Discrete Strategy Game-theoretic Topology Control inWireless Sensor Networks

Evangelos D. Spyrou, Shusen Yang, Dimitrios K. Mitrakos

Abstract

One of the most significant problems in Wireless Sensor Network (WSN) deployment is the generation of topologies that maximize transmission reliability and guarantee network connectivity while also maximising the network’s lifetime. Transmission power settings have a large impact on the aforementioned factors. Increasing transmission power to provide coverage is the intuitive solution yet with it may come with lower packet reception and shorter network lifetime. However, decreasing the transmission power may result in the network being disconnected. To balance these trade-offs we propose a discrete strategy game-theoretic solution, which we call TopGame that aims to maximize the reliability between nodes while using the most appropriate level of transmission power that guarantees connectivity. In this paper, we provide the conditions for the convergence of our algorithm to a pure Nash equilibrium as well as experimental results. Here we show, using the Indriya WSN testbed, that TopGame is more energy-efficient and approaches a similar packet reception ratio with the current closest state of the art protocol ART.

References

  1. Abbasi, M. and Fisal, N. (2015). Noncooperative gamebased energy welfare topology control for wireless sensor networks. Sensors Journal, IEEE, 15(4):2344- 2355.
  2. Ahmed, N., Misra, P., Jha, S., and Ostry, D. (2009). Characterization of link asymmetry in wireless sensor networks. In ACM SenSys, pages 373-374.
  3. Altman, E., Kumar, A., and Hayel, Y. (2009). A potential game approach for uplink resource allocation in a multichannel wireless access network. In Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools, page 72. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).
  4. Antonopoulos, C., Prayati, A., Stoyanova, T., Koulamas, C., and Papadopoulos, G. (2009). Experimental evaluation of a WSN platform power consumption. In IEEE IPDPS, pages 1-8. IEEE.
  5. Blough, D., Leoncini, M., Resta, G., and Santi, P. (2007). Topology control with better radio models: Implications for energy and multi-hop interference. Performance Evaluation, 64(5):379-398.
  6. Breza, M. and McCann, J. (2008). Lessons in implementing bio-inspired algorithms on wireless sensor networks. In Adaptive Hardware and Systems, 2008. AHS'08. NASA/ESA Conference on, pages 271-276. IEEE.
  7. Burkhart, M., Von Rickenbach, P., Wattenhofer, R., and Zollinger, A. (2004). Does topology control reduce interference? In ACM MOBIHOC, pages 9-19.
  8. Candogan, U. O., Menache, I., Ozdaglar, A., Parrilo, P., et al. (2010). Near-optimal power control in wireless networks: A potential game approach. In INFOCOM, 2010 Proceedings IEEE, pages 1-9. IEEE.
  9. Chen, J., Yu, Q., Cheng, P., Sun, Y., Fan, Y., and Shen, X. (2011). Game theoretical approach for channel allocation in wireless sensor and actuator networks. IEEE transactions on automatic control, 56(10):2332-2344.
  10. Dammer, S. and Hinrichsen, H. (2003). Epidemic spreading with immunization and mutations. Physical Review E, 68(1):016114.
  11. Fu, Y., Sha, M., Hackmann, G., and Lu, C. (2012). Practical control of transmission power for wireless sensor networks. In Network Protocols (ICNP), 2012 20th IEEE International Conference on, pages 1-10. IEEE.
  12. Ganesan, D., Krishnamachari, B., Woo, A., Culler, D., Estrin, D., and Wicker, S. (2002). Complex behavior at scale: An experimental study of low-power wireless sensor networks. Technical report, Citeseer.
  13. Ganesh, A. and Xue, F. (2007). On the connectivity and diameter of small-world networks. Advances in Applied Probability, 39(4):853-863.
  14. Gao, Y., Hou, J., and Nguyen, H. (2008). Topology control for maintaining network connectivity and maximizing network capacity under the physical model. In IEEE INFOCOM, pages 1013-1021.
  15. Gnawali, O., Fonseca, R., Jamieson, K., Moss, D., and Levis, P. (2009). Collection tree protocol. In ACM SenSys, pages 1-14.
  16. Hackmann, G., Chipara, O., and Lu, C. (2008). Robust topology control for indoor wireless sensor networks. In ACM SenSys, pages 57-70. ACM.
  17. Hao, X.-C., Wang, M.-Q., Hou, S., Gong, Q.-Q., and Liu, B. (2015). Distributed topology control and channel allocation algorithm for energy efficiency in wireless sensor network: From a game perspective. Wireless Personal Communications, 80(4):1557-1577.
  18. Heikkinen, T. (2006). A potential game approach to distributed power control and scheduling. Computer Networks, 50(13):2295-2311.
  19. Horn, R. and Johnson, C. (2005). Matrix analysis. Cambridge university press.
  20. Jorswieck, E. and Boche, H. (2006). Majorization and matrix-monotone functions in wireless communications. Foundations and Trends in Communications and Information Theory, 3(6):553-701.
  21. Komali, R., MacKenzie, A., and Gilles, R. (2008). Effect of selfish node behavior on efficient topology design. IEEE Tran. Mobi. Comput., pages 1057-1070.
