with High-Speed Wi-Fi 6 for Long-Range IoT Connectivity in Diverse Environments.

CES 2024 – Morse Micro, a leading Wi-Fi HaLow silicon vendor, in partnership with Edgecore Networks, a prominent provider of traditional and open network solutions for enterprises, data centers, and telecommunication service providers, today announced a strategic partnership.

Together, they unveiled the new Wi-Fi HaLow router – the Edgecore EAP112.

This innovative router, ideal for outdoor environments and a variety of other settings, is designed to meet the rapidly growing demands of IoT applications. Equipped with advanced Wi-Fi HaLow capabilities and supporting the Matter protocol, the Edgecore EAP112 is uniquely positioned to connect thousands of IoT devices through a single device, marking a significant advancement in IoT connectivity solutions.

Edgecore EAP112 integrates Morse Micro’s MM6108 Wi-Fi HaLow module, marking a significant advancement for both commercial and consumer access point/router segments. The EAP112 is poised to revolutionize wireless connectivity by providing enhanced coverage and longer range for Wi-Fi-enabled IoT products using Wi-Fi HaLow technology.

“Our collaboration with Edgecore represents a pivotal moment in Morse Micro’s journey to redefine wireless connectivity for the IoT with Wi-Fi HaLow technology,” said Michael De Nil, co-founder and CEO at Morse.

“The integration of our Wi-Fi HaLow module into Edgecore’s EAP112 access point/router platform exemplifies our commitment to expanding long-range connectivity options for the growing IoT ecosystem.”

Wi-Fi HaLow technology is the first Wi-Fi standard tailored to meet the unique needs of the Internet of Things (IoT), making it a superior alternative to traditional Wi-Fi alternatives in a wide range of IoT applications. Operating in the sub-GHz frequency band, the Wi-Fi HaLow 802.11ah standard was designed from the ground up for the IoT, offering an optimal combination of extended range, power efficiency, long battery life for wireless devices, superior penetration of barriers, large network capacity, advanced security, and Wi-Fi compatibility.

The EAP112 offers robust extensibility and usability for diverse IoT applications, featuring Wi-Fi 6, Wi-Fi HaLow, 4G LTE, BLE, Zigbee, and Thread. Designed for resilience in various environments and applications, especially in challenging terrains or climates, it operates within a temperature range of -30°C to 50°C and is IP65-rated for superior water and dust resistance. The EAP112 also provides flexible mounting options, including wall, ceiling, and pole mount, to accommodate specific site needs.

The Edgecore EAP112 is designed to leverage the complementary strengths of Wi-Fi HaLow and Wi-Fi 6 technologies, providing unparalleled range, coverage, speed and reliability. This Wi-Fi combination is especially critical in today’s IoT-driven world, where the ever-increasing demand for consistent and robust wireless communication is driving innovations in access point and router platforms.

Key Features of the Edgecore EAP112 Access Point/Router:

• Enhanced Coverage: The EAP112, equipped with Morse Micro’s MM6108 module, significantly boosts IoT connectivity, ensuring efficient and effective coverage. This enhancement enables intelligent applications across a variety of settings, including expansive residential areas, commercial spaces, and industrial environments.
• Advanced Connectivity: The platform’s Wi-Fi 6 technology ensures higher data rates and increased capacity, making it ideal for handling multiple IoT devices and high-bandwidth applications.
• Optimized Power Efficiency: The integration of Wi-Fi HaLow ensures that the EAP112 is not only powerful but also power-efficient, making it suitable for deployments in remote locations.
• Future-Proof Technology: The EAP112 is designed to meet the evolving demands of both commercial and consumer applications, paving the way for widespread adoption of Wi-Fi HaLow technology in the IoT ecosystem.

“Our partnership with Morse Micro aligns seamlessly with our goal to provide innovative products in the Wi-Fi HaLow IoT router market. The launch of the EAP112 represents a pivotal moment in harnessing the extensive potential of IoT connectivity, bringing significant advantages to our clientele in both commercial and consumer segments,” declared TengTai Hsu, VP of Edgecore Networks. “The EAP112 embodies our commitment to innovation and excellence, reflecting our dedication to advancing IoT applications with technologies like Wi-Fi HaLow and Matter. We have initiated Proof of Concept (POC) collaborations with selected regional customers. Our long-established partner, BeMap, is gearing up to kick off the sales and marketing of the EAP112 in Japan. Concurrently, Edgecore Wi-Fi is preparing to open pre-orders for the EAP112 to the global market later this year.”

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• Telit Cinterion’s new end-to-end, turnkey IoT solution will feature 3GPP Release-17 NB-IoT-over-NTN compliant modules with hybrid cellular and satellite connectivity services
• New partnership will provide transportation, logistics, agriculture, mining and other verticals with seamless, ubiquitous connectivity based on floLIVE’s global cellular infrastructure, which acts as the backbone to worldwide connectivity and is complemented by Skylo’s satellite service — even in remote areas

Telit Cinterion, an end-to-end IoT solutions enabler, today announced a solution featuring cellular and satellite connectivity services on its ME910G1 and ME310G1 modules.

In partnership with floLIVE and Skylo Technologies, the new solution ensures that mobile assets — shipping containers, agricultural equipment, trucks, and other high-value assets — are continually trackable and monitored anywhere on earth, from cities and rural highways to deserts and oceans.

The new Telit Cinterion solution is ideal for enterprises in transportation, logistics, construction, mining, agriculture and other verticals whose mission- and business-critical applications require guaranteed global connectivity with their mobile assets. The solution will provide OEMs, system integrators, and enterprises with a turnkey, end-to-end suite featuring:

• Telit Cinterion cellular LPWA module that is fully compliant with the new 3GPP R17 NB-IoT over non-terrestrial network (NTN) standard. This will enable satellite data communication using the NB-IoT protocol — without any special hardware changes required for the integration of the cellular module in the customer application.
• Telit Cinterion cellular and satellite data service plans, as well as a connectivity management portal. This drastically simplifies access to global connectivity with unique, centralized support and an advanced monitoring platform for full control of device fleets.
• Satellite connectivity services from Skylo — a direct-to-device Non-Terrestrial Network (NTN) operator focused on connecting anything, anywhere — in conjunction with floLIVE — the creator and owner of the world’s first and largest hyperlocal global network and a leading IoT connectivity enabler.

The Telit Cinterion ME910G1 module is currently under certification and approval by Skylo, making it one of the first modules that will be commercially available for customers globally. The solution will debut in Europe and North America following the launch of the floLIVE-Skylo service in 2024.

“The ability to leverage local cellular connectivity across the globe in tandem with satellite connectivity truly opens the globe to IoT and digital transformation,” said Chen Porat, floLIVE SVP of Sales, APAC and Africa. “floLIVE offers MNOs and IoT MVNOs with a global hyperlocal infrastructure that becomes the backbone to anywhere, ‘always-on’ connectivity. This expansion into the NTN domain signifies our dedication to supporting our partners through continuous growth in carrier relations, global presence, and service offerings.”

“We are thrilled to partner with Telit Cinterion, a recognized leader in the IoT industry. This collaboration will enable us to leverage Telit Cinterion’s expertise in cellular connectivity to expand the reach and capabilities of standards-based NB-IoT-over-NTN with the Skylo solutions,” said Sriram Subramanian, Director of Product Management at Skylo. “Together, we will empower businesses across industries to unlock the true potential of connected devices and drive digital transformation on a global scale.”

“Transportation companies, logistics providers and mine operators are just three examples of enterprises that need to monitor their high-value mobile assets anywhere and everywhere on earth,” said Manish Watwani, Chief Marketing and Product Officer, Telit Cinterion.

“Our strategic partnership with Skylo and floLIVE meets that need by providing the world’s first turnkey package of hardware and seamless, ubiquitous, global connectivity that leverages the best of cellular and satellite — all based on the new 3GPP R17 NB-IoT over NTN standard.”

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Amid waning demand, cellular IoT module market faces another challenging quarter in Q3 2023, according to Counterpoint Research.

• Despite a decline in shipments, Quectel continued to lead, followed by Fibocom and China Mobile.
• The top five applications – smart meter, automotive, POS, router/CPE and telematics – accounted for over 60% of total cellular IoT module shipments.
• Against the backdrop of muted demand, full-year shipments for 2023 are expected to decline by 5%.

