A Novel Framework for Energy‐Efficient Routing and Robust Cluster Head Selection in Wireless Sensor Networks Utilizing Deep Kronecker Neural Networks and Hybrid Metaheuristic Optimization

Wireless Sensor Networks (WSNs) affected by rapid energy depletion, unstable cluster head (CH) rotation, and unreliable routing in large‐scale involvement. To address these challenges, this paper proposes a novel model of Energy‐Efficient Routing and Robust Cluster Head Selection in WSNs utilizing Deep Kronecker Neural Networks and Hybrid Metaheuristic Optimization (DKNN‐HybMBGOQAHA‐WSN). The proposed approach integrates Data Aggregation via Deep Kronecker Neural Network (DKNN) for optimal CH selection and hybrid Multiplayer Battle Game Optimizer and Quantum Artificial Hummingbird Algorithm (HybMBGO‐QAHA) for secure and efficient routing. CH selection is performed via multiobjective fitness parameters such as energy efficiency, delay, throughput, internode distance, traffic rate, and cluster density. The routing phase optimizes trust, connectivity, and Quality of Service (QoS) to enable reliable data transmission and mitigate malicious nodes. Performance evaluation is validated via Network Lifetime, Packet Delivery Ratio, Delay, Overhead, Computational Time, and Alive Nodes. Simulation results indicate that the proposed DKNN‐HybMBGOQAHA‐WSN model enhances Network Lifetime up to 32.21%, improves Packet Delivery Ratio by 29.90%, and significantly lowers computational overhead compared to existing WSN routing techniques. The results confirm the robustness, scalability, and long‐term energy sustainability of the proposed model for large‐scale IoT‐assisted WSN applications.