AI-driven IoT routing: A hybrid deep reinforcement learning and shrike optimization framework for energy-efficient communication

The expansion of Internet of Things (IoT) networks has intensified the need for intelligent and adaptive routing strategies capable of handling frequent topological changes, energy limitations, and application-specific performance requirements. Existing routing protocols often struggle to simultaneously achieve scalability, energy conservation, and reliability. To address these challenges, this paper introduces a novel hybrid routing framework, DRL-SOA, which fuses Deep Reinforcement Learning (DRL) with the Shrike Optimization Algorithm (SOA) to enable real-time, congestion-aware, and energy-efficient routing in IoT environments. The DRL component incrementally learns optimal routing paths by interacting with dynamic network conditions, while SOA enhances the convergence of Q-learning by identifying the most promising action sequences using a nature-inspired hunting mechanism. The proposed method employs a multi-parameter fitness function that considers link stability, link duration, remaining energy, bandwidth availability, and node connectivity to determine optimal routing paths. Extensive simulations using NS-3 demonstrate that DRL-SOA significantly outperforms existing approaches, including RIATA, DRL-IRS, and DOACAR. Notably, the proposed approach achieves up to a 25% increase in network lifespan, reduces routing overhead by 22%, and enhances packet delivery and energy efficiency across different node densities and mobility rates. These results establish DRL-SOA as a scalable and robust routing protocol for next-generation IoT systems.