Global cellular IoT module shipments saw a 2% YoY decline in Q3 2023, according to Counterpoint’s latest Global Cellular IoT Module and Chipset Tracker by Application report.

Weaker demand, rising interest rates and cautious spending from enterprise IoT players are some of the important factors for slowdown in this market.

For the first time, the 5G market’s share in the global cellular IoT module market crossed 5%, indicating traction for 5G adoption. However, 5G applications are currently limited due to the lack of killer use cases and higher prices. We are only witnessing early adoption in the router/CPE, PC and automotive markets.

The IoT module market is undergoing a technology transition from 4G Cat 1 and NB-IoT to 4G Cat 1 bis. The low-cost and power-efficient 4G Cat 1 bis is becoming popular for many applications, including POS, smart meter, telematics and asset tracking.

Commenting on the market dynamics, Associate Director Mohit Agrawal said, “The top five applications, encompassing smart meter, automotive, POS, router/CPE and telematics, accounted for over 60% of total cellular IoT module shipments this quarter. Notably, only the smart meter and router/CPE segments observed positive growth in shipments, with other applications experiencing a decline.

India stands out as the only region to register positive growth in the global cellular IoT module market. Conversely, the market outside of China and India saw a steeper decline compared to China. Contrary to industry expectations, the market is not gaining momentum.”

• Quectel, the market leader, and Telit Cinterion, one of the leading international vendors, experienced a decline in shipments. This trend mirrors the prevailing conditions in both the Chinese and global IoT module markets. However, Quectel showed a slight improvement in performance in the global market on a sequential basis.
• In contrast, the other two vendors in the top five – China Mobile and Fibocom – saw positive growth. This growth was driven by smart meter, asset tracker and POS for China Mobile and router/CPE applications for Fibocom. China Mobile recently established a subsidiary, BILIN’ZHILIAN, to accelerate its module business growth.
• Certain Chinese brands such as Unionman, OpenLuat and Lierda exhibited positive performance from a niche perspective. Their growth was fuelled by applications in smart meters, asset tracking and POS.

Commenting on the future outlook, Senior Research Analyst Soumen Mandal said, “Global cellular IoT module shipments are projected to experience a 5% YoY decline in 2023. However, demand revival is expected by the second half of 2024, with substantial growth predicted for 2025, coinciding with the mass adoption of 5G and 5G RedCap.”

“In the long term, the cellular IoT module market holds promise and applications such as smart meter, router/CPE, POS, automotive and asset tracking will be driving most of the growth for this market.”

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• Hyundai Motor and Kia will link Samsung’s ‘SmartThings’ IoT (Internet of Things) platform to their connected car services
• The three companies aim to break the boundaries between living spaces and mobility spaces, enhancing the time value of driving before, during and after
• Customers can also experience seamless connectivity with OTA and USB-based updates

Hyundai Motor Company and Kia Corporation announced that they have signed an agreement on January 3 with Samsung Electronics for a Car-to-Home and Home-to-Car service partnership, aiming to enhance the connectivity between residential and mobility spaces.

Under this agreement, Hyundai and Kia customers will be able to remotely control digital appliances via touch and voice commands through their cars’ in-car infotainment systems. Conversely, they will have remote vehicle control via AI speakers, TVs and smartphone apps to control various vehicle functions.

This is made possible through the organic integration of Hyundai and Kia’s connected car services and Samsung’s Internet of Things (IoT) platform, ‘SmartThings’. Customers are expected to use it in various ways in their daily lives, enjoying uninterrupted connectivity experiences.

For example, on a hot summer evening commute, the user can activate the ‘Home Mode’ to turn on the registered air conditioner and air purifier, start the robot vacuum, and turn on the lights for a comfortable and pleasant living space. Alternatively, before going out, the user can initiate the ‘Away Mode’ to turn off unnecessary lights and start the robot vacuum to create a clean living space to return to, as well as pre-activate the vehicle’s air conditioning to a comfortable temperature.

In the case of Hyundai and Kia’s Car-to-Home service, users can register and use specific modes for each situation to minimize device operation while driving. In particular, location-based automatic operation, in addition to touchscreen and voice commands, will be implemented to provide more convenient service to customers.

In addition, electric vehicle owners can use SmartThings’ integrated home energy management service to check the energy usage of their home and vehicle and adjust the optimal charging times.

Hyundai and Kia plan to expand their Car-to-Home and Home-to-Car services to overseas customers by linking the connected car platforms and Samsung SmartThings.

To achieve this, Hyundai and Kia will develop stable service-providing infotainment systems, and subsequently provide these features through over-the-air (OTA) and USB-based updates even for existing vehicles.

“This is an opportunity to make the connected car’s Car-to-Home and Home-to-Car services more convenient in various fields,” said Haeyoung Kwon, Vice President of Hyundai and Kia’s Infotainment Development Center. “We plan to accelerate our technology development to continuously make global Hyundai and Kia customers’ journeys meaningful.”

“This collaboration will enable communication from Home-to-Car and integrated home energy management services that are optimized for future lifestyles,” said Chanwoo Park, Executive Vice President at Samsung Electronics.

“By connecting the SmartThings platform with vehicles, we’ll be able to significantly enhance the customer experience in both the home and the car.”

Meanwhile, Hyundai and Kia have already been providing Car-to-Home and Home-to-Car services through collaborations with telecommunication companies and construction companies. The Car-to-Home service — previously available for six items, including lighting, plugs, gas shut-off valves, ventilation, air conditioning and boilers — and the Home-to-Car service for vehicle air conditioning, remote start and charging management will be expanded to support connections with a wider range of devices through this partnership.

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Two investment themes bookended 2023. In January, the European Union backed a $100m venture capital fund, managed by Momenta Partners. In December, Softbank announced its EUR473m ($514m) investment for a 51% stake in Cubic Telecom. This development more than drew the eye as exemplified by the analyst commentary around the high (16x) revenue to implied enterprise value multiple.

In between, the level of corporate activity in the IoT sector continued at roughly the same pace in prior years, albeit down on the years of heightened activity going back five or so years ago. There were several developments among the vendor and network operator communities, but less so among the IoT platform providers. Governments became more active with an emphasis on security and protections for the consumer sector.

Against the backdrop of 5G developments and 6G pathfinding, IoT is becoming a part of the fabric of enterprise operations and national infrastructure. Established players continue to emphasize connectivity, a relatively small portion of IoT value chains, while enterprises focus on quick-to-market solutions enabled by cloud providers and systems integrators. Both approaches risk leaving ‘system of systems’ issues for later consideration.

Investment Splash

As announced, the European Union’s VC funding effort targets Industry 5.0 or, “Industry 4.0 with-a-conscience” prospects because the Industry 4.0 movement is perceived to be overly tech-focused, and one that has failed to prioritise people and the planet. Consumer protection and sustainability are themes that reappear in other developments covered by this review.

SoftBank’s stake in Cubic Telecom drew attention for its high multiples and am implied valuation of over EUR900m for Cubic Telecom. This is a business that raised $124m over a period of some ten years. After beginning life as a company offering an over-the-air software management for M2M applications, it switched to explore the connected car space (working with Tele2) around 2014. An EUR18m investment from Audi and Qualcomm followed in 2015. SoftBank’s investment rationale into Cubic Telecom is to pioneer the future of software-defined connected vehicles. This does not look like an IoT connectivity deal given SoftBank’s 2022 equity investment into 1NCE, the latter being characterised as “the only company that can deliver global IoT”.

One insight on SoftBank’s investment can be gleaned from an even larger IoT investment from several years ago. In 2016, Cisco invested US1.4bn to acquire Jasper Wireless. Compared to Cubic Telecom, Jasper had raised a cumulative investment of $205 million over seven rounds. At the time, Cisco’s acquisition provided it with an entry point into the IoT sector as well as a channel comprising some 3,500 customers including big names such as Ford, GM, Heineken, and Boston Scientific. The acquisition seems to have helped Cisco over subsequent months as it brokered IoT deals with SalesForce.com, IBM and several mobile network operators internationally. Whether SoftBank can achieve the same market gains with Cubic Telecom remains to be seen.

