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April 23, 2026Open Access

Transforming Opportunistic Routing: A Deep Reinforcement Learning Framework for Reliable and Energy-Efficient Communication in Mobile Cognitive Radio Sensor Networks

SZSuleiman ZubairFederal University of Technology Minna BSBala Alhaji SalihuFederal University of Technology Minna ATAltyeb TahaKing Abdulaziz University

Key Points

The aim is to enhance data-forwarding reliability in cognitive radio sensor networks using deep reinforcement learning.
Developed DRL-MROR protocol incorporating deep reinforcement learning for adaptive routing.
Formulated routing as a Markov Decision Process and utilized experience replay with prioritized sampling.
Implemented a lightweight Deep Q-Network with specific resource constraints for real-world deployment.
Achieved up to 38% gain in throughput and 42% in goodput compared to original MROR protocol.
Reduced energy consumption per packet by 29% and improved network lifetime by approximately 18%.
Decreased control overhead by about 30% and end-to-end delay by up to 32% under various operating scenarios.

Abstract

The Mobile Reliable Opportunistic Routing (MROR) protocol improves data-forwarding reliability in Cognitive Radio Sensor Networks (CRSNs) through mobility-aware virtual contention groups and handover zoning. However, its heuristic decision logic is difficult to optimize under highly dynamic spectrum access and random node mobility. To address this limitation, we present DRL-MROR, a refined routing framework that incorporates deep reinforcement learning (DRL) to enable intelligent and adaptive forwarding decisions. In DRL-MROR, the secondary users (SUs) act as autonomous agents that observe local state information, including primary-user activity, link quality, residual energy, and neighbor-mobility patterns. Each agent learns a forwarding policy through a Deep Q-Network (DQN) optimized for long-term network utility in terms of throughput, delay, and energy efficiency. We formulate routing as a Markov Decision Process (MDP) and use experience replay with prioritized sampling to improve learning stability and convergence. The DQN used at each node is intentionally lightweight, requiring 5514 trainable parameters, about 21.5 kB of weight storage in 32-bit precision, and approximately 5.4k multiply-accumulate operations per inference, which supports practical deployment on edge-capable CRSN nodes. Extensive simulations show that DRL-MROR outperforms the original MROR protocol and representative AI-based routing baselines such as AIRoute under diverse operating conditions. The results indicate gains of up to 38% in throughput, 42% in goodput, a 29% reduction in energy consumed per packet, and an approximately 18% improvement in network lifetime, while maintaining high route stability and fairness. DRL-MROR also reduces control overhead by about 30% and average end-to-end delay by up to 32%, maintaining strong performance even under elevated PU activity and higher node mobility. These results show that augmenting opportunistic routing with lightweight DRL can substantially improve adaptability and efficiency in next-generation IoT-oriented CRSNs.

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