Industry 5.0: An Energy-Efficient Smart Task Offloading Mechanism for Multi-Access Edge Computing

Saba Saddique; Arslan Baig; Erssa Arif; Mudasir Zaheer; Shahrukh Hamayoun; Fizza Khan

Authors

Saba Saddique Riphah College of Computing (Riphah International University, Faisalabad, Pakistan)
Arslan Baig Riphah College of Computing (Riphah International University, Faisalabad, Pakistan)
Erssa Arif Riphah College of Computing (Riphah International University, Faisalabad, Pakistan)
Mudasir Zaheer Riphah International University, Faisalabad, Pakistan
Shahrukh Hamayoun Department of Computer Science (National University of Modern Languages, Faisalabad, Pakistan)
Fizza Khan Department of Computer Science (National University of Modern Languages, Faisalabad, Pakistan)

Keywords:

Genetic Algorithm, Industrial Internet of Things, Industry 5.0, Latency Optimization, Multi-Access Edge Computing (MEC), Particle Swarm Optimization, Task Offloading

Abstract

The industry 5.0 heralds a transformation of industrial systems by integrating artificial intelligence (AI), the Industrial Internet of Things (IIoT), and Multi-Access Edge Computing (MEC) to foster resilience, efficiency, and sustainability. However, managing the massive volume of computation-intensive tasks generated by heterogeneous IIoT devices presents major challenges, particularly in optimizing both latency and energy consumption under dynamic industrial conditions. This research proposes a hybrid task offloading framework Computational Genetic Particle Swarm Optimization Algorithm (CGPCA) to intelligently balance energy efficiency and latency in MEC-enabled IIoT networks. CGPCA integrates the global search capability of Genetic Algorithms (GA) with the fast convergence of Particle Swarm Optimization (PSO), forming a two-layer optimization approach for effective task-device associations and power-bandwidth allocation. The framework is evaluated using iFogSim and Edgelands simulation environments, reflecting realistic industrial scenarios with variable workloads, device capabilities, and server conditions. Results indicate that CGPCA reduces average latency by up to 24%, lowers energy consumption by 18–25%, and maintains a task offloading success rate of 94% surpassing conventional GA, PSO, and heuristic baselines. The framework also achieves improved load balancing and faster convergence time, confirming its suitability for time-sensitive and energy-constrained IIoT environments. This study contributes to the realization of Industry 5.0 by offering an adaptive, intelligent solution that enhances computational efficiency while supporting sustainable and human-centered industrial automation. Future directions include extending CGPCA to highly mobile IIoT contexts and integrating predictive analytics for further performance gains.

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