Environment–Mobility Interaction Mapping for Cognitive MANETs

  • Irene Macaluso
  • Timothy K. Forde
  • Oliver Holland
  • Keith E. Nolan
Chapter

Abstract

Cognitive MANETs are likely to be complex radio systems. We already know that no single MANET solution can address all environments that may be encountered; such is the rationale of an ad hoc network that it must address the networking demands of unforeseen scenarios. Rather, a cognitive MANET should be viewed as a feature-rich radio system, i.e. one which has access to a range of radio and network components, each suited to different demands. Such a reconfigurable system requires cognitive functionality to self-architect the radios when they are deployed in addition to the cognitive functionality required for the various layers to self-organise. However, any cognitive decision-making process requires awareness of the world for which it is trying to optimise the system. This chapter introduces the concept of an environment–mobility interaction map, a persistent internal representation of the network which captures the presence of areas in the network’s environment in which particular, sustained, mobility dynamics are observed. Such a self-generated map enables the cognitive MANET to plan a response to challenges brought about by these network dynamics.

Notes

Acknowledgements

This material is based upon work supported by Science Foundation Ireland under Grant No. 03/CE3/I405.

References

  1. 1.
    Forde, T.K., Doyle, L.E., O’Mahony, D., “Ad hoc innovation: Distributed decision making in ad hoc networks”, DOI: 10.1109/MCOM.2006.1632660, 2006, pp: 131–137.Google Scholar
  2. 2.
    Burbank, J.L., Chimento, P.F., Haberman, B.K., Kasch, W.T., “Challenges of military tactical networking and the elusive promise of MANET technology”, Communications Magazine, IEEE, Vol. 44, No. 11 DOI: 10.1109/COMM.2006.248156, 2006, pp: 39–45.Google Scholar
  3. 3.
    Tsunemine, T., Kadokawa, E., Ueda, Y., Fukumoto, J., Wada, T., Ohtsuki, K., Okada, H., “Emergency urgent communications for searching evacuation route in a local disaster”, Consumer Communications and Networking Conference, 2008. CCNC 2008. 5th IEEE, DOI: 10.1109/ccnc08.2007.267, 2008, pp: 1196–1200.Google Scholar
  4. 4.
    Fischer, M.J., Lynch, N.A., and Paterson, M.S., “Impossibility of distributed consensus with one faulty process,” Journal of the ACM, Vol. 32, No. 2, Apr. 1985, pp. 374–82.MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Doyle, L.E., “Essentials of Cognitive Radio (The Cambridge Wireless Essentials Series)”, Cambridge University Press; 1 edition, 30 April 2009.Google Scholar
  6. 6.
    Langley, P., Laird, J., and Rogers, S., “Cognitive architectures: Research issues and challenges,” Cognitive Systems Research, Vol. 10, no. 2, 2009, pp. 141–160.CrossRefGoogle Scholar
  7. 7.
    Nolan, K.E., Sutton, P.D., Doyle, L.E., Rondeau, T.W., Le, B., Bostian, C.W., “Dynamic spectrum access and coexistence experiences involving two independently developed cognitive radio testbeds”, New Frontiers in Dynamic Spectrum Access Networks, 2007. DySPAN 2007. 2nd IEEE International Symposium on, DOI: 10.1109/DYSPAN.2007.43, 2007, pp: 270–275.Google Scholar
  8. 8.
    Hossain, E., “IEEE802.16/WiMAX-based broadband wireless networks: Protocol engineering, applications, and services”, Communication Networks and Services Research, 2007. CNSR ’07. Fifth Annual Conference on, DOI: 10.1109/CNSR.2007.37, 2007, pp: 3–4.Google Scholar
  9. 9.
    Ariyakhajorn, J., Wannawilai, P., Sathitwiriyawong, C., “A comparative study of random waypoint and Gauss-Markov mobility models in the performance evaluation of MANET”, Communications and Information Technologies, 2006. ISCIT ’06. International Symposium on, DOI: 10.1109/ISCIT.2006.339866, 2006, pp: 894–899.Google Scholar
