Grouping Nodes in Wireless Sensor Networks Using Coalitional Game Theory

  • Fatemeh Kazemeyni
  • Einar Broch Johnsen
  • Olaf Owe
  • Ilangko Balasingham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6117)

Abstract

Wireless sensor networks are typically ad-hoc networks of resource-constrained nodes; in particular, the nodes are limited in power resources. It can be difficult and costly to replace sensor nodes, for instance when implanted in the human body. Data transmission is the major consumer of power, so it is important to have power-efficient protocols. In order to reduce the total power consumption in the network, we consider nodes which cooperate to transmit data. Nodes which cooperate, form a group. A mobile node may at some point be without a group, in which case it is desirable for the node to be able to join a group. In this paper we propose a modification of the AODV protocol to decide whether a node should join a given group, using coalitional game theory to determine what is beneficial in terms of power consumption. The protocol is formalized in rewriting logic, implemented in the Maude tool, and validated by means of Maude’s model exploration facilities.

References

  1. 1.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Computer Networks 38(4), 393–422 (2002)CrossRefGoogle Scholar
  2. 2.
    Başar, T., Olsder, G.J.: Dynamic non-cooperative game theory. SIAM, Philadelphia (1999)Google Scholar
  3. 3.
    Clavel, M., Durán, F., Eker, S., Lincoln, P., Martí-Oliet, N., Meseguer, J., Quesada, J.F.: Maude: Specification and programming in rewriting logic. Theoretical Computer Science 285, 187–243 (2002)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Dong, J.S., Sun, J., Sun, J., Taguchi, K., Zhang, X.: Specifying and verifying sensor networks: An experiment of formal methods. In: Liu, S., Maibaum, T.S.E., Araki, K. (eds.) ICFEM 2008. LNCS, vol. 5256, pp. 318–337. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Ergen, S.C., Ergen, M., Koo, T.J.: Lifetime analysis of a sensor network with hybrid automata modelling. In: Raghavendra, C.S., Sivalingam, K.M. (eds.) Proc. First ACM Intl. Workshop on Wireless Sensor Networks and Applications (WSNA 2002), pp. 98–104. ACM, New York (2002)Google Scholar
  6. 6.
    Fehnker, A., Fruth, M., McIver, A.: Graphical modelling for simulation and formal analysis of wireless network protocols. In: Butler, M., Jones, C.B., Romanovsky, A., Troubitsyna, E. (eds.) Methods, Models and Tools for Fault Tolerance. LNCS, vol. 5454, pp. 1–24. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Fehnker, A., van Hoesel, L., Mader, A.: Modelling and verification of the LMAC protocol for wireless sensor networks. In: Davies, J., Gibbons, J. (eds.) IFM 2007. LNCS, vol. 4591, pp. 253–272. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Fudenberg, D., Tirole, J.: Game Theory. MIT Press, Cambridge (1991)Google Scholar
  9. 9.
    Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Norwell (1992)MATHGoogle Scholar
  10. 10.
    Goodman, D., Mandayam, N.: Power control for wireless data. IEEE Personal Communications 7, 48–54 (2000)CrossRefGoogle Scholar
  11. 11.
    Inaltekin, H., Wicker, S.B.: The analysis of nash equilibria of the one-shot random-access game for wireless networks and the behavior of selfish nodes. IEEE/ACM Trans. Netw. 16(5), 1094–1107 (2008)CrossRefGoogle Scholar
  12. 12.