With over 90% of Cubic Telecom’s revenues concentrated in Volkswagen Group, there remains a challenge to diversify the customer base. Of course, SoftBank’s relationships might help with Japanese vehicle manufacturers. This will take time and a greater investment in resources and coalition building. There should also be scope for product and service innovation involving connected car, intelligent transport, and electric vehicle charging systems. It’s worth noting that several months after its Jasper Wireless acquisition, Cisco’s continued foray into the IoT sector led to an additional $3.7bn acquisition of AppDynamics which was active in application performance monitoring, end-user monitoring and infrastructure visibility. Expanding the addressable market might be one factor in SoftBank’s investment calculus.

Incumbents’ Dynamics

Across network operators, connectivity platforms and vendors, the sharpest rise in corporate initiatives points to the ways in which vendors are trying to ease adoption and reduce the friction of developing solutions. For example, ST Microelectronics wants to make it easier to connect devices to cloud providers. It now offers microcontroller software and developer tools targeting Microsoft’s Azure IoT Hub and AWS cloud. ST Microelectronics also partnered with CommScope to integrate the latter’s PKIWorks IoT security platforms to align with align with the Connectivity Standards Alliance’s Matter standard. Making adoption easy applies to another strategic incumbent, Qualcomm. It launched a new platform called Qualcomm Aware comprising Qualcomm silicon and an ecosystem of hardware and software partners all wrapped in a cloud-friendly bundle to simplify the process of “getting into the IoT game”.

Mobile and low-power network operators continued at about the same level of corporate activity as 2022 with two themes apparent. One involved the launch of solutions for distinct verticals. In the utilities sector, for example, Vodafone launched its Water Metering solution for water management companies. Also targeting the water sector, UnaBiz (formerly SigFox) entered into a strategic partnership with KAIFA, a utility sector business digitalisation solution provider. In Australia, Telstra launched an end-to-end industrial automation capability, following its acquisition of industrial IoT providers Aqura Technologies and Alliance Automation. AT&T, one of the forerunners of the IoT industry even decided to relaunch its old “Connected Solutions” business unit. Beginning with connected cars, it wants to help customers navigate the 5G and IoT, by putting dedicated technology and sales executives alongside each other instead of separating them across different AT&T units.

The other theme involved horizontal, or extended connectivity, initiatives. Some of these combined licensed and unlicensed terrestrial network providers (e.g., Bouygues with Netmore Group, UnaBiz with The Things Industries to interwork SigFox and LoRa technologies). Others involved the combination of terrestrial and satellite communications means (e.g., Sateliot with Transatel, Skylo with Telefonica, EchoStar with the Things Industries and, Intelsat with Deutsche Telekom).

Platform providers were less in evidence as far as corporate initiatives are concerned. A marketing report by Analysys Mason for floLive, one of several to publish on eSIM and iSIM developments, suggested industry motivations are driven by a strategy of embedding connectivity earlier in the IoT value chain. For 2023, the requirements associated with this industry change, focused on flexible connectivity, outweighed M&A and platform innovation developments.

Government’s Growing Role

As the sector grows, IoT offerings are starting to expose externalities that purely market-based systems are not geared up to address. That is one explanation for the EU getting involved in VC funding for people and planet issues as noted earlier. In Scotland, the government sees the nation as expanding in a global market valued at $600bn. The country is investing in an innovation hub targeting IoT and related technologies such as sensing and imaging to help Scottish businesses explore opportunities “presented through advanced digital technologies”.

Cybersecurity and consumer protection are other areas where governments can address adverse externalities and set a positive path forward through regulatory and certification measures. For example, the UK government is enacting regulations for Security Requirements for Relevant Connectable Products targeting password management, vulnerability disclosures and software update support. In the USA, the Biden-Harris Administration announced a cybersecurity certification and labelling program via a “U.S. Cyber Trust Mark” to help consumers choose among smart devices that are safer and less vulnerable to cyberattacks. In Asia, the governments of Singapore and South Korea launched an initiative to develop a mutual recognition of IoT security certification schemes. These developments expose market gaps that individual companies and industry alliances are ill-positioned or unwilling to address.

Watching the Horizon

Whether they are labelled opportunities or challenges, other market gaps will shape the IoT industry over 2024 and beyond. Professional media sites such as LinkedIn and Medium are starting to fill up with individuals offering their IoT implementation services, a sure sign that supply and demand are rising up a notch.

Connectivity continues to dominate. To borrow a 1990’s marketing phrase that was commonly applied to sell the commercial Internet, connectivity is analogous to the ‘on-ramp’ for the IoT. However, connectivity represents one of a growing number of elements that contribute to an IoT solution. As the population of connected devices grows it will require both a structured framework and a suite of management services to interoperate at scale. This might emerge as the communications and cloud industries converge on 3GPP planning and a shift in emphasis to massive machine type (mMTC) use cases.

Governments and society are coming to terms that easy access to the Internet results in an asymmetric relationship between users and infrastructure and application providers. As an illustration of the challenges ahead, the Matter protocol set out to make connectivity simple and straightforward for consumers. While homeowners can mix and match devices from a growing ecosystem of suppliers, they still have to choose a home platform for management functions. This element, sitting above the connectivity layer seems to be dysfunctional and not just in a technical sense. As one reviewer put it, “now that Matter is here, these companies are wholly unmotivated to ensure their platforms work well with their direct competitors”. This is an appealing business scenario for system integrators and large platform (or ‘gatekeepers’ in competition law terminology) providers. The supply side of the industry will need to address issues of security, interoperability, certification and possibly data rights now that the wheels of government are rolling.

Another facet of the connectivity discussion is about interworking. How will deployments in large spaces function when they combine wide-area (cellular, satellite) and short-range devices (Bluetooth, Wi-Fi, Zigbee)? Edge computing concepts are applicable so that gateways aggregate short-range connectivity devices, for example. However, there is still a need for additional functionality to provide oversight and management functions and to make these capabilities appealing to developer communities. This dynamic will persist as the number of connected devices grows because many of these will be constrained by factors such as their energy envelope, power budget and sleep-modes of operation. Expect to see an extension of GSMA and TMForum efforts to define APIs that make intelligent management functions accessible to IoT system operators and developers.

A final observation relates to the scope of IoT. Many associate the term with connectivity and connected devices, as if connectivity is the biggest hurdle to overcome. Business users have progressed beyond connectivity and are increasingly adding IoT data management and remote-control capabilities as they deploy solutions for priority or business-critical use cases. Over the longer term, however, users will need to view IoT through a ‘system of systems’ lens. There will be situations that require cross-silo interoperability involving multiple IoT solutions and service providers working together. In addition to business model innovation, the technical challenges associated with improved decision-making will rely on making IoT work with digital twins as well as AI and ML algorithms in a systematic way. Today’s quick and easy solution is to concentrate IoT data in a cloud environment where all processing, intelligence and reporting are centralized. However, quite apart from ceding value to the cloud provider, there will be longer-term requirements for data provenance tracking and causal reasoning that call for bi-directional data flows. The proliferation of constrained IoT devices will call for edge processing and the coordination of distributed information processing and intelligence. These are reasons why notions of IoT connectivity and solutions, in multi-stakeholder settings, need to embrace system of systems approaches.

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• Nokia Drone Networks, available for purchase, lease or one-off flight hire are bundled with compelling services delivering lower cost of ownership so more enterprises and service providers can take advantage of industrial drones.
• Nokia and its partners within the private wireless space, such as DXC Technology, will collaborate with regulators and the industry ecosystem for flight approvals to achieve drone innovation and safety.
• Backed by 4G/LTE and 5G connectivity for safe, reliable and Beyond Visual Line of Sight operations, NDN will allow enterprises to see fastest return on investment, when deployed with Nokia Digital Automation Cloud private wireless-as-a-Service.

Nokia announced the extension of a Drones-as-a-Service partner program with existing private wireless partners to provide Nokia Drone Networks benefits to industries in North America.

Through this collaboration, enterprises and service providers will be able to rapidly realize the capabilities of drones, in a way that meets the needs of their business as well as benefit from new use cases and revenue streams.

Enterprises can opt to purchase the Nokia Drone Networks industrial grade drone-in-a-box solution or minimize upfront capital expense by leasing, sharing or simply hiring drones for one-off or regularly scheduled flights. Nokia and the members of its partner program will offer bundled service packages tailored to the needs of enterprises, including managing data analytics or drone maintenance. The companies will also manage flight scheduling, working with relevant spectrum and airspace regulators to gain approvals, where required.

