Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

A hybrid, game theory based, and distributed clustering protocol for wireless sensor networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Clustering has been well known as an effective way to reduce energy dissipation and prolong network lifetime in wireless sensor networks. Recently, game theory has been used to model clustering problem. Each node is modeled as a player which can selfishly choose its own strategies to be a cluster head (CH) or not. And by playing a localized clustering game, it gets an equilibrium probability to be a CH that makes its payoff keep equilibrium. In this paper, based on game theory, we present a clustering protocol named Hybrid, Game Theory based and Distributed clustering. In our protocol, we specifically define the payoff for each node when choosing different strategies, where both node degree and distance to base station are considered. Under this definition, each node gets its equilibrium probability by playing the game. And it decides whether to be a CH based on this equilibrium probability that can achieve a good trade-off between minimizing energy dissipation and providing the required services effectively. Moreover, an iterative algorithm is proposed to select the final CHs from the potential CHs according to a hybrid of residual energy and the number of neighboring potential CHs. Our iterative algorithm can balance the energy consumption among nodes and avoid the case that more than one CH occurs in a close proximity. And we prove it terminates in finite iterations. Simulation results show that our protocol outperforms LEACH, CROSS and LGCA in terms of network lifetime.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  2. Baronti, P., Pillai, P., Chook, V. W. C., Chessa, S., Gotta, A., & Hu, Y. F. (2007). Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards. Computer Communications, 30(7), 1655–1695.

    Article  Google Scholar 

  3. Yang, J., Zhang, C., Li, X., Huang, Y., Fu, S., & Acevedo, M. F. (2009). Integration of wireless sensor networks in environmental monitoring cyber infrastructure. Wireless Networks, 16(4), 1091–1108.

    Article  Google Scholar 

  4. Pogkas, N., Karastergios, G. E., Antonopoulos, C. P., Koubias, S., & Papadopoulos, G. (2007). Architecture design and implementation of an ad-hoc network for disaster relief operations. IEEE Transactions on Industrial Informatics, 3(1), 63–72.

    Article  Google Scholar 

  5. Pandian, P. S., Mohanavelu, K., Safeer, K. P., Kotresh, T. M., Shakunthala, D. T., Gopal, P., et al. (2008). Smart Vest: Wearable multi-parameter remote physiological monitoring system. Medical Engineering and Physics, 30(4), 466–477.

    Article  Google Scholar 

  6. Byun, J., Jeon, B., Noh, J., Kim, Y., & Park, S. (2012). An intelligent self-adjusting sensor for smart home services based on ZigBee communications. IEEE Transactions on Consumer Electronics, 58(3), 794–802.

    Article  Google Scholar 

  7. Yick, J., Mukherjee, B., Ghosal, D., & IEEE (2005). Analysis of a prediction-based mobility adaptive tracking algorithm. In 2nd International conference on broadband networks (broadnet), 2005 (pp. 809–816). doi:10.1109/icbn.2005.1589681.

  8. Bhende, M., Wagh, S. J., & Utpat, A. (2014). A quick survey on wireless sensor networks. In 2014 Fourth international conference on communication systems and network technologies, 2014 (pp. 160–167). doi:10.1109/csnt.2014.40.

  9. Tian, H., Shen, H., & Sang, Y. P. (2013). Maximizing network lifetime in wireless sensor networks with regular topologies. The Journal of Supercomputing, 69(2), 512–527.

    Article  Google Scholar 

  10. Chamam, A., & Pierre, S. (2010). A distributed energy-efficient clustering protocol for wireless sensor networks. Computers and Electrical Engineering, 36(2), 303–312.

    Article  MATH  Google Scholar 

  11. Liu, X. (2012). A survey on clustering routing protocols in wireless sensor networks. Sensors (Basel), 12(8), 11113–11153.

    Article  Google Scholar 

  12. Al-Karaki, J. N., & Kamal, A. E. (2004). Routing techniques in wireless sensor networks: A survey. IEEE Wireless Communications, 11(6), 6–28.

    Article  Google Scholar 

  13. Liu, X. X., & Shi, J. L. (2012). Clustering routing algorithms in wireless sensor networks: An overview. KSII Transactions on Internet and Information Systems,. doi:10.3837/tiis.2012.07.001.

    Google Scholar 

  14. Kulik, J., Heinzelman, W., & Balakrishnan, H. (2002). Negotiation-based protocols for disseminating information in wireless sensor networks. Wireless Networks, 8(2–3), 169–185.

    Article  MATH  Google Scholar 

  15. Ye, F., Chen, A., Liu, S., & Zhang, L. (2001). A scalable solution to minimum cost forwarding in large sensor networks. In Proceedings of the tenth international conference on computer communications and networks (ICCCN 2001) (pp. 304–309).

  16. Intanagonwiwat, C., Govindan, R., Estrin, D., & Heidemann, J. (2003). Directed diffusion for wireless sensor networking. IEEE/ACM Transactions on Networking, 11(1), 2–16.

