Open Access

ARTICLE

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

Dinesh Mavaluru^1,*, Chettupally Anil Carie², Ahmed I. Alutaibi³, Satish Anamalamudi², Bayapa Reddy Narapureddy⁴, Murali Krishna Enduri², Md Ezaz Ahmed¹

1 School of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia
2 Department of Computer Engineering, SRM University AP, Guntur, India
3 College of Computer and Information Sciences, Majmaah University, Al Majma’ah, Saudi Arabia
4 Department of Public Health, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia

* Corresponding Author: Dinesh Mavaluru. Email:

(This article belongs to the Special Issue: Smart and Secure Solutions for Medical Industry)

Computer Modeling in Engineering & Sciences 2024, 139(2), 1487-1503. https://doi.org/10.32604/cmes.2023.045277

Received 22 August 2023; Accepted 10 November 2023; Issue published 29 January 2024

Abstract

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 resource-efficient 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 Q_n^O. Moreover, the algorithm effectively balances complexity and network performance, as demonstrated when reducing the number of devices in each group (N_g) 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.

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Keywords

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