Modified Leach Ant Colony Optimization Hybrid Algorithm for Energy-Harvesting Wireless Sensor Network

Wireless Sensor Networks (WSNs) comprise a multitude of sensor nodes distributed in a random fashion across the designated area. These nodes are responsible for monitoring and recording physical data from their surroundings. Subsequently, they transmit this collected information to a central base station using wireless communication. Two critical applications of WSNs include biomedical and environmental monitoring. However, in challenging environments, replacing the sensor node's battery is nearly impractical once the sensor node's battery is fully depleted. Consequently, maintaining the network's lifespan and ensuring reliable communication between nodes in such harsh conditions pose significant challenges for researchers. This research aims to enhance both the survivability of the network and its Network Throughput (NTh). To achieve this, a simplified energy harvesting model is employed to enhance the network's ability to survive in challenging conditions. This paper introduces a novel hybrid scheme for selecting Cluster Heads (CH) in energy harvesting WSNs, which is termed Modified Leach Ant Colony Optimization (MLACO). This scheme not only enhances the Network Throughput but also improves the network's overall survivability. Comparative analysis against existing clustering methods like MLEACH and FEEDC reveals that the proposed MLACO scheme outperforms both of them. On average, MLACO exhibits a 31.5% higher network throughput compared to MLEACH and a 26.6% improvement over the FEEDC CH selection strategy. Moreover, the network survivability demonstrated by the proposed scheme is consistent with that of established methods and surpasses them, particularly between 900 and 1300 rounds.