Deep Learning–Enabled Predictive Routing for Energy-Efficient WSNs

Wireless Sensor Networks (WSNs) experience rapid energy depletion due to inefficient routing under dynamic conditions such as node failures, congestion, and fluctuating link quality. Traditional routing protocols including LEACH, AODV, and DSR rely on heuristic mechanisms and lack predictive adaptability. This research proposes a Deep Learning–Enabled Predictive Routing (DL-PR) framework that analyzes historical network parameters such as residual energy, delay, packet delivery ratio, and traffic load to predict future network conditions. A deep learning model constructs a predicted network graph and selects optimal routing paths using a composite cost function. MATLAB-based simulation results demonstrate that DL-PR significantly improves network lifetime, maintains higher residual energy, increases packet delivery ratio, and reduces end-to-end delay compared to conventional routing protocols. The proposed framework is applicable in smart agriculture, healthcare, industrial IoT, and defense communication systems.