Optimizing energy-efficient routing in Mobile Internet of Things (MIoT) networks using Grey Wolf Optimization and Recurrent Neural Networks

The Mobile Internet of Things (MIoT) represents a significant evolution of traditional IoT by enabling seamless connectivity for mobile devices and sensors in dynamic environments. Given the resource constraints and mobility challenges in MIoT networks, developing adaptive and energy-efficient routing strategies is important. This paper proposes a novel routing protocol that integrates Grey Wolf Optimization (GWO) and Recurrent Neural Networks (RNNs) to enhance energy efficiency, reliability, and responsiveness in MIoT systems. The protocol features dynamic clustering, predictive traffic load balancing, and multi-objective optimization for Cluster Head (CH) selection, where RNNs forecast traffic trends and GWO optimizes routing paths. Simulation results demonstrate that the proposed method reduces energy consumption, lowers end-to-end delays, and improves packet delivery ratio (PDR) and network reliability under both static and mobile conditions. Compared to existing methods such as the Krill Herd (KH) algorithm, Dynamic Multi-Sink Routing Protocol (DMS-RP), and Evolutionary Fuzzy Rule-based (EFR) models, the proposed solution exhibits superior performance, validating its scalability and effectiveness for real-world MIoT applications.