Enhancing Quality of Service in IoT-WSN through Edge-Enabled Multi-Objective Optimization

Singh, Shailendra Pratap ORCID: https://orcid.org/0000-0002-2153-9641, Kumar, Naween ORCID: https://orcid.org/0000-0001-7062-0131, Kumar, Gyanendra ORCID: https://orcid.org/0000-0002-0791-3094, Balusamy, Balamurugan ORCID: https://orcid.org/0000-0003-2805-4951, Bashir, Ali Kashif ORCID: https://orcid.org/0000-0001-7595-2522 and Dabel, Maryam M. Al ORCID: https://orcid.org/0000-0003-4371-8939 (2025) Enhancing Quality of Service in IoT-WSN through Edge-Enabled Multi-Objective Optimization. IEEE Transactions on Consumer Electronics. ISSN 0098-3063

Preview

Accepted Version Available under License In Copyright. Download (607kB) | Preview

Abstract

The demand for real-time, high-quality services (QoS) is increasing with the proliferation of the resource-constrained nature of edge devices that facilitate the Internet of Things (IoT) and wireless sensor network (WSN) applications. Several existing multi-objective algorithms, such as MOPSO, Elitism MOGA, MODE, and others, are capable of balancing exploration and exploitation; they assist in efficient QoS management for WSN-IoT applications, address resource limitations, and align with the objectives of the applications. However, they suffer from showing robustness in the solution and efficient convergence rates on benchmark functions impacting overall QoS. This paper proposes a multi-objective optimization and edge-intelligent adaptation-based strategy to address QoS management issues, jointly optimize several competing objectives, like energy and latency, and maximize localization and coverage rates while considering the limitations of edge devices. The proposed work uses a novel Grey-wolf optimizer (GWO) Algorithm with an innovative bird-edge-computing adaptation approach to analyze the complex connections between input parameters, edge resources, and QoS indicators to generate Pareto-optimal solutions. The evaluation of the proposed edge intelligence technique with IoT applications demonstrates its effectiveness compared to conventional heuristic-based approaches. This approach enhances the QoS in IoT applications and improves resource utilization and scalability in edge computing environments.