Hybrid Whale and Artificial Rabbit Optimization for Efficient Multi‑Objective Sensor Deployment in Complex IoT Networks

Abstract

This paper presents a novel hybrid metaheuristic algorithm, combining Whale Optimization Algorithm (WOA) and Artificial Rabbits Optimization (ARO), to solve the multi-objective sensor node placement problem in dynamic and obstacle-rich Internet of Things (IoT) environments. The proposed WOA-ARO algorithm aims to maximize coverage, minimize energy consumption, and reduce redundancy while maintaining robust network connectivity. Leveraging WOA’s strong global search capabilities alongside ARO’s efficient local refinement, the hybrid method balances exploration and exploitation effectively. Extensive simulations conducted on real-world maps with 50 sensor nodes demonstrate that WOA-ARO achieves an average coverage rate of 95.00% with a remaining energy of 88.31%, outperforming competing algorithms such as EFFSA, MAOA, and GA-PSO. Additionally, WOA-ARO achieves the lowest redundancy value of 1.2142, indicating efficient resource utilization. Although its runtime is marginally higher than some methods, the superior solution quality and energy efficiency affirm WOA-ARO as a highly effective approach for optimal sensor deployment in complex IoT scenarios.