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

Introduction to Edge Computing in IoT

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

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

Enhanced Efficiency and Reduced Latency

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

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

Improved Security and Privacy

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

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

Enabling Advanced IoT Applications

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

Challenges and Considerations

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

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

The Future of Edge Computing in IoT

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

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

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Splunk Edge Hub combats data deluge by bridging the data collection gap of physical and edge environments.

Splunk Inc., the cybersecurity and observability leader, today announced Splunk Edge Hub, a new solution that simplifies the ingestion and analysis of data generated by sensors, IoT devices and industrial equipment.

Unveiled at .conf23, Splunk Edge Hub provides more complete visibility across IT and OT environments by streaming previously hard to access data directly into the Splunk platform. Supported by Splunk partner solutions and optimized to work with the Splunk platform’s predictive analytics, Splunk Edge Hub enables advanced monitoring, investigation and response to help organizations drive digital resilience across their systems.

Data Deluge at the Edge

More organizations are realizing the significant benefits of edge computing. This distributed computing framework brings data transfer and storage closer to the data sources themselves to improve response times and save bandwidth. While edge computing is emerging as a driver of innovation, the process of identifying and gathering data in large quantities across multiple physical and virtual sources can be incredibly complex, tedious and costly.

Extends Splunk’s disruptive technology to highly fragmented environments

Splunk Edge Hub streamlines edge data collection and investigation by breaking down the barriers and silos of data access across physical and virtual environments and acting as a data aggregator from other vendors’ platforms. Working right out of the box, the device can be placed in a physical environment or on top of a customer’s existing OT hardware and easily configured to immediately collect, collate and stream data to the Splunk platform.

When combined with the Splunk platform, Splunk Edge Hub enables customers to:

• Monitor environmental conditions, including water, temperature, humidity and gasses to quickly and efficiently identify and remediate problematic conditions.
• Perform predictive analytics to identify anomalies in manufacturing processes and surface early indications of equipment maintenance needs or outages, to minimize operational downtime.
• Achieve more comprehensive visibility across IT and OT environments to better detect, investigate and remediate threats and IT stressors from a single platform.
• Build custom solutions through industry experts across environments that are historically difficult to extract data from, including transportation, oil and gas and supply chain, among others.

Supporting Quotes:

“At GrayMatter, we know getting business insight from your data is a challenge,” said Kemell Kassim, GrayMatter VP Cyber. “Partnering with Splunk allows us to facilitate data collection for customers and integrate in an easily consumable way.”

“Strategic Maintenance Solutions is thrilled to announce our partnership with Splunk to deliver the all-new Edge Hub,” said Jason Oney, President of Strategic Maintenance Solutions. “The Edge Hub enables us to provide our customers with an end-to-end solution for accessing industrial sensor, maintenance, and operations data at scale. With minimal configuration needed, data can now be seamlessly streamed into the Splunk Platform, allowing our customers to quickly start down the Industrial Transformation journey.”

“The University of Illinois Urbana-Champaign has been using Splunk for more than a decade as a part of our mission to serve our students, faculty, and researchers, so we were very interested in testing its latest offering, Splunk Edge Hub, to monitor our data center spaces,” said Nick Vance, Assistant Director, Data Innovation – Technology Services, University of Illinois Urbana-Champaign. “Spanning across 19,000 square feet, we are testing integrating sensors for room and rack temperatures and leak detection, ensuring we monitor temperature changes and flow as they fluctuate with infrastructure load. By sending data from these sensors into Splunk, we can more easily view it, expedite alerts and respond to issues before they become severe. The ability to stay ahead and respond quickly to any problems helps us protect tens of millions of dollars in equipment, so continually improving our monitoring technology is highly valuable for the University.”

“In today’s fast-paced business landscape, innovation is key to staying ahead of the competition. LG Electronics is leveraging Splunk Edge Hub to disrupt traditional industry models and drive innovation with edge computing and AI,” said Bongsu Cho, Vice President, AI & Big Data Division at LGE. “Splunk Edge Hub is enabling us to go beyond data and into automating our physical operations.”

