Developments with edge and internet of things-based initiatives may not ride the top of today’s news cycles, but there’s been a huge surge of activity around computing at the edges. IoT and edge may even be reshaping or creating more technology opportunities than artificial intelligence is — despite AI currently enjoying the lion’s share of attention.
The pervasiveness of edge and IoT computing was borne out in a survey of 1,037 IT executives and professionals, which found that control logic, or embedded automation, surpassed AI as the most common edge computing workload (40% to 37%).
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“Does this imply a renewed focus on the practical aspects of delivering real-world solutions? Only time will tell,” the survey’s authors mused.
The Eclipse survey found development increasing across all IoT sectors, including industrial automation (33%, up from 22% a year before), followed by agriculture (29%, up from 23%), building automation, energy management, and smart cities (all at 24%). Java ranked as the top language for IoT gateways and edge nodes, while C, C++, and Java are the most widely used languages for constrained devices.
When it comes to skill requirements, everyone seems to be worrying about AI design and development — however, edge and IoT bring their own skill demands.
“Key skills in designing and building edge systems involve shifting focus from traditional centralized data center approaches to understanding and optimizing the edge of networks and infrastructure,” George Maddaloni, chief technology officer for operations at Mastercard, told ZDNET. “We need to process data where it’s generated, improving data flow efficiency, and reducing the need to send large amounts of raw data to process centrally.”
Designing and constructing edge and IoT systems “requires a unique set of skills,” Tony Mariotti, CEO of RubyHome, told ZDNET. “Unlike traditional IT which often focuses on centralized data processing, edge computing demands expertise in decentralized architectures and real-time data processing. Professionals need to be adept in IoT integration, network security, and data analytics. These skills focus on rapid, secure data handling at the point of collection, crucial for applications requiring immediate insights.”
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And yes, AI and machine learning also figure into edge and IoT initiatives. This is driven by demand for “more intelligent and autonomous systems capable of making decisions in real-time, directly at the point of data collection,” Harshul Asnani, president of Tech Mahindra’s technology, media, and entertainment business, told ZDNET. “By processing data on the device itself rather than relying on cloud-based systems, these AI-enabled edge devices reduce latency, decrease bandwidth usage, and improve response times. This is crucial for applications requiring immediate action, such as autonomous vehicles, real-time analytics in manufacturing, and smart city technologies.”
The insights technology managers and professionals require to move forward with edge and IoT “include the necessity of scalable solutions to manage large data volumes and the importance of enhanced security measures,” said Mariotti. “Professionals have learned to deploy complex IoT networks that maintain integrity and confidentiality while handling sensitive data, a crucial advancement for all technology-driven businesses.”
This requires “understanding the nuances of data governance and real-time analytics,” Asnani agreed. “As data processing moves closer to the edge, managing the sheer volume, variety, and velocity of data generated by IoT devices becomes a complex task. It necessitates robust data governance frameworks to ensure data quality, privacy, and compliance with regulatory standards.”
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As edge and IoT are more likely to require real-time capabilities, “real-time or near-real-time data analytics become crucial for extracting actionable insights instantaneously, demanding more sophisticated analytical tools and techniques,” Asnani added. “Embracing edge analytics requires technological adaptation and a shift in mindset, prioritizing agility, and the ability to make decentralized decisions. Understanding these aspects will be critical for data managers and analysts to leverage the full potential of edge computing and IoT.”
Leveraging the edge and IoT has proven to be critical for MasterCard, which maintains far-flung data processing centers. The edge footprint “has shifted to something that can now use both private and public cloud,” said Maddaloni. “In public cloud, there is now a series of ‘edge cloud’ regions that we can use for containers, or for a simplified approach in our private cloud. From a resiliency perspective, we can now include both a single consolidated stack with a power distribution unit for energy backup in the case of failure as well as a cloud backup platform if needed.”
MasterCard’s edge systems also include sensors to “monitor the performance of motors, pumps, and emergency power generators,” Maddaloni added. “The ability of these sensors to automate responses to certain conditions, like adjusting cooling systems or power distribution, minimizes the need for human intervention. This automation not only enhances efficiency but also allows personnel to focus on more strategic tasks.”
There are sustainability abilities as well, said Maddaloni. “IoT provides insights that lead to energy savings, water conservation, and overall sustainability in operations. By optimizing resource usage, IoT helps in achieving greener data centers.”
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The move towards decentralized data processing “means that professionals need to understand how to leverage edge computing to enhance operational efficiency and decision-making processes,” said RubyHome’s Mariotti. “This is especially critical in sectors that rely on real-time analytics, such as healthcare, finance, and smart real estate operations.”
That brings us to the question of whether “edge” is the future for which tech and business pros need to prepare. “With the exponential growth of data at the edge and in IoT environments, a company’s edge compute capabilities could become a decisive advantage,” said Maddaloni. “The escalating volume of raw data necessitates a shift from centralized processing to edge processing to mitigate bandwidth constraints, reduce costs, and address issues like network latency and congestion.”
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