Unequal Clustering and Routing Optimization in Wireless Sensor Networks Using Bonobo Optimizer: Maximizing Energy Efficiency and Network Lifetime

ABSTRACT Wireless sensor networks (WSNs) are commonly utilized in various application areas, ranging from environmental monitoring to industrial automation. In WSNs, energy efficiency (EE) is a crucial feature due to sensor nodes' (SNs) limited power resources. Unequal clustering (UC) and routing techniques have emerged as effective approaches to balance energy consumption (ECON) and prolong network lifetime (NLT). However, finding optimal solutions for UC and routing optimization problems in large‐scale WSNs remains challenging. Recently, metaheuristic algorithms have been widely employed to determine the appropriate cluster sizes, cluster heads (CHs), and routing paths that minimize ECON imbalances while maximizing NLT or throughput using various parameters and objectives. Therefore, this study proposes a new bonobo optimization algorithm based on UC with a pelican optimization‐based routing (BOAUC‐POR) technique for energy‐efficient WSNs. The projected BOAUC‐POR technique involves two significant phases: UC construction and routing. In the preliminary phase, the BOAUC‐POR technique uses the BOAUC technique to explore the search space effectively for optimal CH selection and cluster size, considering factors like node density, residual energy (RE), and distance. Besides, the BOAUC‐POR technique follows the POR model for the optimum selection of routes to BS using RE, node degree (ND), and distance. The proposed BOAUC‐POR technique effectively maximizes the EE and NLT in WSN. The simulation values stated that the BOAUC‐POR technique accomplishes superior performance in terms of EE and NLT. The BOAUC‐POR technique exhibits promising results for enhancing the EE and prolonging the lifetime of WSNs, thus enabling sustainable and reliable operation in resource‐constrained environments. The comparison study of the BOAUC‐POR technique portrayed superior values of 510 for HND in the 500‐node scenario and 124 s for CT at 700 rounds.