IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems

In this paper, we present a comprehensive system model for Industrial Internet of Things (IIoT) networks empowered by Non-Orthogonal Multiple Access (NOMA) and Mobile Edge Computing (MEC) technologies. The network comprises essential components such as base stations, edge servers, and numerous IIoT devices characterized by limited energy and computing capacities. The central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy consumption. The system operates in discrete time slots and employs a quasi-static approach, with a specific focus on the complexities of task partitioning and the management of constrained resources within the IIoT context. This study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation, particularly relevant in real-time industrial applications. Experimental results indicate that our proposed algorithm significantly outperforms existing approaches, reducing queue backlog by 45.32% and 17.25% compared to SMRA and ACRA while achieving a 27.31% and 74.12% improvement in QnO. Moreover, the algorithm effectively balances complexity and network performance, as demonstrated when reducing the number of devices in each group (Ng) from 200 to 50, resulting in a 97.21% reduction in complexity with only a 7.35% increase in energy consumption. This research offers a practical solution for optimizing IIoT networks in real-time industrial settings.