Fuzzy Trust‐Based Hybrid Levy Snake Optimization for Secure and Reliable Wireless Sensor Networks

ABSTRACT Wireless sensor networks (WSN) include numerous sensor nodes deployed to collect data efficiently. Energy constraints and unreliable communication remain critical challenges in WSNs. This research proposes a Fuzzy Trust‐based Hybrid Levy Snake Optimization model to improve energy efficiency, scalability, and reliability in WSNs. The Fuzzy Trust‐based Hybrid Levy Snake Optimization model includes cluster formation, optimal cluster head selection using combined levy flight and snake optimization, and a fuzzy trust mechanism to enhance data security and minimize energy consumption. The algorithm identifies secure and energy‐efficient routing paths by evaluating trust factors and optimizing cluster head selection based on parameters like energy and delay. Extensive experiments validate the Fuzzy Trust‐based Hybrid Levy Snake Optimization model, demonstrating superior performance compared to traditional approaches. The Fuzzy Trust‐based Hybrid Levy Snake Optimization model achieves a packet delivery ratio of 98.5%, throughput of 98.7%, and network lifetime of 9500 ms while reducing energy consumption to 83 J and end‐to‐end delay to 0.01 ms. These findings highlight the Fuzzy Trust‐based Hybrid Levy Snake Optimization model's effectiveness in addressing energy and trust challenges in WSNs, offering a robust solution for secure and efficient data transmission.