Multi‐Objective Energy‐Efficient Clustering Protocol for Wireless Sensor Networks: An Approach Based on Metaheuristic Algorithms
MohamadHosein. Behzadi, Homayun Motameni, Hosein Mohamadi, Behnam Barzegar · IET Wireless Sensor Systems · 2025
ABSTRACT Efficient resource management remains a critical challenge in wireless sensor networks (WSNs) due to the constrained nature of sensor nodes. This paper proposes a novel hybrid clustering protocol to address this issue, aiming to optimise energy consumption, extend network lifetime and enhance scalability. Our approach combines the improved version of binary dragonfly algorithm (IVBDA) for cluster head (CH) selection and the Mamdani fuzzy inference system for effective cluster formation. After CH selection and cluster formation, a multi‐hop routing mechanism transmits data packets within the WSN. To validate the performance of the proposed protocol, extensive simulations are conducted on various network topologies, evaluating metrics such as average energy consumption, live node count, network lifetime, and packet reception at the base station (BS). Comparative analyses with existing clustering protocols and other metaheuristic algorithms, including binary particle swarm optimisation (BPSO), binary whale optimisation algorithm (BWOA) and binary dragonfly algorithm (BDA), demonstrate the superior performance of the proposed hybrid approach in terms of energy efficiency, network longevity and overall WSN performance. The improved version of BDA shows faster convergence than BPSO, BWOA and BDA, as ascertained by examining the multi‐objective fitness function. This paper contributes significantly to the development of efficient clustering protocols and showcases the potential of hybrid metaheuristic and fuzzy inference techniques for optimising resource allocation in WSNs. The proposed protocol outperforms other protocols in network lifetime and overall performance, indicating its potential to be a valuable solution for resource management in WSNs. The evaluation of metaheuristic algorithms highlights the importance of considering convergence speed in optimising energy‐efficient clustering.