Drone service providers will benefit from being able to diversify their offering to more industries and realize new revenue streams. This is made possible through Nokia’s commitment to collaborating with industry partners, allowing users to customize the payloads, to meet their business needs.

Companies that choose to deploy or lease Nokia Drone Networks will benefit from an end-to-end solution, which has recently received certification from the Federal Communications Commission (FCC) for operation in North America.

Nokia Drone Networks uses public and private 4G/LTE and 5G network connectivity for the greatest reliability and safety of Beyond Visual Line of Sight (BVLOS) operations. Operated remotely for one-off or scheduled flights, the solution benefits from a highly ruggedized docking station that doubles as a recharging station. This makes it possible for enterprises to rely on the solution to realize new efficiencies and capabilities such as surveillance at remote facilities, environmental or stockpile monitoring at a mine, managing inspections in hazardous locations or for public safety search and rescue operations.

Deployed in conjunction with Nokia Digital Automation Cloud (DAC) and Nokia MX Industrial Edge at their campus network, enterprises will be able to leverage robust, reliable connectivity, service predictability and the ability to process data obtained from the drones in real-time, on-premises to open the door to an array of innovative use cases and accelerate their return on investment.

Stephan Litjens, Vice President of Enterprise Campus Edge Solutions at Nokia, says: “We are pleased to leverage our existing private wireless partnerships, such as the one with DXC Technology to offer Drones-as-a-Service to the North American market. This allows us to offer our innovative solution in a way that meets the complex needs of any enterprise or industry. Through our work with partners across all regions, we’ll maintain a strong collaboration with spectrum and airspace regulators and the ecosystem to ensure we balance innovation with safety for customer flights.”

While visiting Nokia in Espoo, to witness the Drone use cases firsthand, the DXC Team stated: “We want our customers to book a drone flight with the simplicity of hailing a ride-share. Enterprises will benefit from simplicity, leveraging a complete FCC certified drone-in-a-box solution provided by a single vendor without headaches as we manage the flight approval for them working with the relevant authorities. They can adopt drones however it makes sense for their business. And those that choose to deploy the drone-in-a-box solution alongside Nokia DAC private wireless will leverage the greatest capabilities, unlocking new use cases for the fastest return on investment.”

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Kigen and TEAL unveil a new SGP.32-supported eUICC solution compatible with TEAL cloud platforms for acceleration of global eSIM adoption.

TEAL, the first US-based eSIM platform to be certified by the GSMA, is partnering with Kigen to make available a version of the Kigen eSIM Operating System (OS) with SGP.32-ready features, preloaded with TEAL’s in-house SGP.32 Cloud Platforms (eIM, SM-DS).

This partnership is a game changer supporting Original Equipment Manufacturers (OEM) and machine-to-machine (M2M) customers that will soon transition to solutions that support the new eSIM for IoT specification (SGP.32).

“The upcoming SGP.31/32 specification reduces the commercial complexity of switching between eSIM profiles and paves the path for LPWAN devices to leverage standardized RSP at scale,” observes John Canali, Principal Analyst for IoT at Omdia.

“We estimate this will boost global active cellular IoT connections by billions of new connections through 2028, aided by interoperability as companies adopt more adaptable solutions.”

TEAL’s wholly owned and patented eSIM Platform technology connects any device onto any data network worldwide, giving businesses everywhere the flexibility and control to remotely switch between networks over the air. With the GSMA’s consumer eSIM for IoT specification, SGP.32, becoming the go-to deployment model for eSIM, this partnership sets a new standard for the management of IoT at scale.

Kigen eSIM OS combined with TEAL’s SGP.32-cloud platforms prioritizes pragmatic steps to assist TEAL’s customers with the transition to IoT eSIM. The leading choice for energy-efficient devices, Kigen eSIM OS has undergone rigorous testing worldwide for interoperability across cloud platforms and eIM improving reliability and performance. OEMs looking to avoid battery draw for downloads of connectivity profile in-field benefit from the additional capability to embed connectivity profiles with in-factory provisioning before the device ships.

“We are thrilled to partner with Kigen to provide a comprehensive solution melding Kigen’s SGP.32 operating system (OS) and TEAL’s advanced eSIM Platform technology,” said TEAL CEO, Robert Hamblet. “This partnership will enable us to provide our customers with the most forward-thinking eSIM technology available, ensuring the highest level of reliability and performance for any IoT deployment.”

“Kigen is driving the collaboration around the dramatic simplification of IoT expected in the upcoming GSMA SGP.32 standard, and customers at the forefront of innovation need to prepare for this transition.” Kigen CEO, Vincent Korstanje added, “Our partnership with TEAL offers an outstanding combination of cloud and device level stack for eSIM technology at scale.”

This new solution available from Kigen and TEAL will streamline device integration and will facilitate broader eSIM adoption worldwide. Enterprise customers will also benefit from support for iSIM technology as physical eSIMs become a thing of the past.

The post TEAL and Kigen Team Up to Provide Groundbreaking SGP.32 eSIM Combination appeared first on IoT Business News.

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.

The post The leading generative AI companies appeared first on IoT Business News.

Berg Insight presents a unique database covering the 500 largest cellular IoT deployments identified as part of the company’s world-class IoT market research activities since 2004.

The extensive list includes various types of projects and product categories deployed across all types of vertical markets including OEM automotive, aftermarket automotive, transport & logistics, utilities, infrastructure, buildings & security, retail, industrial, consumer electronics, healthcare and other. The database includes project size and geographical distribution by the end of 2022 as well as a 5-year forecast for each individual project.

“The projects included in the top-500 list together account for approximately 548 million active cellular IoT subscriptions”, said Rickard Andersson, Principal Analyst, Berg Insight.

He adds that this corresponds to as much as 20.6 percent of the total number of cellular IoT connections worldwide at the end of 2022. The 500 projects on the list are in the coming years forecasted to grow to 1,024 million units by 2027, corresponding to an overall compound annual growth rate (CAGR) of 13.3 percent.

Mr. Andersson continued:

“More than 100 deployments on the list have reached 1 million subscriptions or more, and the top-10 projects alone account for over 183 million units.”

Transport & logistics is the largest vertical in terms of the number of projects that made the top list, followed by aftermarket automotive, retail, utilities, OEM automotive, buildings & security and healthcare.

“When comparing the number of active subscriptions represented by each vertical for the entries in the top-500 list, OEM automotive is instead the largest vertical, accounting for 191 million units, ahead of utilities at 118 million units and transport & logistics representing 97 million units”, concluded Mr. Andersson.

The post The 500 largest cellular IoT deployments together account for 548 million units appeared first on IoT Business News.

Berg Insight estimates that the global installed base of active off-highway vehicle telematics systems reached 7.9 million units in 2022.

This includes connected units deployed on various off-highway vehicles across the construction, mining, agriculture and forestry sectors. The construction sector accounts for the largest share, driven by OEM telematics systems offered by heavy equipment manufacturers. Agriculture and mining moreover each account for a similar number of connected units deployed on machines and vehicles used in agricultural and mining operations respectively. The remainder is represented by the forestry sector including telematics systems fitted to various forestry equipment. Growing at a compound annual growth rate (CAGR) of 13.1 percent, the active installed base of off-highway vehicle telematics systems across all sectors is forecasted to reach 14.6 million units worldwide in 2027.

“The top-10 equipment manufacturers offering telematics together account for around 70 percent of the total number of off-highway vehicle telematics systems in use across the construction, mining, agriculture and forestry sectors globally”, said Rickard Andersson, Principal Analyst, Berg Insight. Berg Insight ranks Caterpillar as the leading off-highway vehicle telematics provider.

“Far from surprising, the leading equipment manufacturer by market share is also by far the top player when it comes to the number of off-highway vehicle telematics subscribers”, said Mr. Andersson.

He adds that this is a natural development in an industry where telematics has increasingly been included as standard with the machine purchase, gradually covering more models and equipment categories.

“Caterpillar was the first to surpass the milestone of 1 million connected assets across all segments and the company now has more than 1.4 million units”, said Mr. Andersson.

The runners-up are SANY and Komatsu. Other major manufacturers with estimated installed bases of more than 100,000 units include Volvo Construction Equipment, Deere & Company, XCMG, Hitachi Construction Machinery, JCB, HD Hyundai and Doosan Bobcat.

“Additional players having estimated installed bases of off-highway vehicle telematics units in the tens of thousands include CNH Industrial, Liebherr, Terex, JLG Industries, AGCO, CLAAS Group and Tadano”, concluded Mr. Andersson.

