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Genetic Machine Learning Approach for Link Quality Prediction in Mobile Wireless Sensor Networks

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Cooperative Robots and Sensor Networks

Part of the book series: Studies in Computational Intelligence ((SCI,volume 507))

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Abstract

Establishing adequate RF (Radio Frequency) connectivity is the basic requirement for the proper operation of any wireless network. In a mobile wireless network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time can avoid unnecessary or even unuseful control/data messages transmissions. The current paper presents the so-called Genetic Machine Learning Approach for Link Quality Prediction, or simply GMLA, which is a solution to forecast the remainder RF connectivity time in mobile environments. Differently from all related works, GMLA allows building connectivity knowledge to estimate the RF link duration without the need of a pre-runtime phase. This allows to apply GMLA at unknown environments and mobility patterns. Its structure combines a Classifier System with a Markov chain model of the RF link quality. As the Markov model parameters are discovered on-the-fly, there is no need of a previous history to feed the Markov model. Obtained simulation results show that GMLA is a very suitable solution, as it outperforms approaches that use geographical positioning systems (GPS) and also approaches that use link-quality prediction, such as BD and MTCP. GMLA is generic enough to be applied to any layer of the communication protocol stack, especially in the link and network layers.

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Acknowledgments

Thanks are given to the Brazilian research agency CAPES (Coordination for the Improvement of Higher Education Personnel) for its financial contribution under grants 0155-11-0 and 0616-11-7.

Author information

Authors and Affiliations

  1. Department of Automation and Control Systems, Federal University of Santa Catarina, Florianópolis, Brazil

    Gustavo Medeiros de Araújo & Leandro Buss Becker

  2. UFSC/CTC/DAS/PPGEAS, Room 214, Trindade, PO 476, CEP 88040-900, Florianópolis, Brazil

    Gustavo Medeiros de Araújo & Leandro Buss Becker

  3. Department of Computer Science and Statistics, Paulista State University (UNESP), São Paulo, Brazil

    A. R. Pinto

  4. Rua Cristóvão Colombo, 2265 - Jardim Nazareth, CEP 15054-000, São José do Rio Preto, SP, Brazil

    A. R. Pinto

  5. Department of Distributed Systems, Otto-Von-Guericke-Univesität Magdeburg, Magdeburg, Germany

    Jörg Kaiser

  6. Universitätsplatz, 2 D-39106, Room 323, Magdeburg, Germany

    Jörg Kaiser

Authors
  1. Gustavo Medeiros de Araújo
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  2. A. R. Pinto
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  3. Jörg Kaiser
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  4. Leandro Buss Becker
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Corresponding author

Correspondence to Gustavo Medeiros de Araújo .

Editor information

Editors and Affiliations

  1. Prince Sultan University, Riyadh, Saudi Arabia

    Anis Koubâa

  2. CS Department DEEDS Group, TU Darmstadt, Darmstadt, Germany

    Abdelmajid Khelil

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Araújo, G.M.d., Pinto, A.R., Kaiser, J., Becker, L.B. (2014). Genetic Machine Learning Approach for Link Quality Prediction in Mobile Wireless Sensor Networks. In: Koubâa, A., Khelil, A. (eds) Cooperative Robots and Sensor Networks. Studies in Computational Intelligence, vol 507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39301-3_1

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  • DOI: https://doi.org/10.1007/978-3-642-39301-3_1

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