A Novel Fuzzy Cluster Head Selection and Adaptive Hawk Optimisation Routing Framework for Energy Efficient Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have revolutionized various industries by enabling remote monitoring and data collection from inaccessible locations.However, they face significant challenges in data routing and cluster head selection due to limitations such as energy constraints, bandwidth, and dynamic environments.These challenges arise as nodes may fail due to energy depletion, causing changes in network topology.Efficient communication between cluster heads and the base station is essential, as long-distance data transmission consumes substantial energy, leading to rapid node depletion and reduced network lifetime.To address these issues, this paper proposes a novel Fuzzy-based Cluster Head Selection (FCHS) algorithm that dynamically elects cluster heads based on multiple criteria, including residual energy, distance to the base station, and node density.The FCHS algorithm ensures that cluster heads are selected in a manner that optimizes energy utilization, considering the diverse conditions in a WSN.In addition, an Adaptive Hawk Optimization (AHO) algorithm is employed to optimize data routing between cluster heads and the base station.The AHO algorithm dynamically adapts to changes in network topology, selecting the most energy-efficient paths for data transmission, thereby reducing overall energy consumption.The proposed FCHS-AHO approach is evaluated against existing cluster head selection and routing methods.Simulation results demonstrate that the FCHS-AHO method outperforms traditional techniques in terms of energy efficiency, network lifetime, and data delivery reliability.By optimizing both cluster head selection and routing, this framework significantly enhances the overall performance of WSNs, particularly in dynamic and resource-constrained environments.The proposed method provides a robust solution for improving the sustainability and reliability of WSNs in real-world applications.