OPTIMIZING ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS THROUGH SINE COSINE ALGORITHM-BASED CLUSTER HEAD SELECTION AND FUZZY LOGIC-ENHANCED DATA AGGREGATION

Abstract

Wireless Sensor Networks (WSNs) have become indispensable in various critical applications, such as environmental monitoring and healthcare, due to their capability to efficiently collect and transmit data. However, optimizing energy consumption remains a significant challenge, particularly in large-scale and heterogeneous networks. This paper proposes an enhanced Distributed Energy-Efficient Clustering (DDEEC) protocol that integrates the Sine Cosine Algorithm (SCA) for optimized cluster head selection and fuzzy logic for improved data aggregation. The hybrid approach addresses the energy imbalance issues inherent in existing methods. Simulation results demonstrate that the DDEEC protocol increases network lifetime by 66.67%, achieving 750 rounds before the first node energy depletion, compared to 450 rounds for the traditional DDEEC protocol. Additionally, the throughput is enhanced by 28.57%, reaching 4500 packets, compared to 3500 packets for DDEEC, while energy consumption is reduced by 33.33%, lowering to 100 Joules compared to 150 Joules for DDEEC. These improvements indicate that the proposed methodology significantly enhances energy efficiency, extends network lifetime, and improves data accuracy, offering a promising solution for large-scale, heterogeneous WSN deployments.