An Energy‐Efficient and Delay‐Sensitive Routing for Mobile Wireless Sensor Networks Using an Optimized Deep‐Learning Network

Traditional Mobile Wireless Sensor Networks (MWSNs) often use mobile sinks to mitigate issues like energy holes. However, mobile sinks introduce new challenges, such as significant delays and buffer overflows due to fixed trajectories and variable moving speeds. These issues are particularly problematic for delay‐sensitive applications, as most existing researches either focus on delay‐tolerant scenarios or rely on energy‐intensive greedy data collection methods. This research proposes an improved deep learning model with STGRN for delay‐sensitive, energy‐efficient routing and mobile sink prediction in MWSNs. STGRN‐HRFO addresses mobility challenges in MWSNs through optimized routing, energy‐efficient transmission, adaptive resource allocation, predictive mobility models, dynamic topology adjustments, and machine learning, enhancing stability, reducing energy consumption, and improving performance. Based on the projections from STGRN, an effective cluster‐based routing system is implemented. A Hybrid Red‐billed Frilled lizard magpie Optimizer (HRFO) is introduced to optimize the selection of cluster heads and improve routing efficiency. Significant gains are made using the STGRN‐HRFO framework, which reduces the end‐to‐end path's hop count to 0.2 s, minimizes network energy usage to 3.6 J, and boosts throughput to 90%. Additionally, the energy consumed per packet is minimized to 2.2 mJ. Comparative analysis demonstrates that the STGRN‐HRFO protocol effectively enhances network performance, ensuring low latency, high packet delivery ratios, and efficient energy use, particularly in real‐world scenarios with complex optimization needs.