A Chaotic Reptile Search Algorithm for Energy Consumption Optimization in Wireless Sensor Networks

A wireless sensor network (WSN) relies on sensor nodes equipped with integrated processors to monitor and manage environmental conditions within a designated area. These nodes communicate with a base station (BS), which acts as the central processing unit of the entire system. WSNs face several challenges, including limited processing capability and short network lifetimes. To address the issue of network longevity, we propose a hybrid meta-heuristic (MH) optimization method called the Chaotic Reptile Search Algorithm (CRSA) for energy-efficient WSN operation. CRSA enhances the original Reptile Search Algorithm (RSA) by integrating chaotic maps. While RSA—like many meta-heuristic algorithms—can become trapped in local minima, the use of chaotic maps increases solution diversity and mitigates this issue. In WSNs, selecting optimal cluster heads (CHs) is crucial for conserving energy. The proposed CRSA identifies the most efficient set of cluster heads among the sensor nodes across the sensing field. We evaluate the performance of CRSA under various conditions and compare it with five different meta-heuristic algorithms and three variants of the Low-Energy Adaptive Clustering Hierarchy (LEACH) algorithm. Experimental results demonstrate that CRSA outperforms these existing methods in terms of total energy consumption, number of active nodes, packet reception at the base station, and overall network lifetime. These findings indicate that CRSA is a promising approach that achieves notable improvements over previously studied algorithms.

Keywords: Cluster Head Selection, Energy Consumption Optimization, Meta-heuristic (MH) Algorithms, Network Lifetime, Reptile Search Algorithm (RSA), Wireless Sensor Network (WSN).