Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Tembine, H.: Distributed Strategic Learning for Wireless Engineers, 1st edn. CRC Press, May 2012
Dietritch, I., Dressler, F.: On the lifetime of wireless sensor networks. ACM Transactions on Sensor Networks (TOSN) (2009)
Aung, A.: Distributed Algorithms for Improving Wireless Sensor Network Lifetimewith Adjustable Sensing Range. Thesis, Georgia State University (2007)
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)
Watson, J.: Strategy: An Introduction to Game Theory, 3rd edn. W. W. Norton Company (2013)
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)
Sengupta, S.: A Game Theoretic Framework for Power Control in Wireless Sensor Networks. IEEE Transactions on Computers 59(2), 231–242 (2010)
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 2014
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)
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 2009
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 2005
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)
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 2004
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 2011
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 2015
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 2010
Saraydar, U.C., Narayan, B.M., Goodman, D.J.: Efficient Power Control via Pricing in Wireless Data Networks. Transactions on Communications 50(2), February 2002
Takashi, U.: A Note on Discrete Convexity and Local Optimality. Japan Journal of Industrial Applied Mathemathics, 21–29 (2006)
Barth, D., Echabbi, L., Hamlaoui, C.: Optimal Transit Price Negotiation: The Distributed Learning Perspective. Journal of Universal Computer Science 14(5) (2008)
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 2008
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
El Hammouti, H., Echabbi, L., Ben Maissa, Y. (2016). Maximizing QoS in Heterogeneous Wireless Sensor Networks Using Game Theory and Learning Algorithms. In: Sabir, E., Medromi, H., Sadik, M. (eds) Advances in Ubiquitous Networking. UNet 2015. Lecture Notes in Electrical Engineering, vol 366. Springer, Singapore. https://doi.org/10.1007/978-981-287-990-5_18
Download citation
DOI: https://doi.org/10.1007/978-981-287-990-5_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-287-989-9
Online ISBN: 978-981-287-990-5
eBook Packages: EngineeringEngineering (R0)