Enhanced Energy-Efficient Routing in WSNs: A Multipath Approach Using Binary Gray Wolf Optimization and Sugeno Fuzzy Logic

Wireless Sensor Networks (WSNs) are pivotal in enabling real-time Internet of Things (IoT) applications through sensor nodes that communicate both with each other and their environment. Despite their potential, WSNs face significant challenges due to limited energy resources, which are crucial for sustaining long-term operations. One of the most pressing issues is unbalanced energy utilization during data packet routing, which can lead to premature network failure. This paper addresses these challenges by introducing an Enhanced Zone Stable Election Multipath Routing Protocol that leverages Binary Gray Wolf Optimization (BGWO) and Sugeno Fuzzy Logic. The proposed protocol aims to enhance energy efficiency, load balancing, and network scalability through an optimized clustering and routing approach. By employing multipath routing, the protocol creates multiple paths between source and destination, thereby improving data delivery probability and network resilience. Simulation results demonstrate that our approach outperforms existing methods, such as LEACH and DFLC, in terms of packet delivery ratio, energy consumption and network life time. The findings suggest that the proposed solution significantly extends network lifetime and improves overall performance, offering a robust alternative to conventional WSN routing strategies.