In the Internet of Things (IoT), edge devices seem almost an afterthought, assigned to a minor position at the boundary between IoT devices in the periphery and the sophisticated IoT software applications in the cloud. Yet, as IoT developers tackle emerging IoT requirements, edge devices will play a central role in addressing the many challenges that lie ahead in large-scale IoT systems.
In their most basic role, IoT edge devices connect IoT terminal devices with remote resources—not unlike how a telecom wire center, industrial I/O controller, and Wi-Fi router respectively connect telephones, factory automation devices, and home computers. By supporting diverse wireless technologies and protocols, an edge device can greatly simplify requirements for IoT device design. Developers can focus on the application in their IoT device designs rather than work through the limited options for wireless connectivity. Yet, the nature of IoT drives the need for edge devices able to support functionality beyond basic connectivity.
IoT applications thrive on the mass effect of hundreds or thousands of wireless sensor nodes pouring out streams of data. Deploying and maintaining those nodes in their large numbers represents a significant logistical challenge that can surely impede overall IoT success. Edge devices offer a natural solution by providing a local host for initial commissioning of massive numbers of devices onto IoT networks and handling subsequent over-the-air updates of those devices. In addition, edge devices can provide local versions of cloud-based services to maintain operations when cloud connections are down. For time-sensitive operations, edge devices can perform local processing essential to support short-latency feedback loops unable to tolerate the extra delay imposed by cloud access.
At the same time, edge devices can help ensure that deployment, maintenance, and ongoing operations remain secure from device to cloud. In partitioning off a subnet of sensor nodes, edge devices inherently offer a degree of protection unavailable in shallower IoT topologies that allow hackers to reach through the cloud to directly attack a large set of terminal devices. With their combination of isolation and greater performance capabilities, edge devices can provide more robust security necessary to mitigate all but the most determined attacks.
Edge devices also provide developers with a means to address emerging requirements more effectively. One such requirement, “privacy,” stands to rise as a critical issue driven by regulation and consumer demand. Scheduled to take effect in 2018, the European Union’s General Data Protection Regulation will impose privacy regulations not just on EU companies, but on any organization that processes data from EU residents. Concepts, such as privacy-by-default, and privacy techniques, such as data minimization, will add IoT requirements that are likely to find resolution at the edge. At the same time, as data scientists find more constraints on pushing detailed data to the cloud, IoT solutions will need to pull data-intensive algorithms, such as anonymized machine learning and advanced pattern matching, into the edge.
To meet their diverse requirements, edge devices will build on a hardware foundation that combines the features of host platforms and real-time systems, using both application processors and microcontrollers (Figure 1). As IoT intelligence expands from the cloud to encompass the edge, these devices will offer greater performance and processing specialization capabilities necessary to support algorithm sophistication in security, privacy, analytics, and more.
Developers can expect to see a growing emphasis on edge systems at all levels of the IoT solution chain—from chips to modules and boards as well as through software specialization. In fact, Arm has already identified edge devices as a key element in meeting emerging requirements in the IoT. As IoT applications evolve, edge devices will play a pivotal role in meeting more complex requirements for effective IoT solutions.
About the Author
Stephen Evanczuk has more than 20 years of experience writing for and about the electronics industry on a wide range of topics including hardware, software, systems, and applications including the IoT. He received his Ph.D. in neuroscience on neuronal networks and worked in the aerospace industry on massively distributed secure systems and algorithm acceleration methods. Currently, when he’s not writing articles on technology and engineering, he’s working on applications of deep learning to recognition and recommendation systems.
Source: Mouser Electronics
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