It is predicted by Gartner that 50% of the enterprise-generated data will be created and processed in the edge infrastructure comprised of ever-increasing billions of edge devices. Instead of implementing the intelligence in the cloud environment giving the full responsibility to manage a large number of edge devices, a minimal and required intelligence is imposed on the edge devices, enabling the edge intelligence that works in a master-worker approach. The major problem with this approach is that an edge device relies mostly on the data collected by the onboard sensors and the built-in or cloud-instructed intelligence, resulting in little scope for cooperation and collaboration among peers and hence no device-to-device knowledge transfer.
Unlike the traditional approach to cluster the IoT devices based on the location and other meta information, a new research direction focuses on clustering the intelligence that the edge infrastructure is equipped with, also known as Clustered Edge Intelligence (CEI). Intelligence discoverability and observability are some of the primary challenges that need to be addressed to realize the full capacity of CEI. This Ph.D. project will focus on designing and developing dynamic intelligence clustering methodology with the discoverability and observability feature for large-scale edge infrastructure.
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Please write to chinmaya(dot)dehury(at)ut(dot)ee