International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 18, No. 2, 8 Apr. 2026
Cover page and Table of Contents: PDF (size: 771KB)
Reinforcement Learning-Based Self-Healing Routing in Fault-prone Wireless Sensor Networks
PDF (771KB), PP.112-125
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Author(s)
Dipti Chauhan
1,*
Pritika Bahad
1
Jay Kumar Jain
2
Index Terms
Fault Tolerance, Q-Learning, Self-Healing Routing, Reinforcement Learning (RL), Wireless Sensor Networks (WSNs)
Abstract
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.
Cite This Paper
Dipti Chauhan, Pritika Bahad, Jay Kumar Jain, "Reinforcement Learning-Based Self-Healing Routing in Fault-prone Wireless Sensor Networks", International Journal of Computer Network and Information Security(IJCNIS), Vol.18, No.2, pp.112-125, 2026. DOI:10.5815/ijcnis.2026.02.07
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