Edge Computing for Latency-Critical Task Offloading in Industry 5.0

Industry 5.0 is one of the emerging paradigms to boost ind-ustrial operations, businesses, and revolution to the next level. However, designing an efficient task priority assignment strategy and offloading them to the most rewarding Industrial edge computing (IEC) server remains challenging even in Industry 5.0. To address these challenges, we propose a novel task offloading framework called
SparseMax_BP
to optimize weighted energy consumption and execution delay in Industry 5.0. First, we classify Industrial Internet of Things generated tasks into local executable and IEC executable tasks using a probabilistic SparseMax function. Then, we transform our task-offloading problem into a device selection problem in the factor graph and utilize the advantage of the belief propagation technique for making task-offloading decisions. The effectiveness of the proposed strategy is also evaluated through extensive experimental study, providing robust evidence to support the validity and applicability of our approach.