Reinforcement Learning Aided Routing in Tactical Wireless Sensor Networks

Reinforcement Learning Aided Routing in Tactical Wireless Sensor Networks

Okine, A.A., Adam, N., Kaddoum, G. (2023). Reinforcement Learning Aided Routing in Tactical Wireless Sensor Networks. In: Sabir, E., Elbiaze, H., Falcone, F., Ajib, W., Sadik, M. (eds) Ubiquitous Networking. UNet 2022. Lecture Notes in Computer Science, vol 13853. Springer, Cham. https://doi.org/10.1007/978-3-031-29419-8_16

Abstract or Summary

A wireless sensor network (WSN) consists of a large number of sensor nodes with limited battery lives that are dispersed geographically to monitor events and gather information from a geographical area. On the other hand, tactical WSNs are mission-critical WSNs that are used to support military operations, such as intrusion detection, battlefield surveillance, and combat monitoring. Such networks are critical to the collection of situational data on a battlefield for timely decision-making. Due to their application area, tactical WSNs have unique challenges, not seen in commercial WSNs, such as being targets for adversarial attacks. These challenges make packet routing in tactical WSNs a daunting task. In this article, we propose a multi-agent Q-learning-based routing scheme for a tactical WSN consisting of static sensors and a mobile sink. Using the proposed routing scheme, a learning agent (i.e., network node) adjusts its routing policy according to the estimates of the Q-values of the available routes via its neighbors. The Q-values capture the quickness, reliability, and energy efficiency of the routes as a function of the number of hops to sink, the one-hop delay, the energy cost of transmission, and the packet loss rate of the neighbors. Simulation results demonstrate that, in comparison to a baseline random hop selection scheme, the proposed scheme reduces the packet loss rate and mean hop delay, and enhances energy efficiency in the presence of jamming attacks.

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