Celestial swarm optimization for energy-efficient task offloading in mobile edge-enabled IoT networks

초록

In mobile edge computing (MEC)-enabled Internet of Things (IoT) networks, optimizing task offloading is critical for improving energy efficiency, latency, and resource use in dynamic, resource-limited environments. This paper presents a novel artificial intelligence-based optimization algorithm, Celestial Swarm Optimization (CSO), inspired by gravitational dynamics for efficient task allocation in MEC-enabled IoT networks. Unlike traditional swarm intelligence methods, CSO integrates dual-attraction forces: global best guidance and a dynamically computed centre of mass, to balance exploration and exploitation adaptively. The proposed method decides, in real time, whether to offload tasks by checking device constraints and the state of the network, so it can jointly reduce latency, energy, and memory usage. Simulation results show that CSO surpasses standard techniques, including PSO, GA, and GWO, reaching as much as 21% energy savings, 17% less latency, and 22% better memory efficiency. Therefore, CSO appears to be a robust and scalable solution for intelligent task scheduling in IoT environments with strict energy budgets and heavy computation needs.

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