AI on the Edge: IoT and Edge Computing Redefine Data Architectures

Promises Yet to be Fulfilled

While IoT and edge have been evolv­ing for more than a decade, they are still very much works in progress. A survey of IT executives conducted by Schneider Electric, which finds that IoT and edge adoption keeps rising, reports there are still pain points that need addressing. The executives point to managing hybrid IT infrastructure—local IT and cloud—as their greatest current challenge. Integra­tion with existing infrastructures and skills requirements also top the list.

“Existing edge and IoT initiatives have yet to fulfill the promises of true connec­tivity anywhere, in any situation,” Itz said. “With some existing IoT devices relying on continuous power or even a stable internet connection in order to function, outages are not only more probable but have the potential to impact business out­comes severely.”

Edge and IoT are still held back by frag­mentation and multiple standards. “The IoT ecosystem is fragmented, with a vast array of platforms, protocols, and stan­dards,” said Mishra. “This complicates the delivery of universally compatible AI applications. The persistent diversity in communication protocols and data for­mats across devices continues to present a tough challenge.”

Standardizing at the device level “is an essential step in enhancing the functional­ity and efficiency of these devices,” Bajpai agreed. “There also needs to be the estab­lishment of standards in place for edge node coordination. Efficient orchestration and coordination mechanisms can dis­tribute workloads dynamically and opti­mize resource allocation on a distributed or disconnected edge, leading to more efficient and effective operations.”

Connectivity also is a challenge to effective employment of IoT-enabled edge networks in the agricultural sector, said Corio. “Sometimes, it is just general cel­lular or satellite coverage that is lacking. Other times, terrain, trees, and farm build­ings can interfere with otherwise strong communications infrastructure.” The need for rugged field sensors and devices is also an issue. “There is always the challenge of data volumes pushing beyond the capabil­ities of available, affordable bandwidth,” he continued. “Even though network cover­age and speeds continue to dramatically improve, it seems that data generation and transmission still outpace capacity.”

Ultimately, organizational support is key to fulfilling the vast promise of edge and IoT. “Education on the transforma­tive potential of edge and IoT technol­ogies still needs to percolate to decision makers at a large majority of enterprises,” said Bajpai.

The Big Picture

Successfully building edge and IoT capabilities infused with AI requires con­siderable organizational resources and planning. Proponents need to keep the big picture in mind: How will the busi­ness and customers benefit?

Edge AI “offers opportunities to auto­mate repetitive, mundane tasks, unlocking unprecedented insight and intelligence to advance more business operations,” said Corio. “Collecting the data is the easy part—assuming you have the connectiv­ity you need. But using the data to pro­vide business value requires a custom­er-first mentality to create products that people love to use.”

Holistic approaches and solutions must consider how the business needs to move forward in its markets—whether it wants to evolve into a service business that monitors and repairs its products or become a data company. “When the focus is on point solutions that address one use case at a time, it’s difficult to provide effective business insights that show patterns across an organization,” said DeBacker. “All edge technologies need to integrate into the infrastructure and, more importantly, integrate into infrastructure management platforms, so it’s easy for teams to pull out actionable insights that move the business forward.”

AI “will further increase the useful­ness of these technologies, opening up a broad range of possibilities for data collection, assimilation, and conversion into useful, real-time information,” said DeBacker.

Education and awareness are also key to moving forward with edge AI. Busi­ness leaders need to understand how it will add value, increase revenues, and cut costs. Technology and data profession­als also need knowledge of the various components. This encompasses “famil­iarity with edge computing architectures encompassing edge nodes, gateways, and cloud integration [that] is essential for designing scalable and resilient IoT solu­tions,” said Gil Dror, CTO at SmartSense, by Digi. “Proficiency in data analytics tailored for edge environments enables efficient processing and interpretation of data at the edge, facilitating timely insights and informed decision making. A proactive approach to security and incorporating encryption, access con­trol, and threat detection mechanisms is essential for safeguarding sensitive data and preserving the integrity of IoT ecosystems in an increasingly connected and dynamic landscape.”