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Bench Talk for Design Engineers

Bench Talk


Bench Talk for Design Engineers | The Official Blog of Mouser Electronics

New Tech Tuesdays: Edge Computing: The Future of Distributed Data Processing Rudy Ramos

Join Rudy Ramos for a weekly look at all things interesting, new, and noteworthy for design engineers.

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, aiming to improve response times and save bandwidth. Instead of sending data to a central data center or cloud for processing, edge computing processes the data at the edge of the network, near the source of the data (Figure 1). IoT devices, sensors, or local micro-data centers can utilize this computing paradigm.

Figure 1: Edge computing functions as an intermediate layer between devices and the cloud, handling services using distributed edge nodes. (Source: VectorMine/

In an era where real-time data processing and interconnectivity are paramount, edge computing has emerged as a transformative force. Often regarded as an evolution in the field of computing, edge computing decentralizes the traditional model of processing data in a central location (i.e., large data centers), thereby bringing these capabilities closer to the data source. This shift in the computing paradigm has widespread implications for various industries and is backed by recent developments. However, while the benefits are considerable, it is essential to be aware of the challenges that come with this emerging computing model.

Recent Developments in Edge Computing

The swift ascent of edge computing is propelled by various technological advances, starting with the expanding world of the Internet of Things (IoT) and 5G technology. The accelerated increase in IoT integration—with billions of IoT devices getting connected yearly—is driving the demand for quicker data processing closer to the device source. IoT applications, especially in healthcare, automotive, and industrial automation, are increasingly leveraging edge computing for real-time processing. With these wide-ranging applications of IoT comes the need for optimized network solutions.

And answering the call is 5G technology. The rollout of 5G networks is propelling edge computing forward. 5G's high bandwidth and low latency complement edge computing's need for rapid data processing near data sources. Additionally, many industries are integrating artificial intelligence (AI) and machine learning (ML) algorithms at the edge to allow for real-time decision-making. This includes facial recognition in security systems and applications, and predictive maintenance in manufacturing and industrial automation.

The move to containerize applications using tools like Docker and Kubernetes has allowed for seamless deployments across different environments, from central servers to edge locations. Also, micro-data centers are sprouting up, bringing the necessary infrastructure closer to the data sources. Recent innovations in security protocols now cater to the vulnerabilities presented by edge devices. These vulnerabilities are due to the distributed nature of edge devices. Additionally, the emergence of specialized hardware, like AI chips tailored for edge deployments, is also helping to keep edge computing safer. And let’s not forget the strategic collaborations between tech giants and telecom players that are paving the way for integrated cloud, 5G, and edge solutions.

Challenges in the Edge Landscape

While edge computing offers enormous transformative potential, it must overcome several challenges to fully realize that potential. The inherent complexity of managing multiple devices and locations can be daunting. Additionally, security challenges continue to pose a significant concern, especially with the increased vulnerabilities that come with distributed devices. Another concern stems from ensuring data consistency across the board, as data conflicts have increased potential. For example, if an edge computing machine goes down, has the data been backed up properly and can a new machine take its place seamlessly?

There are also energy efficiency concerns. Concentrating your processing power in a data center is more efficient, so an edge computing system will likely use more energy than a comparable cloud-based system. There is also the issue of harsh environments. Edge devices may have to be designed for harsh environments as opposed to a climate-controlled data center. Like the energy bullet, this will increase how much an edge system costs versus a comparable cloud system, adding to the cost of ownership.

Other challenges lay with interoperability due to the variety of devices and vendors in the edge computing landscape, as well as the shorter lifecycle of these devices. Edge devices, especially consumer-grade ones, typically have a shorter lifecycle than enterprise-grade servers in large data centers, leading to frequent replacements. Lastly, edge devices might offer limited resources. While edge devices have become more powerful, they might still have limitations in terms of processing power, storage, and memory compared to centralized data centers.

Another major hurdle is the initial high cost of setting up an edge infrastructure and the additional maintenance associated with remote devices, which can be burdensome and expensive. These are valid reasons that might deter some from immediate adoption.

