Reinforcement Learning-Based Self-Healing Routing in Fault-prone Wireless Sensor Networks

Wireless Sensor Networks consist of energy constrained sensor nodes that monitor and transmit data to a central base station. These networks are highly susceptible to link and node failures, which further degrades performance and reduce overall network reliability. In this paper we have addresses these challenges and proposed a reinforcement learning based self-healing routing (RL-SHR) protocol, implemented in NS2 simulation environment. In the work, each node functions as an autonomous RL agent that learns optimal routing paths by interacting with the network environment and adapting to failure conditions. The protocol enables nodes to dynamically avoid unreliable paths, recover from faults, and optimize performance over time. Simulation results shows that the proposed protocol significantly outperforms traditional routing protocols such as AODV and DSR in terms of packet delivery ratio, end-to-end delay, energy consumption and network lifetime under varying failure scenarios. This work lays the groundwork for integrating learning based resilience mechanisms into next generation sensor networks.