Efficient Load Balanced Routing based Trusted Clustering to Maximizing Network Lifetime in Heterogeneous Wireless Sensor Networks using Distributed Compressive Sensing Technique

Abstract

Wireless Sensor Networks (WSNs) face significant challenges in energy efficiency, load balancing, and data transmission. To address these, we propose the Efficient Load Balanced Routing based Trusted Clustering to Maximize Network Lifetime using Distributed Compressive Sensing (ELRC-DCS) approach. This method employs a hierarchical network model with sensors organized in a bi-layer and a static data sink positioned centrally. Optimized Cluster Head (CH) selection ensures balanced power consumption, enhanced load distribution, and energy efficiency by avoiding low-energy nodes and traffic congestion. The model incorporates a Distributed Compressive Sensing Technique (DCST) to reduce energy consumption and simplify data transmission, while a trust mechanism ensures secure and reliable node-to-node communication. This trust mechanism evaluates direct, indirect, and new trust values to improve data reliability and prevent malicious behavior. The ELRC-DCS approach optimizes clustering, balances energy usage, minimizes communication expenses, and guarantees reliable data transmission, resulting in enhanced network durability. Simulation results demonstrate that ELRC-DCS significantly outperforms existing models (CACIACA, OCCMPHE, and EMRHPFC), achieving the lowest communication delay (108.30 ms) and the highest energy efficiency (93.45%). Additionally, ELRC-DCS achieves a data success rate of 91.64%, network throughput of 784.26 Kbps, and reduced routing overhead with only 898 packets. These results position ELRC-DCS as a robust and efficient solution for large-scale WSN deployments in environmental monitoring applications, offering superior energy efficiency, reliability, and communication quality compared to traditional methods.