Distributed learning for multi-channel selection in wireless network monitoring

Yuan Xue, Pan Zhou, Tao Jiang, Shiwen Mao, Xiaolei Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

In this paper, we address an important problem in the wireless monitoring, i.e., how to choose channels with best (or worst) qualities timely and accurately. We consider both scenarios of one or more sniffers simultaneously monitoring multiple channels in the same area. Since the channel information is initially unknown to the sniffers, we shall adopt learning methods during the monitoring to predict the channel condition by a short time of observation. We formulate this problem as a novel branch of the classic multi- armed bandit (MAB) problem, named exploration bandit problem, to achieve a trade-off between monitoring time/resource budget and the channel selection accuracy. In the multiple sniffer cases, including partly-distributed (with limited communications) and fully-distributed (without any communications) scenarios, we take communication costs and interference costs into account, and analyze how these costs affect the accuracy of channel selection. Extensive simulations are conducted and the results show that the proposed algorithms could achieve higher channel selection accuracy than other exploration bandit approaches, hence it proves the advantages of the proposed algorithms.

Original languageEnglish (US)
Title of host publication2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509017324
DOIs
StatePublished - Nov 2 2016
Event13th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2016 - London, United Kingdom
Duration: Jun 27 2016Jun 30 2016

Publication series

Name2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2016

Conference

Conference13th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2016
CountryUnited Kingdom
CityLondon
Period6/27/166/30/16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Instrumentation

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  • Cite this

    Xue, Y., Zhou, P., Jiang, T., Mao, S., & Huang, X. (2016). Distributed learning for multi-channel selection in wireless network monitoring. In 2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2016 [7732984] (2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SAHCN.2016.7732984