The post The installed base of off-highway vehicle telematics systems to reach 14.6 million units worldwide by 2027 appeared first on IoT Business News.

The rise of the Internet of Things (IoT) has ushered in a new era of possibilities to transform traditional homes into smart, automated spaces. This comprehensive project plan delves into the essential components of building a smart home with IoT, covering goal definition, device selection, network architecture, security considerations, integration strategy, testing, optimization, and ongoing maintenance. Let’s take a look!

What is IoT?

The Internet of Things (IoT) is an innovative paradigm that envisions a network of interconnected devices, physical objects, and systems, all of which can communicate and exchange data over the Internet. Essentially, IoT transforms everyday objects into intelligent data-generating devices that can be monitored, controlled, and optimized for a variety of purposes.

The core of IoT is based on integrating sensors, actuators, and communication technologies into devices to enable data collection and transmission. These devices range from common household items such as thermostats and refrigerators to industrial machinery and portable devices. Data generated by these devices can be analyzed to derive insights, automate processes, and improve decision-making.

The key components of IoT include:

Sensors: Devices with sensors can collect information from the environment, such as temperature, humidity, movement, and light.

Connectivity: IoT devices transmit data to a central system or other connected devices using various communication protocols such as Wi-Fi, Bluetooth, Zigbee, and cellular networks.

Data Processing: The collected data is processed through cloud-based platforms or edge computing where it is analyzed, stored, and transformed into actionable information.

Actuators: Based on insights gained from data analysis, IoT devices can activate actuators to perform specific actions, such as: adjust settings, trigger alarms, and control other connected devices.

I. Defining Smart Home Goals

Before starting a smart home project, it is important to define clear goals to guide the implementation process. Consider specific areas you want to improve, such as home automation, security, energy management, or entertainment.

Prioritize these goals to ensure your projects stay focused and deliver tangible benefits. For example, if energy efficiency is a priority, you might set a goal such as reducing your energy consumption by a certain percentage or optimizing your heating and cooling system. If security is a concern, define goals such as implementing robust surveillance systems, smart locks, and intrusion detection mechanisms. Also, ensure your contractor uses field service management software to manage your project.

II. Device Selection

Selecting the right devices is the cornerstone of a successful smart home. Consider the following categories of IoT devices and their potential applications.

Smart Lighting:

• Choose energy-efficient smart light bulbs that can be controlled remotely via a dedicated app.
• Introduce motion sensors and automating lighting according to occupancy, contributing to both energy savings and comfort.

Smart Thermostat:

• Choose a thermostat that learns your preferences over time and adjusts temperatures for maximum comfort and energy efficiency.
• Integrate your thermostat and weather forecast to proactively adjust heating and cooling settings based on external conditions.

Home Security Camera:

• For added security, choose a high-resolution camera with motion detection.
• Ensure compatibility with central security systems for an integrated approach to monitoring and responding to potential threats.

Smart Door Lock:

• Choose a smart lock that can be controlled remotely via a mobile app.
• Implement features such as virtual keys and access logging to improve security and user accountability.

Smart Sensors:

• Install sensors that monitor environmental factors such as temperature, humidity, and air quality for a comprehensive understanding of your home’s health.
• Integrate security sensors to detect anomalous activity like an unexpected door opening.

III. Network Architecture

Network architecture forms the backbone of a smart home and enables seamless communication between devices. Consider the following:

Network protocols:

• Choose wireless protocols such as Zigbee or Z-Wave for low-power, short-range communication between devices.
• Ensure compatibility with Wi-Fi for high-bandwidth applications such as video streaming.

Hub or Controller:

• Select the central hub or controller that matches the selected device and protocol.
• Evaluate whether the hub supports the integration of various devices and has the computing power to handle the communication load.

Bandwidth considerations:

• Evaluate the bandwidth requirements of each device to ensure a stable and responsive network.
• Select devices with low latency requirements like security cameras and smart door locks to maintain real-time responsiveness.

IV. Security Considerations

With the interconnectedness of IoT devices, ensuring the security of your smart home becomes paramount. Implement the following security measures:

Device Authentication:

• Enable a robust authentication mechanism for each IoT device that requires unique credentials.
• Update your passwords regularly and consider implementing multi-factor authentication as an additional layer of security.

Encryption:

• Implement end-to-end encryption to secure communications between devices and the central hub.
• Always ensure that data sent over the network is encrypted to prevent unauthorized access or interception.

Regular Software Updates:

• Keep all central hub devices and firmware up to date to address security vulnerabilities.
• If possible, enable automatic updates to help your smart home remain resilient to new threats.

Network Segmentation:

• Segment your network to separate IoT devices from critical systems such as PCs and work-related devices.
• Create separate virtual local area networks (VLANs) to increase security and control access.

V. Integration Strategy

Achieving a seamless, integrated smart home experience requires careful planning and execution. Consider the following strategies:

Platform Compatibility:

• Ensure that the selected device is compatible with popular smart home platforms such as Amazon Alexa, Google Assistant, and Apple HomeKit.
• For better interoperability and future-proofing, choose devices that adhere to open standards.

Automation Rules:

• Define automation rules based on user settings and routines.
• Leverage the power of the Intelligent Assistant to create voice-activated commands for your users.

Mobile Application:

• Choose a device with an easy-to-use mobile application for easy control and monitoring.
• The mobile app provides real-time updates and alerts to keep you informed of the status of your smart home.

VI. Testing and Optimization

Once your smart home is set up, thorough testing is essential to identify and resolve issues. Run the following test:

Functional testing:

• Verify that each device functions as intended and responds accurately to commands and triggers.
• Testing automation rules ensure a seamless and reliable experience.

Security Audits:

• Conduct periodic security audits to identify and remediate vulnerabilities.
• Test the responsiveness of security features such as intrusion detection and video surveillance.

User Experience Testing:

• Gather user feedback to evaluate the overall experience of interacting with your smart home.
• Make adjustments based on user settings to address vulnerabilities identified during testing.

VII. Maintenance and Upgrades

Smart Homes require regular maintenance to ensure optimal performance.Create a maintenance plan that includes:

Periodic Equipment Checks:

• Schedule periodic checks to ensure that all equipment is functioning properly.
• Replace or upgrade end-of-life equipment to maintain peak performance.

Firmware Updates:

• Keep both devices and the central hub up to date with firmware updates.
• Quickly implement updates to address security vulnerabilities, introduce new features, and improve overall performance.

Scalability:

• Plan for future expansion by choosing devices and infrastructure that can easily scale.
• Consider integrating new technologies to keep your smart home at the forefront of innovation.

VIII. IoT and Sustainability

The convergence of IoT and sustainability represents a powerful partnership that has the potential to address environmental challenges and contribute to a more resource-efficient and greener future. IoT technology plays an important role in promoting sustainability through several key mechanisms.

Energy Efficiency: IoT-enabled devices with sensors and intelligent algorithms enable precise monitoring and control of energy consumption. In the context of a smart home, this means intelligent lighting systems that adjust brightness based on occupancy, thermostats that optimize heating and cooling, and appliances that operate at peak efficiency. By minimizing energy waste, IoT reduces overall energy consumption and reduces carbon emissions.

Smart Resource Management: IoT sensors can monitor and manage valuable resources such as water and gas. In smart homes, water and gas sensors can instantly detect leaks, preventing unnecessary waste and potential environmental damage.

Waste Reduction: IoT devices help reduce waste by optimizing waste collection processes. Smart containers equipped with sensors can monitor the filling level and send a signal when it needs to be emptied. This not only increases the efficiency of waste management systems but also reduces unnecessary transport and associated emissions.

Environmental Monitoring: IoT plays an important role in monitoring and controlling environmental pollution. Air quality sensors can detect pollutants in real-time, allowing authorities and communities to take timely actions to address air quality issues. This information allows individuals to make informed decisions about their daily activities, contributing to a healthier living environment.

Renewable Energy Integration: IoT technologies improve the integration of renewable energy sources into the power grid. A smart grid can integrate energy generated from solar panels, wind turbines, and other renewable energy sources and dynamically balance energy supply and demand. This not only promotes the use of clean energy but also contributes to the stability and resilience of the entire energy infrastructure.

