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

  • Gustavo Medeiros de Araújo
  • A. R. Pinto
  • Jörg Kaiser
  • Leandro Buss Becker
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 507)

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.

Keywords

Mobile wireless networks RF connectivity prediction  Classifier systems 

Notes

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.

References

  1. 1.
    Ali, A., Latiff, L.A., Fisal, N.: GPS-free indoor location tracking in mobile ad hoc network (MANET) using RSSI. In: Proceeding of IEEE RFM, pp. 251–255 (2005)Google Scholar
  2. 2.
    Araújo, G.M.d., Becker, L.B.: A network conditions aware geographical forwarding protocol for real-time applications in mobile wireless sensor networks. In: Proceeding of IEEE AINA. IEEE Computer Soceity, pp. 38–45 (2011)Google Scholar
  3. 3.
    Araújo, G.M.d., Kaiser, J., Becker, L.B.: An optimized Markov model to predict link quality in mobile wireless sensor networks. In: Proceeding of IEEE ISCC. IEEE Computer Society, California, pp. 307–312 (2012)Google Scholar
  4. 4.
  5. 5.
    Camp, T., Boleng, J., Davies, V.: A survey of mobility models for ad hoc network research. Wireless communications and mobile computing. Wiley Online Libr. 2, 483–502 (2002)Google Scholar
  6. 6.
    Chella, A., Lo, G.R., Macaluso, I., Ortolani, M., Peri, D.: Multi-robot Interacting Through Wireless Sensor Networks. Infrastructure, vol. 4733 , pp. 789–796. Springer, Berlin (2007)Google Scholar
  7. 7.
    Chen, S., Jones, H., Jayalath, D.: Effective link operation duration: a new routing metric for mobile Ad hoc networks. In: International Conference on Signal Processing and Communication Systems, Citeseer (2007)Google Scholar
  8. 8.
    Clausen, T., Jacquet, P.: Optimized link state routing protocol (OLSR). RFC 3626, IETF Network Working, Group, Oct 2003Google Scholar
  9. 9.
    Deak, G., Curran, K., Condell, J.: Filters for RSSI-based measurements in a device-free passive localisation scenario. Int. J. Image Process. Commun. 15, 23–34 (2011)Google Scholar
  10. 10.
    Erman, A.T., Van Hoesel, L., Havinga, P., Wu, J.: Enabling mobility in heterogeneous wireless sensor networks cooperating with UAVs for mission-critical management. IEEE Wireless Commun. 15, 38–46 (2008)Google Scholar
  11. 11.
    Erman, A.T., Van Hoesel, L., Havinga, P., Wu, J.: Mobile wireless sensor network: Architecture and enabling technologies for ubiquitous computing. Proc. IEEE AINAW 2, 113–120 (2007)Google Scholar
  12. 12.
    Farkas, K., Hossmann, T., Legendre, F., Plattner, B., Das. S.K.: Link quality prediction in mesh networks. Comput. Commun. 31, 1497–1512 (2008) ( Elsevier)Google Scholar
  13. 13.
    Freitas, E.P.d., Heimfarth, T., Schmidt, R., Wagner, F.R., Larsson, T., Pereira, C.E., Ferreira, A.M.: Coordinating aerial robots and unattended ground sensors for intelligent surveillance systems. Int. J. Comput. Commun. Control Univ. Oradea 5, 52–70 (2010)Google Scholar
  14. 14.
    Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison-wesley, Reading (1989)Google Scholar
  15. 15.
    Guha, R.K., Sarkar, S.: Characterizing temporal SNR variation in 802.11 networks. IEEE Trans. Veh. Technol. 57, 2002–2013 (2008)CrossRefGoogle Scholar
  16. 16.
    INETMANET Framework for OMNEST/OMNeT++ 4.x. http://wiki.github.com/inetmanet/inetmanet/
  17. 17.
    Koksal, M. M.: A survey of network simulators supporting wireless networks, Middle East Technical University Ankara, TURKEY, 22 Oct 2008Google Scholar
  18. 18.
    Lee, S.J., Su, W., Gerla, M.: Mobility prediction in wireless networks. In: Proceeding of IEEE ICCCN 2000, Boston, MA, p. 49 (2000)Google Scholar
  19. 19.
    Liu, T., Sadler, C.M., Zhang, P., Martonosi, M.: Implementing software on resource-constrained mobile sensors: experiences with Impala and ZebraNet. Proc MobiSys, pp. 256–269. ACM, New York (2004)Google Scholar
  20. 20.
    Nicholson, A.J., Noble, B.D.: Breadcrumbs: forecasting mobile connectivity. In: Proceeding of ACM MobiCom, pp. 46–57 (2088)Google Scholar
  21. 21.
    Perkins, C., Belding-Royer, E., Das, S.: Ad hoc on-demand distance vector (AODV) routing. RFC 3561, IETF Network Working Group, July 2003Google Scholar
  22. 22.
    Priyantha, N.B., Miu, A.K., Balakrishnan, H., Teller, S.: The cricket compass for context-aware mobile applications. In: Proceeding of ACM MobiCom, pp. 1–14 (2001)Google Scholar
  23. 23.
    Rosa, F.d., Malizia, A., Mecella, M.: Disconnection prediction in mobile ad hoc networks for supporting cooperative work. IEEE Pervasive Comput. 3, 62–70 (2005)Google Scholar
  24. 24.
    Sabitha, R., Thangavelu, T.: Performance enhancement of fuzzy logic based transmission power control in wireless sensor networks using Markov based RSSI prediction. Eu. J. Sci. Res. Euro J. Pub. 59, pp. 68–84 (2011)Google Scholar
  25. 25.
    Su, W., Lee, S., Gerla, M.: Mobility prediction in wireless networks. In: Proceeding of IEEE ICCCN. IEEE, New York, pp. 4–9 (1999)Google Scholar
  26. 26.
    The Network Simulator - ns-2. http://www.isi.edu/nsnam/ns/
  27. 27.
    Valente, J., Sanz, D., Barrientos, A., Cerro, J., Ribeiro, Á., Rossi, C.: An Air-Ground Wireless Sensor Network for Crop Monitoring. Sensors 11, 6088–6108 (2011)CrossRefGoogle Scholar
  28. 28.
    Varga, A.: The OMNeT++ discrete event simulation system. In: Proceeding of ESM, pp. 319–324 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Gustavo Medeiros de Araújo
    • 1
    • 2
  • A. R. Pinto
    • 3
    • 4
  • Jörg Kaiser
    • 5
    • 6
  • Leandro Buss Becker
    • 1
    • 2
  1. 1.Department of Automation and Control SystemsFederal University of Santa CatarinaFlorianópolisBrazil
  2. 2.UFSC/CTC/DAS/PPGEASFlorianópolisBrazil
  3. 3.Department of Computer Science and StatisticsPaulista State University (UNESP)São PauloBrazil
  4. 4.Rua Cristóvão ColomboSão José do Rio PretoBrazil
  5. 5.Department of Distributed SystemsOtto-Von-Guericke-Univesität MagdeburgMagdeburgGermany
  6. 6.UniversitätsplatzMagdeburgGermany

Personalised recommendations