Maximizing QoS in Heterogeneous Wireless Sensor Networks Using Game Theory and Learning Algorithms

  • Hajar El HammoutiEmail author
  • Loubna Echabbi
  • Yann Ben Maissa
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 366)


A Wireless Sensor Network (WSN) is composed of sensor equipped devices that aim at sensing and processing information from the surrounding environment. Energy consumption is the major concern of WSNs. At the same time, quality of service is to be considered especially when dealing with critical WSNs.

In this paper, we present a game theory based approach to maximize quality of service, defined as the aggregate frame success rate, while optimizing power allocation. Game theory is designed to study interactions between players (e.g. chess players) who decide on a set of actions (e.g. the players moves) to reach the objective outcomes (e.g. to win the game). Here, we model the system as a potential game. We show that the optimal power allocation, crucial in a heterogeneous sensor network, is a Nash equilibrium of this game, and we discuss its uniqueness. For simulations, we present a fully distributed algorithm that drives the whole system to the optimal power allocation.


Game theory Nash equilibrium Global optimum Potential game Frame success rate Distributed algorithms Learning algorithms Heterogeneous wireless sensor networks Quality of service Energy consumption 


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  1. 1.
    Tembine, H.: Distributed Strategic Learning for Wireless Engineers, 1st edn. CRC Press, May 2012Google Scholar
  2. 2.
    Dietritch, I., Dressler, F.: On the lifetime of wireless sensor networks. ACM Transactions on Sensor Networks (TOSN) (2009)Google Scholar
  3. 3.
    Aung, A.: Distributed Algorithms for Improving Wireless Sensor Network Lifetimewith Adjustable Sensing Range. Thesis, Georgia State University (2007)Google Scholar
  4. 4.
    Cheng, X., Narahari, B., Simha, R., Cheng, M.X., Liu, D.: Strong minimum energy topology in WSNs: NPCompleteness and heuristics. IEEE Transactions on Mobile Computing, 248–256 (2003)Google Scholar
  5. 5.
    Watson, J.: Strategy: An Introduction to Game Theory, 3rd edn. W. W. Norton Company (2013)Google Scholar
  6. 6.
    Shi, H.Y., Wang, W.L., Kwok, N.M., Chen, S.Y.: Game Theory for Wireless Sensor Networks: A Survey. Sensors 12(7), 9055–9097 (2012)CrossRefGoogle Scholar
  7. 7.
    Sengupta, S.: A Game Theoretic Framework for Power Control in Wireless Sensor Networks. IEEE Transactions on Computers 59(2), 231–242 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Wazid, M., Gupta, N., Singh, D.P., Goudar, R.H.: Data recovery with energy efficient task allocation in wireless sensor networks. In: IEEE Colombian Conference on Communications and Computing (COLCOM), pp. 1–5, June 2014Google Scholar
  9. 9.
    Mahani, A., Farahmand, E., Kavian, Y.S., Rashvand, H.F.: A distributed sensing and medium access algorithm to prolong the lifetime of WSN. In: IET Conference on Wireless Sensor Systems (WSS), pp. 1–5 (2012)Google Scholar
  10. 10.
    Hongliang, R., Meng, M.Q.H.: Game-Theoretic Modeling of Joint Topology Control and Power Scheduling for Wireless Heterogeneous Sensor Networks. IEEE Transactions on Automaton Science and Engineering 6(4), October 2009Google Scholar
  11. 11.
    Meshkati, F., Poor, H.V., Schwartz, S.C., Mandayam, N.B.: An Energy Efficient Approach to Power Control and Receiver Design in Wireless Data Networks. IEEE Transactions on Communications 53(11), November 2005Google Scholar
  12. 12.
    Basslam, M., Elazouzi, R., Essabir, S., Echabbi, L.: Market share game with adversarial access providers: a neutral and a non-neutral network analysis. In: International Conference on NETwork Games, COntrol and OPtimization (NETGCOOP) (2011)Google Scholar
  13. 13.
    Weinberg, M., Rosenschein, J.S.: Best-response multiagent learning in non-stationary environments. In: The Third International Joint Conference on Autonomous Agents and Multiagent Systems AAMAS, pp. 22–23, July 2004Google Scholar
  14. 14.
    Song, Y., Wong, S.H.Y., Lee, K.: A game theoretical approach to gateway selections in multi-domain wireless networks. In: The Fifth Annual Conference of the International Technology Alliance (ACITA 2011), College Park, MD, September 2011Google Scholar
  15. 15.
    Elhammouti, H., Echabbi, L., Elazouzi, R.: A fully distributed algorithm for power allocation in heterogeneous networks. In: Proceedings of the International Conference of Networked Systems (NETYS), pp. 13–14, May 2015Google Scholar
  16. 16.
    Valli, R., Dananjayan, P.: A Non-Cooperative Game Theoretical Approach for Power Control in Virtual MIMO Wireless Sensor Network. International Journal of UbiComp (IJU) 1(3), July 2010Google Scholar
  17. 17.
    Saraydar, U.C., Narayan, B.M., Goodman, D.J.: Efficient Power Control via Pricing in Wireless Data Networks. Transactions on Communications 50(2), February 2002Google Scholar
  18. 18.
    Takashi, U.: A Note on Discrete Convexity and Local Optimality. Japan Journal of Industrial Applied Mathemathics, 21–29 (2006)Google Scholar
  19. 19.
    Barth, D., Echabbi, L., Hamlaoui, C.: Optimal Transit Price Negotiation: The Distributed Learning Perspective. Journal of Universal Computer Science 14(5) (2008)Google Scholar
  20. 20.
    Xing, Y., Chandramouli, R.: Stochastic Learning Solution for Distributed Discrete Power Control Game in Wireless Data Networks. IEEE/ACM Transactions on Networking 16(4), March 2008Google Scholar

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Authors and Affiliations

  • Hajar El Hammouti
    • 1
    Email author
  • Loubna Echabbi
    • 1
  • Yann Ben Maissa
    • 1
  1. 1.Telecommunications Systems, Networks and Services LaboratoriesNational Institute of Posts and TelecommunicationsRabatMorocco

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