“The only way to truly improve resilience is to be able to see everything going on within your organization,” said Tom Casey, SVP & GM of Products and Technology at Splunk. “Edge Hub is breaking down barriers and providing access to data that has historically been difficult to extract and integrate, to empower our customers with a level of visibility they have never had before. Our partners can use Splunk Edge Hub to build even more solutions across a multitude of industries that are tailor-made to their needs.”

Availability:

Splunk Edge Hub will be exclusively distributed through authorized domain expert partners who can tailor the solution to solve critical business and operational challenges within their industries. Splunk Edge Hub is currently available on a Limited Availability Release in the United States, with plans to expand to EMEA and APAC.

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The 4th annual survey reveals an increased enterprise reliance on open source technologies to accelerate the adoption of IoT and Edge Computing solutions.

The Eclipse Foundation, one of the world’s largest open source foundations, today announced the availability of its 2022 IoT and Edge Commercial Adoption Survey report, based on an online survey of more than 260 IoT and edge professionals conducted from April 1st to June 15th, 2022.

The survey’s objective is to gain a better understanding of the IoT and edge computing ecosystems by identifying the requirements, priorities, and challenges faced by organizations that deploy and use commercial solutions, including those based on open source technologies.

Mike Milinkovich, executive director of the Eclipse Foundation, said:

“IoT and edge computing continued to accelerate in 2022 and into 2023 with no signs of slowing down, despite the current macroeconomic climate. These trends suggest that IoT and Edge are thought to be strategic investments that deliver true ROI. The open source model will only augment these benefits.”

Survey participants represent a broad set of industries, organizations, and job functions. Nine of the top conclusions drawn from the survey data include:

• IoT technologies are being adopted at an accelerated rate. 53% of respondents currently deploy IoT solutions and an additional 24% plan to deploy within the next 12 to 24 months, while 18% are currently evaluating deployments.

• Edge computing adoption is also on the rise. 53% of organizations are either utilizing or planning to utilize edge computing technologies within 12 months. Another 20% are currently evaluating the use of edge deployments.

• There is a shift towards higher investments into IoT & Edge. 23% of respondents project spending between $100K – $1M in 2022, growing to 33% in 2023. 10% anticipate spending over $10M and growing to 12% in 2023.

• There is a trend towards a larger number of IoT & Edge assets managed per deployment. Deployments of fewer than 1K managed assets will remain steady or decline, while larger deployments are on the rise. In terms of asset implementation, 52% are a mix of both greenfield and brownfield.

• More organizations now see IoT and edge as strategic, with spending decisions being driven at the executive level 38% of the time. This increased by 3% compared to the year 2021.

• 73% of organizations factor open source into their deployment plans. This clearly demonstrates that the dominant IoT & edge platforms will either be open source or based on open source. Only 27% of organizations using IoT and Edge technologies state they do not use open source technologies.

• The primary benefits of using open source according to respondents include: the ability to customize or influence code in projects (30%); flexibility (22%); as well as cost advantages (16%).

• The top 3 IoT and edge operational challenges are: 1) connectivity; 2) security; and 3) data collection & analytics.

• There is a trend towards a hybrid cloud strategy. 42% of respondents suggest that IoT deployments are using, or will use a hybrid cloud (i.e. composed of two or more distinct cloud infrastructures such as private and public).

The report also includes details on IoT and edge adoption by industry and the top concerns of commercial adopters.

The post The Eclipse Foundation Releases 2022 IoT and Edge Commercial Adoption Survey Results appeared first on IoT Business News.

Cypress Microcontrollers and Connectivity Devices, Software, Tools, and Ecosystem Partners Make IoT Product Development Faster, Lower Cost, and Lower Risk.

Cypress Semiconductor Corp. today unveiled solutions that give IoT product developers a simplified path to build high-quality, secure, and reliable IoT products.

