QoS-Aware Energy-Efficient Routing in Wireless Sensor Networks via Multi-Objective Differential Evolution

Wireless Sensor Networks (WSNs) are integral to diverse applications, ranging from environmental monitoring to healthcare systems. However, inherent battery capacity constraints dictate that energy efficiency remains a paramount design criterion. Fulfilling the strict Quality of Service (QoS) demands of modern applications—such as low latency and high reliability—often conflicts with energy conservation objectives. Consequently, unequal energy dissipation among sensor nodes, particularly in the vicinity of the Base Station (BS), frequently induces the critical “energy hole” problem, thereby severely degrading network lifetime. To mitigate these challenges, this paper proposes a novel QoS-aware and energy-efficient routing protocol utilizing a Multi-Objective Differential Evolution (MODE) algorithm. The primary innovation involves optimizing the clustering phase through the optimal selection of Cluster Heads (CHs) to ensure network load balancing. Specifically, the framework formulates a multi-objective fitness function that concurrently optimizes three critical decision variables: the residual energy of candidate nodes, the intra-cluster spatial compactness, and the communication distance from CHs to the BS. By exploiting the evolutionary operators of mutation, crossover, and selection, the proposed MODE dynamically determines near-optimal routing paths that satisfy QoS constraints while minimizing overall energy expenditure. Extensive MATLAB simulations validate the protocol’s efficacy. Experimental outcomes demonstrate that the MODE-based architecture significantly outperforms conventional routing paradigms, achieving a substantial extension in network lifetime, a superior Packet Delivery Ratio (PDR), and minimized end-to-end delay. Ultimately, this research validates the algorithm’s robustness and efficiency in maintaining a strict balance between energy conservation and QoS demands within resource-constrained WSNs. A novel QoS-aware routing protocol using Multi-Objective Differential Evolution is proposed. Cluster Head selection is optimized to mitigate the energy hole problem in WSNs. The fitness function integrates residual energy, distance, and link quality metrics. The MODE approach significantly enhances network lifetime, PDR, and end-to-end delay.