Intelligent Cluster Head Selection for Energy-Efficient Wireless Sensor Networks: An MLP-Based Approach

Energy conservation is a critical concern in Wireless Sensor Networks due to their vital role in providing essential sensing services across diverse applications.Clustering algorithms have been recognized as key drivers for power savings in energy-awareness networks, particularly in stabilizing the network load, reducing resource consumption, and enhancing the network’s availability period. In this paper, we propose a novel intelligent cluster head selection platform that leverages the Multi-layer Perceptron (MLP) formalism. The main idea is to enable the platform to learn from the dynamic network conditions and intelligently alternate the cluster head position between nodes with optimal energy values. By adapting MLP models to learn and extract from running network’s parameters, we design an optimized cluster head selection process that efficiently utilizes available energy resources in the network. The proposed intelligent routing protocol not only significantly reduces overall power consumption in the network but also ensures optimal communication delay in WSNs. Through extensive simulations, we demonstrate that our modified version outperforms the classical LEACH protocol conventions and PEGASIS in terms of network lifetime, energy efficiency, throughput, delay, and packet delivery ratio. These results highlight the promising potential of the proposed intelligent cluster head selection platform in achieving energy awareness and enhancing the performance of Wireless Sensor Networks, contributing to their broader applicability and sustainability.