In the end, there's a potential trade-off between latency reduction and bandwidth costs that adopters and designers of edge computing will have to consider. One thing is for certain: in the upcoming years, innovation and technical advancements will continue driving the distributed data landscape, and businesses must remain agile, anticipating the next big breakthrough just ahead.

Featured Products

This week’s New Tech Tuesday features products from two companies leading the way in edge computing technology: ADLINK Technology and Advantech. These products deliver real-time processing enhancements and benefits to edge computing.

In the evolving realm of edge computing, ADLINK's Express-ADP Type 6 Module emerges as a trailblazer. The introduction of the COM Express COM.0 Revision 3.1 epitomizes the blend of future tech trends with the present, supporting advanced interfaces like PCI Express Gen4 and USB 4. Its popular form, designed particularly for x86-based silicon, caters to a myriad of applications, from gaming and medical to industrial automation. The module's prowess doesn't end there; its design, equipped with the 12th Gen Intel® Core processor, ensures efficiency in IoT tasks and is backed by the latest memory tech and superior AI capabilities.

What truly sets the Express-ADP apart is its unparalleled graphics performance, capable of delivering up to four 4K60 HDR displays. Its specifications, ranging from 64GB DDR5 to diverse connectivity options, solidify its position as a must-have for those venturing into edge computing. In essence, ADLINK’s Express-ADP isn't just a piece of technology; it's the cornerstone of next-gen edge computing solutions.

Advantech's ThinManager® Pocket-Size Edge IoT Thin Client, ESRP-CMS-U2271V2, offers a sleek integration with ThinManager® software, designed to display content streamed from a centralized ThinManager server. This system allows multiple clients to be stationed across facilities, each playing unique roles, yet all centrally controlled by the server. The content-centric server architecture ensures minimal downtime during failures, given that all content is hosted server-side. The ESRP-CMS-U2271V2, powered by Intel® Celeron® N6210, is not only compact and fanless, but also offers a modular design. For businesses diving into edge computing applications, these Edge IoT Thin Clients stand out as a prime solution, ensuring a solid and reliable data foundation. By bolstering a centralized and consistent data stream, they pave the way for IoT success, making it an optimal choice for industrial applications needing dual-monitor support and centralized factory floor management.

The Thin Client boasts a series of certifications, including CE, FCC, UL, and more. With a solid aluminum casing, it measures a compact 100mm x 70mm x 30mm and weighs a mere 1.1lb. Mounting is versatile, with options for stands, walls, and optional VESA and DIN rail attachments. Its power consumption peaks at 30W, and it is designed to be compatible exclusively with ThinManager software, ensuring a seamless and reliable experience pivotal for achieving IoT triumphs.

Tuesday’s Takeaway

Edge computing, with its promise of reduced latency and enhanced real-time processing, stands as a beacon for the future of distributed data processing. Driven by technological advancements and the expanding universe of IoT, 5G, AI, and ML, its influence is evident across numerous industries, from healthcare and automotive to industrial automation. However, as with any technological evolution, it brings its set of challenges. Addressing these issues head-on by focusing on security, data consistency, energy efficiency, and, of course, cost is crucial for the continued evolution of edge computing. As we forge ahead, the equilibrium between the centralized and decentralized models of computing will shape the next chapter in the digital revolution.


  • Xie, David. “Top Six Edge Computing Trends To Know About In 2023.” Forbes, March 17, 2023.

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Rudy RamosRudy is a member of the Technical Content Marketing team at Mouser Electronics, bringing 35+ years of expertise in advanced electromechanical systems, robotics, pneumatics, vacuum systems, high voltage, semiconductor manufacturing, military hardware, and project management. As a technology subject matter expert, Rudy supports global marketing efforts through his extensive product knowledge and by creating and editing technical content for Mouser's website. Rudy has authored technical articles appearing in engineering websites and holds a BS in Technical Management and an MBA with a concentration in Project Management. Prior to Mouser, Rudy worked for National Semiconductor and Texas Instruments.

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