There You Have It

Building a smart home using IoT is a dynamic and challenging process that requires careful planning, device selection, network configuration, and ongoing maintenance. Improve comfort, security, and energy efficiency by defining clear goals, choosing the right devices, establishing a robust network architecture, prioritizing security, and implementing an effective integration strategy. You can build a smart home that not only makes your home more accessible but also more adaptable to new technologies. Regular testing, optimization, and maintenance are key to ensuring your smart home continues to meet your changing needs in the ever-changing landscape of IoT technologies.

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The global smart city movement represents a major shift in how urban environments are designed, experienced and navigated. This monumental change is driven in part by digital transformation and Internet of Things (IoT) technologies, which are reshaping urban infrastructure and cityscapes into hubs of intelligent connectivity. Central to this trend is the emergence of advanced wireless technologies that align with the unique demands of smart cities. Among these emerging technologies, Wi-Fi CERTIFIED HaLow^TM stands out as an ideal wireless protocol for smart city connectivity.

Wi-Fi HaLow, an evolution of conventional Wi-Fi, is purpose-built to serve the needs of IoT applications. Incorporating the IEEE 802.11ah standard, it was released as a new certification by the Wi-Fi Alliance in November 2021. Wi-Fi HaLow operates in the sub-GHz band and surpasses traditional Wi-Fi in the 2.4, 5 and 6 GHz bands in terms of range, coverage and power efficiency, redefining the boundaries of wireless connectivity for smart city and IoT applications. Wi-Fi HaLow has the capacity to connect more than 8,000 devices from a single access point, providing long range connectivity beyond 1 km, low power consumption, advanced Wi-Fi CERTIFIED WPA3^TM security, and massive network density – precisely the attributes demanded by smart cities.

Building on the strengths of the IEEE 802.11ah standard, Morse Micro is developing next-generation Wi-Fi HaLow solutions that extend 10 times farther and cover 100x the area of traditional Wi-Fi networks. These advancements further the goals of smart city application developers, facilitating long-range connectivity, automating urban services and promoting environmental sustainability.

A prime example of this innovation is the potential impact of Wi-Fi HaLow networks on smart city transit systems. Traditional network infrastructure upgrades often reach bottlenecks due to the high cost and complexity of expanding wireline networks, underscoring the need for new forms of long range wireless technology. In such scenarios, Wi-Fi HaLow’s superior range, penetration and performance offer a transformative solution, far surpassing the range limitations of conventional Wi-Fi in the 2.4, 5 and 6 GHz bands while outperforming the low data rates of low-power wide-area networks (LPWANs) such as LoRa.

Wi-Fi HaLow’s versatility enables it to combine diverse building automation systems into a unified connectivity platform that provides an optimal balance of speed and range, and allows innovative IoT applications that may combine video with sensors, for example. It provides seamless connectivity between real-time operational data and the people and systems managing smart buildings, data centers, industrial processes, and other urban utilities. Its extended range and advanced security make it ideal for connecting a plethora of subsystems, from HVAC and smart lighting to microgrids and edge AI cameras.

By using standards-based Wi-Fi HaLow, the total cost to deploy and manage network services for smart cities is lower than other wireless solutions. Wi-Fi HaLow uses license-free radio spectrum in its operation, and Wi-Fi HaLow enabled equipment can be sourced from multiple OEMs. Unlike cellular service providers, which charge fees to use their networks, there is no recurring cost to use Wi-Fi HaLow connectivity. Expert personnel who understand Wi-Fi technology are plentiful and can use well-established methodologies to operate and maintain Wi-Fi HaLow networks. These economic benefits help reduce smart city operating costs, and the savings can trickle down to a municipality’s citizens.

On an enterprise level, Wi-Fi HaLow supercharges a wide array of smart city applications including security and safety systems, energy management, maintenance, occupant services, utility billing, demand management, indoor air quality (IAQ) monitors and compliance systems. With its distinct advantages in range, power efficiency, network capacity and security, Wi-Fi HaLow can equip these applications with the capacity to handle amounts of IoT device connectivity, significantly enhancing operations and services within a smart city.

Wi-Fi HaLow’s unique blend of long range, low-power consumption, advanced security and high-density connectivity is transforming smart city applications. Whether in support of automated transit systems, streamlined building operations or enhanced enterprise applications, Wi-Fi HaLow is a powerhouse protocol capable of addressing the myriad needs of a smart city. Its ability to connect thousands of IoT devices across sprawling urban landscapes enables efficient data sharing and automation, driving improved city services, environmental sustainability, and a higher quality of life for residents.

As cities worldwide transition to smart, connected environments, advanced wireless protocols like Wi-Fi HaLow have become key enablers of technology innovation. By providing a connectivity solution tailored to the distinct requirements of IoT applications, Wi-Fi HaLow isn’t merely contributing to the development of smart cities – it’s setting a higher standard of wireless communications. Wi-Fi HaLow’s growing market momentum represents a significant leap toward a smarter, safer, and more connected future, reshaping our urban landscapes one city at a time.

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Today’s businesses rely heavily on IT systems for smooth operations, increased productivity, and remaining competitive. But just like any complex machinery, IT requires regular upkeep to function at its peak level.

In the initial quarter of 2023, global data breaches exposed over six million data records. This alarming statistic underscores the critical importance of prioritizing the health and security of your IT infrastructure. Neglecting these aspects can have serious repercussions, including system failures, security breaches, and an adverse effect on your overall business operations.

Our comprehensive guide outlines essential steps and best practices for maintaining the health of your IT system.

Learn How to Manage Vulnerabilities

Effectively managing vulnerabilities is paramount in maintaining a secure IT environment. This is where the vulnerability management lifecycle comes into play, providing a strategic framework for the continuous identification, assessment, and mitigation of system vulnerabilities.

This approach involves a systematic cycle of activities, beginning with the discovery and assessment of vulnerabilities, followed by prioritization based on risk and potential impact. Subsequently, a mitigation plan is devised, which may involve implementing patches, configuration changes, or other security measures.

Regular monitoring and reassessment ensure that the system remains resilient against emerging threats. This proactive and cyclical process not only strengthens the overall security posture but also aligns with the dynamic nature of cybersecurity, allowing organizations to adapt swiftly to evolving threats and protect their digital assets effectively.

Data Backup and Recovery

Data is essential to any organization, and protecting it should be prioritized. Create an effective data backup and recovery plan to safeguard against accidental deletions, hardware failures, or cyberattacks. Regularly back up critical data to secure offsite locations, and conduct periodic tests to ensure the integrity and effectiveness of your backup system.

Consider employing automated backup solutions to streamline the process and reduce the risk of human error. Additionally, document and regularly update your disaster recovery plan, outlining the steps to be taken in case of a catastrophic event.

Network Security

Securing your network is crucial to maintaining the overall health of your IT system. Start by implementing a robust firewall to monitor and control incoming and outgoing network traffic. Regularly update and review firewall rules to adapt to evolving threats.

In addition, purchase and implement a reliable antivirus/anti-malware solution to detect and eliminate malicious software. Conduct regular security audits to identify vulnerabilities in your network infrastructure and promptly address them.

For added security, consider implementing a virtual private network (VPN) to encrypt communication between devices and ensure secure data transfer, especially when accessing the network remotely.

Hardware Maintenance

Hardware components are the backbone of any IT system, and their proper functioning is integral to system health. Develop a routine hardware maintenance schedule that includes cleaning, inspection, and testing of all equipment. Dust and debris can accumulate within hardware, leading to overheating and reduced performance. So, regularly clean and inspect servers, routers, switches, and other hardware components to prevent these issues.

Monitor hardware performance metrics and address any anomalies promptly. Replace aging or faulty hardware components before they fail to avoid unexpected downtime and potential data loss.

User Training and Awareness

Human error remains one of the leading causes of IT system issues. Educate your employees on best practices for IT system usage and security. Conduct regular training sessions to update them on the latest cybersecurity threats and preventive measures.

Encourage the use of strong, unique passwords and implement multi-factor authentication to enhance user account security. Establish clear policies regarding the use of personal devices on the corporate network to mitigate the risk of unauthorized access.

Monitoring and Performance Optimization

Implementing a robust monitoring system is essential for proactive IT system maintenance. Utilize monitoring tools to track performance metrics, detect anomalies, and identify potential issues before they escalate. Monitor network traffic, server performance, and application responsiveness to ensure a smooth user experience.