    Johnsen, E.B., Owe, O., Bjørk, J., Kyas, M.: An object-oriented component model for heterogeneous nets. In: de Boer, F.S., Bonsangue, M.M., Graf, S., de Roever, W.-P. (eds.) FMCO 2007. LNCS, vol. 5382, pp. 257–279. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Katelman, M., Meseguer, J., Hou, J.C.: Redesign of the lmst wireless sensor protocol through formal modeling and statistical model checking. In: Barthe, G., de Boer, F.S. (eds.) FMOODS 2008. LNCS, vol. 5051, pp. 150–169. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Lloret, J., Palau, C.E., Boronat, F., Tomás, J.: Improving networks using group-based topologies. Computer Communications 31(14), 3438–3450 (2008)CrossRefGoogle Scholar
  15. 15.
    Mackenzie, A.B., Wicker, S.B.: Game theory and the design of self-configuring, adaptive wireless networks. IEEE Communications Magazine 39(11), 126–131 (2001)CrossRefGoogle Scholar
  16. 16.
    Meseguer, J.: Conditional rewriting logic as a unified model of concurrency. Theoretical Computer Science 96, 73–155 (1992)MATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Miller, D.A., Tilak, S., Fountain, T.: Token equilibria in sensor networks with multiple sponsors. In: CollaborateCom. IEEE, Los Alamitos (2005)Google Scholar
  18. 18.
    Nair, S., Cardell-Oliver, R.: Formal specification and analysis of performance variation in sensor network diffusion protocols. In: Balsamo, S., Chiasserini, C.-F., Donatiello, L. (eds.) Proc. 7th Intl. Symp. on Modeling Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2004), pp. 170–173. ACM, New York (2004)CrossRefGoogle Scholar
  19. 19.
    Ölveczky, P.C., Thorvaldsen, S.: Formal modeling, performance estimation, and model checking of wireless sensor network algorithms in Real-Time Maude. Theor. Comput. Sci. 410(2-3), 254–280 (2009)MATHCrossRefGoogle Scholar
  20. 20.
    Park, S., Savvides, A., Srivastava, M.B.: SensorSim: a simulation framework for sensor networks. In: Boukerche, A., Meo, M., Tropper, C. (eds.) Proc. 3rd Intl. Symposium on Modeling Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2000), pp. 104–111. ACM, New York (2000)CrossRefGoogle Scholar
  21. 21.
    Perkins, C.E., Belding-Royer, E.M.: Ad-hoc on-demand distance vector routing. In: WMCSA, pp. 90–100. IEEE Computer Society, Los Alamitos (1999)Google Scholar
  22. 22.
    Pham, H.N., Pediaditakis, D., Boulis, A.: From simulation to real deployments in WSN and back. In: Intl. Symp. on a World of Wireless, Mobile and Multimedia Networks (WoWMoM 2007), pp. 1–6. IEEE, Los Alamitos (2007)CrossRefGoogle Scholar
  23. 23.
    Saad, W., Han, Z., Debbah, M., Hjørungnes, A., Başar, T.: Coalitional game theory for communication networks: A tutorial. IEEE Signal Processing Magazine 26(5), 77–97 (2009); Special Issue on Game TheoryCrossRefGoogle Scholar
  24. 24.
    Strauss, R., Abedi, A.: Game theoretic power allocation in sparsely distributed clusters of wireless sensors (gpas). In: Guizani, M., Mueller, P., Fähnrich, K.-P., Vasilakos, A.V., Zhang, Y., Zhang, J. (eds.) IWCMC, pp. 1454–1458. ACM, New York (2009)CrossRefGoogle Scholar
  25. 25.
    Tschirner, S., Xuedong, L., Yi, W.: Model-based validation of QoS properties of biomedical sensor networks. In: de Alfaro, L., Palsberg, J. (eds.) Proc. 8th Intl. Conf. on Embedded Software (EMSOFT 2008), pp. 69–78. ACM, New York (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fatemeh Kazemeyni
    • 1
    • 2
  • Einar Broch Johnsen
    • 1
  • Olaf Owe
    • 1
  • Ilangko Balasingham
    • 2
    • 3
  1. 1.Department of InformaticsUniversity of OsloNorway
  2. 2.The Interventional Center, Oslo University Hospital, Institute of Hospital MedicineUniversity of OsloNorway
  3. 3.Department of Electronics and TelecommunicationNorwegian University of Science and TechnologyTrondheimNorway

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