  22. Li, L., Halpern, J., Bahl, P., Wang, Y., and Wattenhofer, R. (2005a). A cone-based distributed topologycontrol algorithm for wireless multi-hop networks. IEEE/ACM Trans. Netw., 13(1):147-159.
  23. Li, N., Hou, J., and Sha, L. (2005b). Design and analysis of an MST-based topology control algorithm. IEEE Trans. Wireless Commun., 4(3):1195-1206.
  24. Lin, S., Zhang, J., Zhou, G., Gu, L., Stankovic, J., and He, T. (2006). ATPC: adaptive transmission power control for wireless sensor networks. In ACM SenSys, pages 223-236.
  25. Marshall, A. W., Olkin, I., and Arnold, B. (2010). Inequalities: theory of majorization and its applications. Springer Science & Business Media.
  26. Meshkati, F., Poor, H. V., Schwartz, S. C., and Balan, R. V. (2006). Energy-efficient power and rate control with qos constraints: a game-theoretic approach. In Proceedings of the 2006 international conference on Wireless communications and mobile computing, pages 1435-1440. ACM.
  27. Moeller, S., Sridharan, A., Krishnamachari, B., and Gnawali, O. (2010). Routing without routes: The backpressure collection protocol. In Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, pages 279- 290. ACM.
  28. Monderer, D. and Shapley, L. (1996). Potential games. Games and economic behavior, 14:124-143.
  29. Myerson, R. B. (1991). Game theory: analysis of conflict. Harvard University.
  30. Nahir, A., Orda, A., and Freund, A. (2008). Topology design and control: A game-theoretic perspective. In IEEE INFOCOM, pages 1620-1628.
  31. Nash Jr, J. (1950). The bargaining problem. Econometrica: Journal of the Econometric Society, pages 155-162.
  32. Neel, J. O., Reed, J. H., Gilles, R. P., et al. (2004). Convergence of cognitive radio networks. In WCNC, volume 4, pages 2250-2255.
  33. Nisan, N., Roughgarden, T., Tardos, E., and Vazirani, V. V. (2007). Algorithmic game theory, volume 1. Cambridge University Press Cambridge.
  34. Papadimitriou, C. H. (1994). On the complexity of the parity argument and other inefficient proofs of existence. Journal of Computer and system Sciences, 48(3):498- 532.
  35. Son, D., Krishnamachari, B., and Heidemann, J. (2004). Experimental study of the effects of transmission power control and blacklisting in wireless sensor networks. In Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004. 2004 First Annual IEEE Communications Society Conference on, pages 289-298. IEEE.
  36. Son, D., Krishnamachari, B., and Heidemann, J. (2005). Experimental study of the effects of transmission power control and blacklisting in wireless sensor networks. In IEEE SECON, pages 289-298. IEEE.
  37. Song, Y., Wong, S. H., and Lee, K.-W. (2011). A game theoretical approach to gateway selections in multidomain wireless networks. Gateways, 1:S1.
  38. Spyrou, E. D. and Mitrakos, D. K. (2015a). Approximating nash equilibrium uniqueness of power control in practical wsns. arXiv preprint arXiv:1512.05141.
  39. Spyrou, E. D. and Mitrakos, D. K. (2015b). On the homogeneous transmission power under the sinr model. In 4th International Conference on Telecommunications and Remote Sensing, ICTRS 2015.
  40. Srinivasan, K., Kazandjieva, M., Agarwal, S., and Levis, P. (2007). The beta-factor: Improving bimodal wireless networks. In ACM SenSys.
  41. Tan, Q., An, W., Han, Y., Liu, Y., Ci, S., Shao, F.-M., and Tang, H. (2015). Energy harvesting aware topology control with power adaptation in wireless sensor networks. Ad Hoc Networks, 27:44-56.
  42. Ui, T. (2008). Discrete concavity for potential games. International Game Theory Review, 10(01):137-143.
  43. Von Neumann, J., Morgenstern, O., Rubinstein, A., and Kuhn, H. (2007). Theory of games and economic behavior. Princeton Univ Pr.
  44. Yeung, M. and Kwok, Y. (2006). A game theoretic approach to power aware wireless data access. IEEE transactions on mobile computing, pages 1057-1073.
  45. Zhao, J. and Govindan, R. (2003). Understanding packet delivery performance in dense wireless sensor networks. In ACM SenSys, pages 1-13.
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Paper Citation


in Harvard Style

Spyrou E., Yang S. and Mitrakos D. (2017). Discrete Strategy Game-theoretic Topology Control inWireless Sensor Networks . In Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-211-0, pages 27-38. DOI: 10.5220/0006128700270038


in Bibtex Style

@conference{sensornets17,
author={Evangelos D. Spyrou and Shusen Yang and Dimitrios K. Mitrakos},
title={Discrete Strategy Game-theoretic Topology Control inWireless Sensor Networks},
booktitle={Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS,},
year={2017},
pages={27-38},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006128700270038},
isbn={978-989-758-211-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - Discrete Strategy Game-theoretic Topology Control inWireless Sensor Networks
SN - 978-989-758-211-0
AU - Spyrou E.
AU - Yang S.
AU - Mitrakos D.
PY - 2017
SP - 27
EP - 38
DO - 10.5220/0006128700270038