Developments with edge and internet-based initiatives may not be at the top of today’s news cycles, but there has been a huge increase in activity around edge computing. IoT and the edge may be reshaping or creating even more technological opportunities than artificial intelligence — although AI is currently getting the most attention.
The popularity of edge computing and IoT has been proven in one survey of 1,037 executives and IT professionals found that control logic or embedded automation surpassed AI as the most popular 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,” the survey’s authors mused? Only time will tell.”
The Eclipse survey shows growing growth across all IoT sectors, including industrial automation (33%, up from 22% a year ago), followed by agriculture (29%, up from 23%), building automation, energy management and smart cities (all at 24%). Java ranks 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 concerned about AI design and development — however, edge and IoT have their own skill requirements.
“Key skills in designing and building edge systems include shifting the focus from traditional centralized data center approaches to understanding and optimizing the network and infrastructure edge floor,” George Maddaloni, chief technology officer in charge of operations at Mastercard, told ZDNET. “We need to process data where it is created, improving data flow efficiency and reducing the need to send large amounts of raw data for centralized processing.”
Designing and building 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 requires expertise in decentralized architecture and real-time data processing. Experts need to be successful.” Proficiency in IoT integration, network security, and data analytics These skills focus on fast, secure data processing at the time of collection, critical for applications that require detailed information. details immediately.”
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And yes, AI and machine learning are also applied to edge and IoT initiatives. This is driven by the need 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 instead of relying on a cloud-based system, these AI-enabled edge devices reduce latency, reduce bandwidth usage, and improve response time. This This is important for applications that require immediate action, such as real-world, autonomous vehicles.” -time analytics in manufacturing and smart city technology.”
The insights technology managers and professionals need to move forward with Edge and IoT, Mariotti said, “include the need for scalable solutions to manage large volumes of data and the importance of enhanced security measures”. “Experts have learned how to deploy complex IoT networks that maintain integrity and security while handling sensitive data, an important advancement for all technology-driven businesses.”
This requires “understanding the nuances of data governance and real-time analytics,” Asnani agrees. “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 requires frameworks Strong data governance to ensure data quality, privacy, and compliance with regulatory standards.”
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As the edge and IoT are more likely to require real-time capabilities, “real-time or near-real-time data analytics becomes critical to extract immediately actionable insights, requires more complex analytical tools and techniques,” Asnani added. “Adopting edge analytics requires technological adaptation and a change in mindset, prioritizing agility and decentralized decision-making. Understanding these aspects will be critical for with data managers and analysts to leverage the full potential of edge computing and IoT.”
Taking advantage of IoT has proven to be crucial for MasterCard, which maintains remote data processing centers. Maddaloni said the edge footprint “has shifted to something that can now use both private and public clouds.” “In the public cloud, there are now a series of ‘edge cloud’ zones 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 unified stack with distributed unit power for power redundancy in case of failure as well as a cloud backup platform if needed.”
MasterCard’s edge systems also include sensors to “monitor the performance of engines, pumps and emergency generators,” Maddaloni added. “The ability of these sensors to automatically respond to certain conditions, such as adjusting cooling or power distribution systems, minimizes the need for human intervention. This automation goes beyond improving efficiency but also allowing employees to focus on more strategic tasks.”
There are also sustainability possibilities, Maddaloni said. “IoT provides insights that help with energy savings, water conservation, and overall sustainability in operations. By optimizing resource usage, IoT helps achieve greener data centers .”
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The move to decentralized data processing “means professionals need to understand how to leverage edge computing to improve operational efficiency and decision-making processes,” RubyHome’s Mariotti said. “This is especially important 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 “advantage” is the future for which technology and business professionals need to prepare. “With the exponential growth of data at the edge and in IoT environments, a company’s edge computing capabilities can become a decisive advantage,” said Maddaloni. “The increasing volume of raw data requires a shift from centralized to edge processing to minimize bandwidth constraints, reduce costs, and address issues such as latency and network congestion.”