The solutions, branded IoT-AdvantEdge, include connectivity devices and microcontrollers, software, tools and support, and capabilities from ecosystem partners to slash development complexity by solving critical IoT product design problems. With IoT-AdvantEdge, companies can overcome the challenges of wireless connectivity, device and cloud security, power consumption, device management and maintenance, component integration, consumer ease-of-use, human-machine interfaces, and platform monetization, to quickly bring reliable, secure, high-quality products to market.

“Building intelligent, connected products for the IoT edge is challenging. Getting wireless and embedded systems to work together and dealing with issues like security, cloud integration, and power management can be time-consuming and expensive,” said Hassane El-Khoury, president and CEO of Cypress.

“The problem solvers at Cypress have taken on this challenge and expanded our solutions to help our customers bring high-quality, secure, and reliable products to market faster. IoT-AdvantEdge simplifies the development process, bringing together the essential building blocks of the IoT through powerful software and hardware combinations.”

Cypress’ IoT-AdvantEdge solutions include:

• Devices: Cypress has a unique portfolio of microcontrollers, Wi-Fi, and Bluetooth/BLE devices that work in concert to support a broad range of IoT product requirements from battery-operated cameras to healthcare products. It integrates security and robust communication technologies and is used in many of the world’s most-sophisticated IoT products.
• Software: Robust software is fundamental to building high-quality, secure, and reliable products, and Cypress’ software is built for the IoT. Cypress’ ModusToolbox® development toolchain dramatically simplifies the development of Wi-Fi and Bluetooth/BLE IoT products with RTOS system MCUs like Cypress’ PSoC® family. ModusToolbox includes middleware empowering companies to connect their products to leading cloud-software platforms or to proprietary cloud services on public or private cloud infrastructure. In addition, Cypress’ open-source contributions into the Linux kernel are one of the reasons its Wi-Fi and Bluetooth products are so broadly used by IoT developers.
• Tools & Support: A comprehensive set of tools and support are critical to effectively address the unique challenges of IoT product design. Cypress’ tools – including low-power assistant, multi-radio smart coexistence, secure authentication, and over-the-air updates – substantially reduce the time and cost required to bring high-quality products to market. Cirrent (a Cypress subsidiary) provides IoT Network Intelligence (INI), a cloud-based analytics platform that delivers unprecedented insights into connectivity, networking, and other product-performance parameters for fleets of products in the field. Cypress engineers passionately support the IoT community and customers in tackling the challenges of building IoT products.
• Ecosystem Partner Capabilities: Building IoT products often requires capabilities from a broad ecosystem of suppliers. Cypress pre-integrates capabilities from a broad range of partners – including cloud service providers, application-specific semiconductor products, and applications developers – to help companies bring their IoT products to market faster.

As part of the launch of IoT-AdvantEdge, Cypress announced several new IoT products and resources:

• New Microcontrollers: Cypress expanded the PSoC 6 MCU family with two new memory configurations for the PSoC 62 and PSoC 64 Secure MCU lines: a high-performance, dual-core M4F/M0+ platform with 2MB flash and 1MB SRAM, and a cost-effective MCU with 512kB Flash and 256kB SRAM. These two new configurations give product companies more flexibility to choose the right configuration for their application.
• New Development Kits: Cypress’ new kits include low-power solutions that help designers tackle the challenges of complex connectivity, secure provisioning, secure firmware management, and cloud security. These integrated solutions are powered with ModusToolbox software and validated for AWS IoT Core and Pelion cloud applications, easing development of home automation and portable consumer devices.
  • PSoC 62 2M with 43012 Pioneer Kit: ultra-low power M4/M0+ MCU with dual-band 802.11n Wi-Fi and Bluetooth® 5.0
  • PSoC 62 512K with 4343W Prototyping Kit: cost-effective HMI MCU with 802.11n Wi-Fi and Bluetooth 5.0
  • PSoC 64 2M Secure Boot with 4343W Pioneer Kit: enables secure cloud connectivity and provisioning, and secure firmware management
  • Cypress-Azurewave Module Pioneer Kit: pre-certified module delivers a turnkey connected MCU platform that is easily integrated into any product
• New Software: ModusToolbox combines Cypress’ secure compute and connectivity expertise in a common software tool, making it easier for IoT developers to bring successful products to market and to support them through the lifecycle. Cypress released ModusToolbox version 2.1, which expands its cloud support beyond AWS IoT Core and Pelion services to include specific support for customers that have built their own cloud backends. In addition, ModusToolbox now supports five popular IDEs so developers can use the environment of their choice.
• New Web Resources: Cypress launched IoT-AdvantEdge on Cypress.com and a new IoT Developer Zone for its online developer community. The Cypress Community offers robust discussion forums, technical blogs and a resource library for visitors around the globe. The Cypress Community also helps customers, developers, and partners connect with their peers, access a vast depth of resources, and ultimately bring high-quality, secure, and reliable products to market faster.