    Article  Google Scholar 

  17. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences (pp. 1–10). doi:10.1109/HICSS.2000.926982.

  18. Lindsey, S., & Raghavendra C. S. (2002). PEGASIS: Power-efficient gathering in sensor information systems. In Proceedings of the IEEE aerospace conference (Vol. 3, pp. 1125–1130). Montana, USA.

  19. Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.

    Article  Google Scholar 

  20. Salim, A., Osamy, W., & Khedr, A. M. (2014). IBLEACH: Intra-balanced LEACH protocol for wireless sensor networks. Wireless Networks, 20(6), 1515–1525.

    Article  Google Scholar 

  21. Bsoul, M., Al-Khasawneh, A., Abdallah, A. E., Abdallah, E. E., & Obeidat, I. (2012). An energy-efficient threshold-based clustering protocol for wireless sensor networks. Wireless Personal Communications, 70(1), 99–112.

    Article  Google Scholar 

  22. Tang, F. L., You, I., Guo, S., Guo, M. Y., & Ma, Y. G. (2010). A chain-cluster based routing algorithm for wireless sensor networks. Journal of Intelligent Manufacturing, 23(4), 1305–1313.

    Article  Google Scholar 

  23. Xiao, G., Sun, N., Lv, L., Ma, J., & Chen, Y. (2015). An HEED-based study of cell-clustered algorithm in wireless sensor network for energy efficiency. Wireless Personal Communications, 81(1), 373–386.

    Article  Google Scholar 

  24. Jin, R. C., Gao, T., Song, J. Y., Zou, J. Y., & Wang, L. D. (2013). Passive cluster-based multipath routing protocol for wireless sensor networks. Wireless Networks, 19(8), 1851–1866.

    Article  Google Scholar 

  25. Akkarajitsakul, K., Hossain, E., Niyato, D., & Kim, D. I. (2011). Game theoretic approaches for multiple access in wireless networks: A survey. IEEE Communications Surveys And Tutorials, 13(3), 372–395.

    Article  Google Scholar 

  26. Charilas, D. E., & Panagopoulos, A. D. (2010). A survey on game theory applications in wireless networks. Computer Networks, 54(18), 3421–3430.

    Article  MATH  Google Scholar 

  27. Shi, H. Y., Wang, W. L., Kwok, N. M., & Chen, S. Y. (2012). Game theory for wireless sensor networks: A survey. Sensors, 12(7), 9055–9097.

    Article  Google Scholar 

  28. AlSkaif, T., Guerrero Zapata, M., & Bellalta, B. (2015). Game theory for energy efficiency in wireless sensor networks: Latest trends. Journal of Network and Computer Applications, 54, 33–61. doi:10.1016/j.jnca.2015.03.011.

    Article  Google Scholar 

  29. Koltsidas, G., & Pavlidou, F.-N. (2010). A game theoretical approach to clustering of ad-hoc and sensor networks. Telecommunication Systems, 47(1–2), 81–93.

    Google Scholar 

  30. Xie, D., Sun, Q., Zhou, Q., Qiu, Y., & Yuan, X. (2013). An efficient clustering protocol for wireless sensor networks based on localized game theoretical approach. International Journal of Distributed Sensor Networks,. doi:10.1155/2013/476313.

    Google Scholar 

  31. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.

    Article  Google Scholar 

  32. Yang, P. T., & Lee, S. (2012). A distributed reclustering hierarchy routing protocol using social welfare in wireless sensor networks 2012. International Journal of Distributed Sensor Networks,. doi:10.1155/2012/681026.

    Google Scholar 

  33. Bajaber, F., & Awan, I. (2013). An efficient cluster-based communication protocol for wireless sensor networks. Telecommunication Systems, 55(3), 387–401.

    Article  Google Scholar 

  34. Shokouhifar, M., & Jalali, A. (2015). A new evolutionary based application specific routing protocol for clustered wireless sensor networks. AEU International Journal of Electronics and Communications, 69(1), 432–441.

    Article  Google Scholar 

  35. Sabet, M., & Naji, H. R. (2015). A decentralized energy efficient hierarchical cluster-based routing algorithm for wireless sensor networks. AEU International Journal of Electronics and Communications, 69(5), 790–799.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Communication Engineering, Chongqing University, Chongqing, 400044, China

    Liu Yang, Yuan-Chang Zhong, Xue-Gang Wu & Shao-Jing Xing

  2. School of Electronic Information Engineering, Yangtze Normal University, Chongqing, 408100, China

    Yin-Zhi Lu

Authors
  1. Liu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yin-Zhi Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuan-Chang Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xue-Gang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shao-Jing Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuan-Chang Zhong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, L., Lu, YZ., Zhong, YC. et al. A hybrid, game theory based, and distributed clustering protocol for wireless sensor networks. Wireless Netw 22, 1007–1021 (2016). https://doi.org/10.1007/s11276-015-1011-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-015-1011-3

Keywords

Search

Navigation