  10. 10.
    Biradar, S.R., Sarma, H.H.D., Sharma, K., Sarkar, S.K., Puttamadappa, C., “Performance comparison of reactive routing protocols of MANETs using group mobility model”, 2009 International Conference on Signal Processing Systems, DOI: 10.1109/ICSPS.2009.56, 2009, pp: 192–195.Google Scholar
  11. 11.
    Gowrishankar, S., Sarkar, S., Basavaraju, T.G., “Simulation based performance comparison of community model, GFMM, RPGM, Manhattan Model and RWP-SS mobility models in MANET”, Networks and Communications, 2009. NETCOM ’09. First International Conference on, DOI: 10.1109/NetCoM.2009.31, 2009, pp: 408–413.Google Scholar
  12. 12.
    Doyle, L.E., Kokaram, A.C., Doyle, S.J., Forde, T.K., “Ad hoc networking, Markov random fields, and decision making”, Signal Processing Magazine, IEEE, Vol. 23, No. 5, 2006 , pp: 63–73.CrossRefGoogle Scholar
  13. 13.
    Abu Ali, N.A., Taha, A.-E.M., Hassanein, H.S., Mouftah, H.T., “IEEE 802.16 Mesh Schedulers: Issues and design challenges”, Network, IEEE, Vol. 22, No. 1, DOI: 10.1109/ MNET.2008.4435904, 2008, pp: 58–65.CrossRefGoogle Scholar
  14. 14.
    Ahmad, S., Awan, I., Waqqas, A., Ahmad, B., “Performance analysis of DSR & extended DSR protocols”, Modeling & Simulation, 2008. AICMS 08. Second Asia International Conference on, DOI: 10.1109/AMS.2008.72, 2008, pp: 191–196.Google Scholar
  15. 15.
    Rasheed, T., Javaid, U., Jerbi, M., Al Agha, K., “Scalable multi-hop ad hoc routing using modified OLSR routing protocol”, Personal, Indoor and Mobile Radio Communications, 2007. PIMRC 2007. IEEE 18th International Symposium on, DOI: 10.1109/PIMRC.2007.4394079, 2007, pp: 1–6.Google Scholar
  16. 16.
    Blazevic, L., Le Boudec, J.-Y., Giordano, S., “A location-based routing method for mobile ad hoc networks”, Mobile Computing, IEEE Transactions on, Vol. 4, No. 2, DOI: 10.1109/TMC.2005.16, 2005, pp: 97–110.Google Scholar
  17. 17.
    Blazevic, L., Buttyan, L., Capkun, S., Giordano, S., Hubaux, J.-P., Le Boudec, J.-Y., “Self organization in mobile ad hoc networks: the approach of Terminodes”, Communications Magazine, IEEE, Vol. 39, No. 6, DOI: 10.1109/35.925685, 2001, pp: 166–174.CrossRefGoogle Scholar
  18. 18.
    Ali, H.M., Busson, A., Veque, V., “Network layer link management using signal strength for ad-hoc networks”, Computers and Communications, 2009. ISCC 2009. IEEE Symposium on, DOI:10.1109/ISCC.2009.5202353, 2009, pp: 141–146.Google Scholar
  19. 19.
    Hua, E.Y., Haas, Z.J., “An algorithm for prediction of link lifetime in MANET based on unscented kalman filter”, Communications Letters, IEEE, Vol. 13, No. 10, DOI: 10.1109/LCOMM.2009.090974, 2009, pp: 782–784.CrossRefGoogle Scholar
  20. 20.
    Nen-Chung Wang, Yu-Li Su, “A power-aware multicast routing protocol for mobile ad hoc networks with mobility prediction”, Local Computer Networks, 2005. 30th Anniversary. The IEEE Conference on, DOI: 10.1109/LCN.2005.15, 2005.Google Scholar
  21. 21.
    Kuipers, B., “The spatial semantic hierarchy,” Artificial Intelligence, Vol. 119, No. 1–2, 2000, pp: 191–233.MathSciNetMATHCrossRefGoogle Scholar
  22. 22.
    Thrun, S., “Learning metric-topological maps for indoor mobile robot navigation* 1,” Artificial Intelligence, Vol. 99, No. 1, 1998, pp: 21–71.MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Irene Macaluso
    • 1
  • Timothy K. Forde
    • 1
  • Oliver Holland
    • 2
  • Keith E. Nolan
    • 1
  1. 1.CTVR, Trinity CollegeDublinIreland
  2. 2.CTR, King’s CollegeLondonUK

Personalised recommendations