Energy-efficient and QoS-aware clustering routing framework using improved sharpbelly fish optimization and adaptive regularized routing optimization for WSNs

Wireless sensor networks (WSNs) face persistent challenges in maintaining energy efficiency, balanced resource utilization, and reliable data transmission as network density increases. Existing clustering and routing approaches generally address these requirements independently, resulting in inadequate coordination among cluster-head selection, node grouping, routing, and cooperative resource allocation. To address this research gap, this study proposes an integrated Energy-Efficient and QoS-Aware Clustering Routing Framework that jointly optimizes these network operations. The framework employs the Improved Sharpbelly Fish Optimization Algorithm (ISFOA) for cluster-head selection by considering residual energy, node density, communication distance, and trust characteristics. Hyperbolic Fuzzy C-Means with Adaptive Weight-based Filtering (Hyper-FCM) is subsequently employed to establish robust and adaptive clusters while reducing the influence of uncertain or unreliable node information. The Adaptive Regularized Routing Optimization Algorithm (ARROA) determines energy-efficient multi-hop routes by balancing routing load, residual energy, and end-to-end delay. In addition, the Shapley-driven N-Agent Teamwork (SNAT) framework promotes cooperative resource utilization and balanced participation among sensor nodes. MATLAB-based simulations were conducted for network sizes ranging from 100 to 500 nodes. In the densely populated 500-node scenario, the proposed framework achieved 16.0 mJ energy consumption, 97.5% packet delivery ratio, and 0.915 Mbps throughput, demonstrating controlled performance degradation with increasing network density. The results indicate that the integrated optimization of clustering, routing, and cooperative decision-making improves energy utilization and QoS while supporting scalable and reliable WSN operation.