Mobility‐Aware Lévy‐Enhanced Adaptive Squirrel Optimisation Framework With Improved Double Q‐Learning for Energy‐Efficient Dynamic Wireless Sensor Networks

Wireless sensor networks (WSNs) operating in challenging, resource‐constrained dynamic environments often struggle to address the persistent issues related to energy efficiency, node mobility, network coverage and performance. To overcome these research challenges, an innovative hybrid optimisation framework is proposed. This proposed framework effectively integrates squirrel search optimisation (SSO) with adaptive Lévy flights for enhancing the balance between the exploration‐exploitation process, and enhanced double Q‐learning for adaptive energy‐aware routing. In addition, a long short‐term memory (LSTM)‐based mobility‐aware node prediction model enables proactive cluster adaptation and a residual energy‐based cluster head (CH) selection process to improve reliability, convergence speed and energy usage. To ensure a uniform workload among sensor nodes, the proposed algorithm incorporates adaptive data aggregation and task‐aware load distribution, which minimises the possibility of redundant transmissions and enhances the operational lifespan of nodes under varying node densities. Simulation results across diverse scenarios confirm the effectiveness of our hybrid scheme in terms of performance improvements achieved across various performance evaluation metrics, including a 14.4% improvement in residual energy, a 11.7% improvement in coverage retention, a 18.71% improvement in cluster stability, a 13.42% enhancement in load balancing efficiency, a 4.5% improvement in scalability, a 11.51% enhancement in QoS reliability and a 61% reduction in complexity overhead across various node counts. Additionally, the proposed framework maintains superior network stability and outstanding packet delivery reliability under varying node densities when validated with state‐of‐the‐art algorithms. These capabilities make our hybrid framework a reliable solution for diverse WSN applications where adaptability and resource efficiency are critical priorities.