Regularly review and optimize system configurations to enhance efficiency and resource allocation. Consider implementing automated monitoring alerts to notify IT personnel of potential issues in real time.

Documentation and Change Management

Maintaining accurate and up-to-date documentation is vital for efficient IT system management. Document network configurations, hardware specifications, software licenses, and other critical information. This documentation serves as a valuable resource for troubleshooting, upgrades, and future planning.

Implement a robust change management process to track and manage modifications to your IT environment. Clearly define and document all changes, assess their potential impact, and communicate them to relevant stakeholders. This helps prevent unforeseen issues and ensures that changes align with business objectives.

Conclusion

A proactive approach to maintaining your IT system health is essential for the smooth operation of your business and the protection of sensitive data. By following the comprehensive guide outlined above, you can establish a solid foundation for IT system maintenance.

Managing vulnerabilities, data backups, network security, hardware maintenance, user training, monitoring, and documentation, all play an integral part in maintaining an efficient and secure IT infrastructure. By investing time and resources in these key areas, you can avoid downtime, enhance system performance, and protect against cyber threats—an essential aspect of success in today’s digital era. A healthy IT system forms the backbone of successful businesses in the digital era.

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According to a new research report from the IoT analyst firm Berg Insight, the global market for precision agriculture solutions is forecasted to grow from € 3.1 billion in 2022 at a compound annual growth rate (CAGR) of 11.4 percent to reach about € 5.2 billion in 2027.

A set of technologies are applied in precision farming practices, which are aimed at managing variations in the field to maximise yield, raise productivity and reduce consumption of agricultural inputs. While solutions such as auto-guidance and machine monitoring and control via on-board displays today are mainstream technologies in the agricultural industry, telematics and Variable Rate Technology (VRT) are still in the early stages of adoption. Interoperability between solutions remains a challenge, although initiatives to provide common protocols and language structure for data sharing are making progress.

Most major agricultural equipment manufacturers have today operations related to precision agriculture with varying strategies. Leading vendors of precision agriculture solutions include the world’s largest manufacturer of agricultural equipment Deere & Company, followed by the precision technology vendors Trimble, Topcon Positioning Systems, Raven Industries and Hexagon. Major input manufacturers like BASF, Bayer, Corteva Agriscience and Syngenta have entered the space primarily through acquisitions and focus on providing mapping tools and decision support for the purpose of input optimisation and yield maximisation. A group of companies have surfaced as leaders on the nascent market for in-field sensor systems. These include Semios, Pessl Instruments with its METOS brand, Davis Instruments and Sencrop.

The move from automation to autonomy is the next step in the evolution of the agricultural industry. “Although autonomy on a component level has been exploited by multiple manufacturers, autonomous agricultural operations on an equipment level are now on the rise”, said Veronika Barta, IoT analyst at Berg Insight.

Today, original equipment manufacturers are developing autonomous machines such as driverless tractors and seed-planting robots. Agricultural drones are the most advanced segment, performing autonomous operations by utilising multispectral cameras, LiDAR sensors and route algorithms. Aerial imagery for crop monitoring is the most common application area, followed by spraying operations of crop protection chemicals. Satellite navigation, sensors, artificial intelligence and machine learning will be the main facilitators of autonomous equipment in the future of farming.

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According to a new research report from the IoT analyst firm Berg Insight, the global installed base of connected vending machines reached an estimated 5.8 million units in 2022.

North America represents the largest share of these machines, closely followed by the Rest of the World markets. Both are estimated to have installed bases of around 2.1 million each.

In Europe, the installed base of connected vending machines is estimated to have reached around 1.5 million units. Berg Insight forecasts that the number of connected vending machines worldwide will grow at a compound annual growth rate (CAGR) of 16.4 percent to reach 12.3 million units by 2027. As a result, the global penetration rate will reach 75.7 percent at the end of the forecast period.

The global connected vending solution market is served by a variety of players. Many of the leading providers are specialised technology companies offering connected vending telemetry and cashless payment solutions. US-based Cantaloupe is established as the clear leader in terms of installed base with more than 900,000 connected vending machines, mainly in North America.

Other leading technology suppliers include Crane Payment Innovations, Nayax, Ingenico and Televend (INTIS). The latter has continued to show significant growth and is clearly established as a top player on the European market. MatiPay, Vianet Group and Vendon are additional examples of technology players with relatively significant installed bases in Europe. In China, InHand networks holds a prominent position and is estimated to have installed its solutions in close to 300,000 vending machines by the end of 2022.

“The number of connected vending machines continues to grow and has now reached very high levels, particularly in North America”, said Felix Linderum, IoT Analyst at Berg Insight.

In Europe and the Rest of the World markets penetration levels are still modest but growing. Overall, cashless payments continue to be the main driver for adding connectivity in vending machines.

“This development was further reinforced by the Covid-19 pandemic”, continued Mr. Linderum.

He adds that because of the pandemic, there has been an accelerated adoption of cashless and contactless payments as users seek versatility, convenience and safety to a larger extent than before. Outside of North America and Europe, the main driver for growth is the growing installed base of connected vending machines in China. While stringent lockdowns enforced during the Covid-19 pandemic led to a slowdown in the country, there has been a noteworthy surge in the number of vending machines in China over the past few years. Berg Insight anticipates that the country is poised to emerge as a key player in the connected vending space.

Mr. Linderum concluded:

“Mobile payments and remote management of multimedia content are basically standard on the vending machines in China. Facial recognition technology is also a growing trend and enables pay-with-your-face functionality.”

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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 today’s fast-paced industrial and manufacturing sectors, safety and efficiency are paramount concerns. Companies are increasingly turning to innovative technologies to transform their workplace culture, with the Wearable Internet of Things (WIoT) taking center stage. This cutting-edge technology is not only overhauling traditional practices but also revolutionizing the way companies approach worker safety and productivity. In this article, we will delve into the ways wearable technology is currently reshaping the industrial and manufacturing landscape, explore the myriad benefits of WIoT, and shed light on the software solutions that are propelling this revolution.

A Shift in Workplace Culture

Industrial and manufacturing environments have long been associated with rigorous physical demands and safety concerns. However, as technology advances, so too does the ability to safeguard workers and improve overall efficiency. Wearable technology, in particular, has emerged as a game-changer. From smart helmets to augmented reality glasses, these devices are revolutionizing the way workers interact with their environment.

One of the most significant shifts brought about by WIoT is the move towards a more proactive approach to safety. Traditionally, safety measures were often reactive, focusing on addressing incidents after they occurred. With wearable devices, companies now have access to real-time data that enables them to identify potential hazards before they become accidents. For example, smart vests equipped with sensors can monitor environmental conditions, such as temperature and air quality, alerting workers and management to unsafe conditions instantly.

The Multifaceted Benefits of WIoT

The adoption of WIoT is not solely driven by safety concerns; it also promises a host of other benefits. One of the most compelling advantages is its ability to reduce worker fatigue. In physically demanding industries, fatigue can lead to accidents and decreased productivity. WIoT devices can monitor a worker’s biometrics, such as heart rate and body temperature, in real-time. When fatigue is detected, alerts can be sent to both the worker and their supervisor, prompting necessary breaks or adjustments to tasks.

Furthermore, WIoT is facilitating the digital transformation of facilities. These devices are no longer just tools for monitoring workers; they are becoming integral components of interconnected systems that optimize operations. For instance, by equipping machinery with IoT sensors, it becomes possible to track equipment performance, anticipate maintenance needs, and reduce downtime. This seamless integration of WIoT technology results in cost savings and improved efficiency.

Enhancing Body Mechanics with Wearable Devices

The realm of body mechanics in the workplace is also being revolutionized by WIoT. Wearable devices, such as exoskeletons and wearable sensors, are designed to support workers and help them maintain correct postures and motions. These devices are equipped with sensors that can provide real-time feedback to workers, guiding them to adopt ergonomic positions that reduce the risk of musculoskeletal injuries.

Additionally, the data and understanding collected by these wearable devices is a goldmine of information. To harness this potential, companies are turning to sophisticated software solutions. These solutions aggregate data from various wearable devices and integrate it into a centralized platform. This allows for comprehensive analysis and insights that were previously unattainable.

For example, advanced analytics can identify patterns of movement and posture that may lead to injuries over time. By utilizing this data, companies can implement targeted training programs to improve worker ergonomics and reduce the risk of chronic injuries. Furthermore, the data can be used to engineer workflows, optimize the allocation of tasks and resources for maximum efficiency.