“We are driven to make IoT technology ubiquitous, with Cypress solutions in every IoT device,” El-Khoury said. “IoT-AdvantEdge solutions reflect our commitment toward achieving that goal.”

The post Cypress Unveils IoT-AdvantEdge™ Solutions Providing Developers a Trusted Design Path to IoT Edge Products appeared first on IoT Business News.

• First platform of the LoRa Edge portfolio is a geolocation solution revolutionizing IoT devices for asset management
• Platform integrates an ultra-low power LoRa® transceiver, GNSS and Wi-Fi scanning technologies
• Combining LoRa Edge solutions and Cloud-based geolocation services creates a unique system architecture that offers the best balance between location accuracy and low power consumption

Semtech Corporation has today announced LoRa Edge, a new highly versatile and low power software defined LoRa®-based platform that will enable a wide portfolio of applications for indoor and outdoor asset management, targeting industrial, building, home, agriculture, transportation, and logistics markets.

The first product from this portfolio is a geolocation solution that revolutionizes the development of Internet of Things (IoT) devices for asset management applications, featuring low power Wi-Fi and GNSS sniffing capabilities combined with simple to use and cost effective LoRa Cloud geolocation and device management services to significantly reduce the cost and complexity of locating and monitoring IoT assets.

“Semtech continually delivers Internet of Things (IoT) solutions that simplify and accelerate the development of LPWAN applications,” said Pedro Pachuca, Director of IoT Wireless in Semtech’s Wireless and Sensing Products Group.

“LoRa Edge and LoRa Cloud geolocation services enable customers to develop ultra-low power applications for a variety of industries and will expand the mass adoption of LoRa in the IoT ecosystem.”

Over the next decade, 500 billion devices are expected to connect to the internet (Cisco), as organizations continue to shift towards a more IoT-focused business strategy, and the majority of those IoT devices require some form of localization capability either at point of install or through the asset’s life.

The LoRa Edge geolocation platform will enable solution providers to leverage the unique localization capabilities of LoRa as well as GNSS and Wi-Fi scanning capabilities from a single chip solution, allowing customers to choose the best localization tool for the application task they are addressing.

By removing the need for incremental GNSS and Wi-Fi components, LoRa Edge reduces the bill of material (BOM) costs of devices and significantly reduces design and procurement complexity. With the addition of LoRa Cloud geolocation services, providing easy-to-use and cost effective TDOA, GNSS and Wi-Fi-based location calculation in the Cloud to dramatically reduce device power requirements and improve asset management efficiency, LoRa Edge enables customers to further manage total cost of ownership (TCO), paying only when they need an asset to be located. The best-in-class key provisioning at point of manufacture and a secure join process further simplifies the development of IoT solutions, which adhere to customers exacting expectations of security.

The first LoRa Edge chipset targeted with geolocation (LR1110) is available today and more products from this portfolio will be released in the first half of 2020.

