A Chaotic Elite Cloning Artificial Jellyfish Algorithm for Efficient Task Allocation in IOTWSNs

Abstract

The Internet of Things wireless sensor networks (IOTWSNs) are crucial in modern smart systems, where self-organizing sensor nodes enable efficient and flexible network structures for applications like environmental monitoring and smart cities. The task allocation problem in IOTWSNs is NP-hard, making effective strategies essential for optimal network performance. This article proposes an improved artificial jellyfish search algorithm (CECJS) that integrates chaotic initialization, elite, and cloning strategies to enhance global search ability and convergence speed. To evaluate CECJS’s efficiency, the article introduces network gain, reflecting both network effectiveness and task completion quality. Experimental results show that CECJS significantly outperforms traditional algorithms like genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) in task allocation gains, achieving improvements of several to tens of percentage points. In addition, CECJS exhibits faster convergence, finding near-optimal solutions more efficiently, making it an effective solution for large-scale IOTWSNs task optimization.