M. Chennaiah

A dynamic reward framework for scalable and efficient IoT-WSN routing using deep reinforcement learning.

A dynamic reward structuring framework within deep reinforcement learning to enable adaptive and balanced routing in IoT-WSNs and achieves significant performance gains, including approximately 30% improvement in energy efficiency, 25% reduction in latency, and 35% increase in network throughput compared with baseline methods.