The rapid deployment of Wireless Sensor Networks (WSNs) faces critical challenges due to sensor nodes’ limited energy and communication capabilities, which restrict network lifetime and data transmission efficiency. Traditional clustering and routing protocols often lead to unbalanced energy consumption and uneven load distribution, whereas intelligent optimization approaches are hindered by high computational costs and slow convergence. This research formulates the clustering and routing problems in WSNs as an optimization challenge under resource and energy constraints, aiming to improve stability, energy efficiency, and throughput. This research proposed three quantum optimization-based solutions to address complex issues. First, a Quantum Genetic-Enhanced K-means (QGE-K) protocol addresses inaccurate cluster-head initialization by adaptively determining the optimal number of clusters and selecting energy-balanced cluster heads, thereby improving clustering accuracy and routing efficiency. Second, a Fuzzy-Enhanced Quantum Annealing Algorithm (FEQA) protocol integrates fuzzy inference with quantum tunneling dynamics to select cluster heads and compute the most energy-efficient routing paths, extending the network lifetime in large-scale deployments. Third, a Quantum-Enhanced Particle Swarm Clustering and Routing (QE-PSCR) protocol encodes clustering and routing into a single optimization particle, employing chaotic mapping and Lévy flight strategies to accelerate convergence and escape local optima, thereby reducing computation overhead. The simulation results demonstrate that all three protocols achieve significant improvements in energy consumption, load balance, throughput, and overall network lifetime. The proposed methods apply to domains such as environmental monitoring, the industrial Internet of Things, and military security, highlighting both theoretical contributions and practical value in advancing energy-efficient WSN design.
Rehman et al. (Thu,) studied this question.