Skip to main content

Advertisement

Log in

Cooperative differential game model based on trade-off between energy and delay for wireless sensor networks

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

A wireless sensor network (WSN) consists of a large number of unattended sensors with limited storage, battery power, computation, and communication capabilities, where battery power (or energy) is the most crucial resource for sensor nodes. The information sensed by sensors needs to be transmitted to sink quickly especially for the applications with delay restriction. However, it is difficult to achieve optimal energy efficiency and source-to-sink delay simultaneously. So it is very necessary to find a power control solution based tradeoff between energy and delay. In this paper, a cooperative differential game model is proposed, and a power solution is obtained which determines a fair distribution of the total cooperative cost among sources.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Akkaya, K., & Younis, M. (2004). Energy-aware delay-constrained data in wireless sensor networks. Journal of Communication Systems (Special Issue on QoS Support and Service Differentiation in Wireless Networks), 17(6), 663–687.

    Google Scholar 

  • Bellman, R.E. (1957). Dynamic Programming. Princeton: Princeton University Press. Republished in 2003. New York: Dover.

    Google Scholar 

  • Habib, M. A., & Sajal, K. D. (2008). A trade-off between energy and delay in data dissemination for wireless sensor networks using transmission range slicing. Computer Communications, 31, 1687–1704.

    Article  Google Scholar 

  • Haenggi, M. (2003). Energy-balancing strategies for wireless sensor networks. Circuits and Systems, 4, 828–831.

    Google Scholar 

  • Iranli, A., Fatemi, H., & Pedram, M. (2003). A game theoretic approach to dynamic energy minimization. In Wireless transceivers, international conference on computer aided design proceedings of the 2003 IEEE/ACM international conference on computer-aided design.

    Google Scholar 

  • Jin, Z., & Papavassiliou, S. (2003). On the energy-efficient organization and the lifetime of multi-hop sensor networks. IEEE Communications Letters, 7, 537–539.

    Article  Google Scholar 

  • Li, L., & Halpern, J. Y. (2001). Minimum-energy mobile wireless networks revisited. In IEEE international conference on communications, ICC’01 (pp. 278–283).

  • Lindsey, S., Raghavendra, C., & Sivalingam, K. (2002). Data gathering algorithms in sensor networks using energy metrics. IEEE Transactions on Parallel and Distributed Systems, 13(9), 924–935.

    Article  Google Scholar 

  • Mackenzie, A., & Wicker, S. (2001). Game theory and the design of self-configuring, adaptive wireless networks. IEEE Communications Magazine, 39(11), 126–131.

    Article  Google Scholar 

  • Miller, M., & Sengul, C. I. (2005). Exploring the energy-latency trade-off for broadcasts in energy-saving sensor networks. In The 25th IEEE international conference on distributed computing systems (ICDCS 2005), Columbus, OH (pp. 6–10).

    Google Scholar 

  • Petrosyan, L., & Zaccour, G. (2003). Time-consistent Shapley value allocation of pollution cost reduction. Journal of Economic Dynamics & Control, 27, 381–398.

    Article  Google Scholar 

  • Rao, D., Miller, S., & Tilak, T. (2005). Fountain, “Token” equilibria in sensor networks with multiple sponsors. In Proceedings of the workshop on stochasticity in distributed systems.

    Google Scholar 

  • Shapley, L. S. (1953). Shapley. A value for n-person games. In H. W. Kuhn & A. W. Tucker (Eds.), Annals of mathematics studies: Vol. 28. Contributions to the theory of games (pp. 307–317).

    Google Scholar 

  • Singh, S., Woo, M., & Raghavendra, C. S. (1998). Power-aware routing in mobile ad hoc networks. In Proceedings of the 4th annual ACM/IEEE international conference on mobile computing and networking (pp. 181–190).

    Chapter  Google Scholar 

  • Srinivasan, V., Nuggehalli, P., Chiasserini, C., & Rao, R. (2003). Cooperation in wireless ad hoc networks. In Proceedings of INFOCOM 2003, San Francisco, CA, USA (Vol. 2, pp. 808–817).

    Google Scholar 

  • Urpi, A., Bonuccelli, M., & Giordano, S. (2003). Modelling cooperation in mobile ad hoc networks: a formal description of selfishness. In Proceedings of WiOpt’03, Sophia-Antipolis, France.

    Google Scholar 

  • Yeung, D. W. K., & Petrosyan, L. A. (2006). Cooperative stochastic differential games. Berlin: Springer.

    Google Scholar 

  • Yuan, J., & Yu, W. (2006). Distributed cross-layer optimization of wireless sensor networks: a game theoretic approach. In Proceedings of IEEE global telecommunications conference.

    Google Scholar 

  • Zhang, H. W., & Arora, A. (2003). GS3: scalable self-configuration and self-healing in wireless sensor networks. Computer Networks, 2, 459–480.

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported in part by the National Science Foundation of P.R. China under Grant no. 60773074 and by the National Science Foundation of P.R. China under Grant no. 60903004. Meanwhile, is supported by the Education Department of Henan province Science and Technology Research Projects no. 13A510030.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xu-Na Miao.

Appendix: Proof of convexity of the cooperative game

Appendix: Proof of convexity of the cooperative game

To prove that the cooperative game Γ c (x,t) is convex, we need to show that

$$v(K) + v(L) \geq v(K \cup L) + v(K \cap L),\quad \mbox{i.e.}, $$
$$v(K) + v(L)-v(K \cup L) + v(K \cap L) \geq0,\quad \forall K,L \in M. $$

Let k=|K|, l=|L|, x=|KL|, m=|M|. Then, |KL|=k+lx. Using (16), the left-hand side of the above inequality is given by

Straightforward calculations permit one to reduce the above expression to

The result follows from the fact that k≥1, l≥1 and x≤min(k,l).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Miao, XN., Xu, G. Cooperative differential game model based on trade-off between energy and delay for wireless sensor networks. Ann Oper Res 206, 297–310 (2013). https://doi.org/10.1007/s10479-013-1354-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-013-1354-z

Keywords

Navigation