Optimizing Energy Efficiency in Wireless Sensor Networks through Adaptive Neuro-Fuzzy Inference-Based Energy Harvesting

Wireless Sensor Networks (WSNs) are essential in various applications, including healthcare, environmental monitoring, and surveillance. Energy efficiency is a major concern across all communication areas, including WSNs, where the limited power resources of sensor nodes make it particularly crucial. The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol employs a hierarchical approach to enhance energy efficiency by organizing sensor nodes into clusters and periodically rotating the role of cluster heads, thereby ensuring a more balanced distribution of energy consumption. However, traditional LEACH still needs to improve its ability to maximize energy harvesting from the environment. This paper introduces a novel simulationbased approach in adaptive neuro-fuzzy inference with an energyharvesting enhancement to the LEACH (EHANFIS-LEACH) protocol in wireless sensor networks. The simulation method utilizes advanced modeling techniques to evaluate the impact of various parameters on energy consumption, network longevity, and data transmission rates. By taking into account factors such as node deployment density, sensing range, and energy harvesting capabilities, the proposed simulation method offers valuable insights for optimizing the performance of LEACH-based networks. The Simulation results indicate that the proposed method performs better than the conventional LEACH, OLD FUZZY, Multi-Criteria Cluster Head Delegation based on Fuzzy Logic (MUCH), and Adaptive Neuro-Fuzzy Inference System (ANFIS LEACH) methods in terms of nodes alive, throughput, and dissipated energy.