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Reinforcement learning based routing in wireless mesh networks

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Abstract

This paper addresses the problem of efficient routing in backbone wireless mesh networks (WMNs) where each mesh router is equipped with multiple radio interfaces and a subset of nodes serve as gateways to the Internet. Most routing schemes have been designed to reduce routing costs by optimizing one metric, e.g., hop count and interference ratio. However, when considering these metrics together, the complexity of the routing problem increases drastically. Thus, an efficient and adaptive routing scheme that takes into account several metrics simultaneously and considers traffic congestion around the gateways is needed. In this paper, we propose an adaptive scheme for routing traffic in WMNs, called Reinforcement Learning-based Distributed Routing (RLBDR), that (1) considers the critical areas around the gateways where mesh routers are much more likely to become congested and (2) adaptively learns an optimal routing policy taking into account multiple metrics, such as loss ratio, interference ratio, load at the gateways and end-to end delay. Simulation results show that RLBDR can significantly improve the overall network performance compared to schemes using either Metric of Interference and Channel switching, Best Path to Best Gateway, Expected Transmission count, nearest gateway (i.e., shortest path to gateway) or load at gateways as a metric for path selection.

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Acknowledgments

We would like to thank Mr Vinicius da Cunha Martins Borges [43] for providing us with code for simulation. The research reported in this manuscript has been supported in part by Natural Sciences and Engineering Research Council of Canada (NSERC) and Bell Canada.

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Authors and Affiliations

  1. Network Research Laboratory, University of Montreal, Montreal, Canada

    Mustapha Boushaba, Abdelhakim Hafid & Abdeltouab Belbekkouche

  2. CIRRELT and MAGI, École Polytechnique de Montreal, Montreal, Canada

    Michel Gendreau

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  1. Mustapha Boushaba
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  2. Abdelhakim Hafid
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  3. Abdeltouab Belbekkouche
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  4. Michel Gendreau
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Correspondence to Mustapha Boushaba.

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Boushaba, M., Hafid, A., Belbekkouche, A. et al. Reinforcement learning based routing in wireless mesh networks. Wireless Netw 19, 2079–2091 (2013). https://doi.org/10.1007/s11276-013-0592-y

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