The Power of Integration

Integration is key to unlocking the full potential of WIoT. By consolidating data from wearable devices into a single platform, companies can achieve a holistic view of their operations. This data-driven approach enables predictive maintenance, real-time safety monitoring, and workflow optimization, all within one cohesive system.

Moreover, the benefits of WIoT extend beyond the factory floor. Office-based employees can also benefit from wearable technology, as it can monitor posture and sedentary behavior, promoting better health and well-being. For instance, smart wristbands can remind office workers to take breaks, stretch, or adjust their sitting positions, reducing the risk of long-term health issues.

Embracing Innovation: WIoT’s Role in Shaping Tomorrow’s Workplace

The Wearable Internet of Things is ushering in a new era of workplace culture, where safety, efficiency, and worker well-being take center stage. Companies that embrace WIoT are not only reducing the risk of injuries but also driving digital transformation, reducing worker fatigue, and optimizing operations. With the integration of advanced software solutions, the potential for improvement is boundless.

As more companies recognize the transformative power of WIoT, it is clear that this technology is here to stay. It is no longer a matter of if, but when, organizations will adopt WIoT to enhance worker safety and productivity. The future of industrial and manufacturing workplaces is being shaped by wearable technology, and those who embrace it are poised to lead the way in the evolving landscape of worker safety and efficiency.

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According to a new research report from the IoT analyst firm Berg Insight, there were a total of 2,900 private LTE/5G networks deployed across the world at the end of 2023, including trial and pilot deployments.

Private 5G network deployments are moving from trials to commercial operations and amounted to an estimated 700 networks whereof trials accounted for close to half. Until 2028, the number of private LTE/5G network deployments are forecasted to grow at a compound annual growth rate (CAGR) of 33 percent to reach 11,900 networks at the end of the period. Increasingly, the networks will be deployed into commercial operations faster as there is less need for use case testing. A meaningful number of private LTE network deployments will also be upgraded to 5G, starting in the next 2–3 years.

Berg Insight defines a private cellular network as a 3GPP-based private LTE/5G network built for the sole use of a private entity such as an enterprise or government organisation. Referred to as non-public networks by the 3GPP, private LTE/5G networks use spectrum defined by the 3GPP and LTE or 5G NR base stations, small cells and other radio access network (RAN) infrastructure to transmit voice and data to edge devices.

“The major RAN vendors (Ericsson, Nokia and Huawei) all play significant roles as integrated solution providers and are challenged by a number smaller RAN equipment providers”, said Fredrik Stalbrand, Principal Analyst, Berg Insight.

Nokia counts the largest number of private network deployments with more than 635 private cellular network customers at the end of Q2-2023.

Mr. Stalbrand continued:

“The vendors increasingly pursue channel-led sales strategies, and have developed ecosystems of mobile operators, system integrators, VARs and consulting partners to bring solutions to market.”

A number of small cell and other RAN equipment providers including Airspan Networks, Baicells, CommScope, JMA Wireless, Mavenir, Samsung Networks, Sercomm and ZTE provide competitive LTE/5G radio products and in some cases complete private network offerings.

Important specialised core network software vendors include Druid Software, Athonet (acquired by HPE in June 2023), as well as Affirmed Networks and Metaswitch (both part of Microsoft since mid-2020). In total, EPC/5GC offerings are available from close to 30 vendors. A third category is IT-centric players like Cisco and HPE. These companies focus on delivering fully integrated Wi-Fi and private LTE/5G solutions, enabling network managers to administer Wi-Fi and private LTE/5G networks through a single pane of glass. Celona is a new entrant in the space, backed by NTT Data and Qualcomm, offering its integrated private cellular solution in a single SaaS subscription.

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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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Quectel Wireless Solutions, a global IoT solutions provider, today announces a collaboration with Syrma SGS Technology Ltd, a leading electronic manufacturing services provider, to facilitate the manufacturing of modules in India, aligning with the Make in India initiative.

The collaboration is designed to leverage both Quectel’s extensive experience in cellular and connectivity modules and Syrma SGS’s innovative and efficient electronic system design and manufacturing skills to manufacture a range of IoT modules in India for customers both in India and globally.

The Make in India initiative was launched in September 2014 as part of a wider set of nation-building initiatives and is designed to transform India into a global design and manufacturing hub. With this partnership, Quectel is leveraging India’s ascent as a global leader in electronics manufacturing sector and the strategic choice to establish manufacturing operations in India enables Quectel to broaden its business footprint and explore new models within the country. As India’s technology industry continues to flourish, Quectel is well-equipped to cater to its evolving needs through this partnership.

“By combining Quectel’s cutting-edge technology and Syrma SGS’s expertise in electronics manufacturing, we aim to deliver unparalleled innovation to the market,” commented Norbert Muhrer, President and CSO, Quectel Wireless Solutions.

“This synergy reflects our shared commitment to providing top-notch, reliable solutions, and we are excited about the transformative impact this collaboration will have on the industry, enabling us to effectively meet the evolving demands of the Indian and global market.”

Mr. Krishna Pant Co-founder, Syrma SGS said “This collaboration signifies a union of Syrma SGS’s excellence in electronics manufacturing and Quectel’s cutting-edge technology, promising to deliver unparalleled value to our clients and the industry at large.”

The partnership is poised to bring about a positive impact on the electronic manufacturing ecosystem delivering high-quality products and services to clients worldwide, offering customers a broader spectrum of solutions and empowering businesses to thrive in the era of connected devices and smart technologies. The collaboration aims to cater to the diverse needs of industries, including telecommunications, automotive, smart metering, sound boxes, and other sectors. The partnership will provide clients with solutions that meet their specific requirements, and both companies share a commitment to upholding the highest standards of quality in their products and services.

Quectel’s global presence will play a pivotal role in establishing a seamless and efficient supply chain. This international reach ensures timely delivery and comprehensive support across borders, reinforcing the partnership’s ability to meet the demands of a wide array of industries.

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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.

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Neue announces its sensor as a service offering that integrates world-leading security out-of-the-box from Kigen to dramatically reduce key product development hurdles for companies seeking cutting-edge connectivity solutions, enabling the development of groundbreaking connected products.

The combination of the solutions that address two principal challenges faced by product designers looking to industrialize always-on, connected products, the no-code, quick path offers significant speed-to-market.

Empowering Connected Products:

Neue, celebrated for its innovative IoT platform, has teamed up with Kigen, a leading expert in securing connected solutions. The collaboration empowers clients globally using the Neue iEnbl, ensuring that wherever there is a need for a SIM, eSIM, or iSIM, these are readily available from the very start in a way that extends to full-scale deployments.

This partnership sets the stage for developing a wide array of new connected products and services, from smart devices to industrial IoT solutions and beyond. The Neue iEnbl has been adopted by 30 customers at various stages, from prototyping to large scale deployments, and is now generally available. The versatile suite of technologies packed in the Neue iEnbl hardware, iSIM enablement, and no-code software reduces on average, between 6-12 months of development time. Customers can leverage manufacturing expertise from Neue’s and Kigen’s mutual partner, Flex.

The collaboration combines the strengths offering:

1. Seamless Connectivity: Effortlessly integrate SIM, eSIM, and iSIM technologies into products and services using the Neue iEnbl that support a number of connectivity options: LTE Cat-M, NB-IoT, 5G, WiFi, BLE 4.2, RS-232/RS-485, GNSS (GPS, GLONASS, GALILEO, BeiDou).

2. Security: Leverage Kigen’s secure OS for all types of eSIM and iSIM, with expertise in secure connectivity to guarantee data protection and reliability in an increasingly connected world.

A Unified Vision:

“We are thrilled to collaborate with Kigen to pioneer a no-code path to the era of innovative connected products”, remarked Fredrik Wanhainen, CEO of Neue. “Leveraging iSIM resonates with our vision of democratizing IoT with the Neue iEnbl, a complete sensor as service solution and Playground creation environment, empowering our clients to create connected products and services that break new ground.”

“The promise of the integrated iSIM is to unleash secure connected solutions for any company, independent of their experience in cellular,” affirmed Vincent Korstanje, CEO of Kigen.

“Together, we are empowering customers who are building the future of IoT and AI with security that’s ever-present and available out of the box, opening up new frontiers for connected experiences.”

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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?

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