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Industry 5.0 aims to elevate industrial operations, businesses, and revolution to new heights by promoting sustainable, resilient, and human-centric practices. The popularity of Industry 5.0 is reflected in the increasing demand for real-time and near-edge processing in most latency-critical Industrial Internet of Things (IIoT) applications. However, designing an efficient task priority assignment strategy and accordingly executing tasks within the stipulated deadline is complex and challenging. Therefore, in this work, we design a novel Multi-device Edge Service Provisioning (MESP) framework for optimizing delay in Industry 5.0. At first, the MESP strategy classifies edge executable tasks using multi-nomial probability theory. Then, we prove that multi-device service demand at the edge devices is an NP-Hard problem, which requires approximate algorithms for finding near-optimal solutions. To follow this, we propose a game-theoretic approach where multiple IIoT devices request various services simultaneously while maximizing their mutual satisfaction. We also examine the structural property of the proposed game and show how this property helps in achieving the equilibrium point of the proposed game with finite improvement steps. Experimental analysis shows that MESP reduces computational overhead and end-to-end execution delay by 20-30% compared to standard algorithms.
Hazra et al. (Thu,) studied this question.