Innovative Hybrid Framework for Routing and Clustering in Wireless Sensor Networks with Quantum Optimization

Wireless Sensor Networks are vital for applications like environmental monitoring, smart cities, and industrial Internet of Things, yet their efficiency is often limited by sensor node energy constraints. To address this, we propose a novel hybrid routing algorithm namely, Fuzzy C-Means - Fuzzy-Based Spotted Hyena Optimization - Quantum Annealing, which enhances energy efficiency and network longevity. The three-phase approach begins with adaptive fuzzy clustering, allowing nodes to belong to multiple clusters for improved load balancing. Next, the Fuzzy-Based Spotted Hyena Optimization algorithm selects initial routes using parameters such as residual energy, distance to the base station, and link quality. Finally, Quantum Annealing refines routing paths to ensure global optimization and avoid local optima. This hybrid method adapts dynamically to changing network conditions and energy levels, delivering superior scalability and performance. Experimental results show a 30% reduction in total energy consumption and a 25% increase in effective data transmission. The algorithm achieves a packet delivery ratio of 98.87%, throughput of 65 kbps, and latency of 115 ms, outperforming conventional techniques. This work contributes a robust and adaptive routing solution, supporting sustainable and efficient WSN deployments in dynamic environments.