Key Product Features:

Multi-Purpose Radio Front-End

• 150 – 2700 MHz continuous frequency synthesizer range
• GPS/BeiDou scanning
• Wi-Fi passive scanning

Low-Power LoRa/(G)FSK RF Transceiver

• Worldwide frequency bands support in the range 150 – 960 MHz
• High power PA path +22 dBm
• High efficiency PA path +15 dBm
• Fully compatible with the LoRaWAN® standard

Cryptographic Engine

• Hardware support for AES-128 encryption/decryption based algorithms
• Handling device parameters such as DevEUI and JoinEUI
• Protects confidential information such as encryption keys
• Stores NwkKey, AppKey, as defined in the LoRaWAN standard

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Internet Of Things, commonly called IoT, refers to the connection of daily devices like cars, home appliances, and industrial devices to the internet. As Gartner predicts IoT will reach 26 billion connected devices by 2020. One of the challenges in IoT is to capture, analyze and gain insights from data from this massive volume of devices effectively and efficiently. Most Internet Of Things Devices equipped with sensors have two parts- firstly, a front-end application or a device like Coffee Maker; Door lock (generally closer to the consumer) and secondly, a cloud where the data from the device goes and is then processed to build context (generally remote to the consumer). The first one is referred to as “IoT Edge” and the second one is called the “IoT Cloud.” The activities happening on the IoT Edge part is known as “Edge Computing.”

Microsoft, CISCO, IBM, Dell, and many startups are championing Edge computing; now that represents a shift in IoT implementation architecture. In Edge computing, data intelligence happens on the Edge instead of the Cloud which resulted in localizing certain kinds of data analysis and decision-making. Edge computing enables quicker response times, resolves network latency and reduced data traffic by sending only selective data to the cloud. Edge computing helps to achieve better efficiencies.

Two major components in Edge computing are Edge computing hardware and Edge computing software. The processing power of the Edge hardware plays a crucial role in determining the Edge software capability. Usually, a high computing device like a desktop or server or custom Edge hardware is a good choice. Edge software typically comprises of components that are required to establish the connection with the device sensors like Wifi, Bluetooth, ZigBee, Z-Wave and components needed to extract and store data from the device sensors like database.

The third components are necessary to perform data analysis like data analytics and machine learning. Edge Analytics and Edge Machine learning have been around for a few years now. There are many open source and proprietary software in the market. However, they had limited use for the last few years due to the complexity of including them in low compute power legacy IoT network solutions. AWS Edge and Azure Edge are a couple of prominent Edge software from Amazon and Microsoft. Apache Kafka and Scikit are open source implementations of Edge Analytics and Machine learning respectively.  

Of late, the Edge software package that has been making waves is Edge AI. Edge AI is Artificial Intelligence capability on the IoT Edge and is also a prime place for more innovation, as  adding these advanced software capabilities into a limited computing power resource is considered to be technically challenging. The goal of Edge AI is to understand, learn and act on the data from the IoT devices without any human intervention.

Edge Software is taking center stage in IoT applications due to its ability to offer lower network latency, faster reaction times on the IoT data and lower cost of operating on the data without much support of cloud computing. Because of this reason, we will start seeing them more in legacy and new IoT applications. We will see all IoT platforms, both of established players and startups embracing Edge software into their platform offerings. The hyper-scale cloud players like AWS, Microsoft and Google have been slow to enter the Edge software space; solely because Edge can have negative effect on their IoT cloud revenues. However, the technology and business case for the Edge software, as discussed above, has become prominent recently, that nobody can afford to ignore the role of Edge. Hence, this is forcing the IoT cloud players to explore newer revenue models that includes both IoT Cloud and IoT Edge Date intelligence revenue streams rather than riding only on the IoT cloud revenue stream.  

Edge Data Intelligence has become a key part of Enterprise IoT strategy already and there is lot of technology advancements happening. This will continue to have a positive impact on the IoT applications as the industry gets into a mass adoption phase.

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