Skip to main content

Advertisement

SpringerLink
Log in

A game theory based energy efficient clustering routing protocol for WSNs

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

The energy constraint is one of the inherent defects of the Wireless Sensor Networks (WSNs). How to prolong the lifespan of the network has attracted more and more attention. Numerous achievements have emerged successively recently. Among these mechanisms designing routing protocols is one of the most promising ones owing to the large amount of energy consumed for data transmission. The background and related works are described firstly in detail in this paper. Then a game model for selecting the Cluster Head is presented. Subsequently, a novel routing protocol named Game theory based Energy Efficient Clustering routing protocol (GEEC) is proposed. GEEC, which belongs to a kind of clustering routing protocols, adopts evolutionary game theory mechanism to achieve energy exhaust equilibrium as well as lifetime extension at the same time. Finally, extensive simulation experiments are conducted. The experimental results indicate that a significant improvement in energy balance as well as in energy conservation compared with other two kinds of well-known clustering routing protocols is achieved.

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

Access this article

Log in via an institution

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
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Du, X., Xiao, Y., & Dai, F. (2008). Increasing Network lifetime by balancing node energy consumption in heterogeneous sensor networks. Wireless Communication and Mobile Computing, 8(1), 125–136.

    Article  Google Scholar 

  2. Wadaa, A., Olariu, S., Wilson, L., Jones, K., & Eltoweissy, M. (2003). On training a sensor network. In Proceedings of parallel and distributed processing symposium.

  3. Sheng, Z., Yang, S., Yu, Y., et al. (2013). A survey on the ietf protocol suite for the internet of things: standards, challenges, and opportunities. Wireless Communications IEEE, 20(6), 91–98.

    Article  Google Scholar 

  4. Zheng, Y., Peng, Z., & Vasilakos, A. V. (2014). A survey on trust management for internet of things. Journal of Network & Computer Applications, 42(3), 120–134.

    Google Scholar 

  5. Jing, Q., Vasilakos, A. V., Wan, J., et al. (2014). Security of the internet of things: Perspectives and challenges. Wireless Networks, 20(8), 2481–2501.

    Article  Google Scholar 

  6. Acampora, G., Gaeta, M., Loia, V., et al. (2010). Interoperable and adaptive fuzzy services for ambient intelligence applications. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 5(2), 737–744.

    Google Scholar 

  7. Rahimi, M. R., Ren, J., Liu, C. H., et al. (2014). Mobile cloud computing: A survey, state of art and future directions. Mobile Networks & Applications, 19(2), 133–143.

    Article  Google Scholar 

  8. Chen, M., Gonzalez, S., Vasilakos, A. V., et al. (2011). Body area networks: A survey. Mobile Networks & Applications, 16(2), 171–193.

    Article  Google Scholar 

  9. Efthymiou, C., Nikoletseas, S., & Rolim, J. (2006). Energy balanced data propagation in wireless sensor networks. Wireless Networks, 12(6), 691–707.

    Article  Google Scholar 

  10. Ren, F., Zhang, J., He, T., Lin, C., & Das, S. K. (2012). EBRP: Energy-balanced routing protocol for data gathering in wireless sensor network. IEEE Transactions on Parallel and Distributed Systems, 22(12), 2108–2125.

    Article  Google Scholar 

  11. Han, K., Luo, H., Liu, Y., & Vasilakos, A. V. (2013). Algorithm design for data communications in duty-cycled wireless sensor networks: A survey. IEEE Communications Magazine, 51(7), 107–113.

    Article  Google Scholar 

  12. Xiao, Y., Peng, M., Gibson, J., Xie, G. G., et al. (2012). Tight performance bounds of multihop fair access for MAC protocols in wireless sensor networks and underwater sensor networks. IEEE Transactions on Mobile Computing, 11(10), 1538–1554.

    Article  Google Scholar 

  13. Sengupta, S., Das, S., Nasir, M., Vasilakos, A. V., & Pedrycz, W. (2012). An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Transactions on Systems Man & Cybernetics Part C, 42(6), 1093–1102.

    Article  Google Scholar 

  14. Zhang, X. M., Zhang, Y., Yan, F., et al. (2015). Interference-based topology control algorithm for delay-constrained mobile ad hoc networks. IEEE Transactions on Mobile Computing, 14(4), 742–754.

    Article  Google Scholar 

  15. Chang, C., & Chang, H. (2008). Energy-aware node placement, topology control and MAC scheduling for wireless sensor networks. Computer Networks, 52(11), 2189–2204.

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  17. Li, M., Li, Z., & Vasulakos, A. V. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557.

    Article  Google Scholar 

  18. Halder, S., Ghosal, A., & Bit, S. D. (2011). A pre-determined node deployment strategy to prolong network lifetime in wireless sensor network. Computer Communication, 34(11), 1294–1306.

    Article  Google Scholar 

  19. Wu, X., Chen, G., & Das, S. K. (2008). Avoiding energy holes in wireless sensor networks with nonuniform node distribution. IEEE Transactions on Parallel and Distributed System, 19(5), 710–720.

    Article  Google Scholar 

  20. Wei, Dali, Jin, Yichao, Vural, S., Moessner, K., & Tafazolli, R. (2011). An energy-efficient clustering solution for wireless sensor network. IEEE Transactions on Wireless Communication, 10(11), 3973–3983.

    Article  Google Scholar 

  21. Zhou, H., Wu, Y., Hu, Y., & Xie, G. (2010). A novel stable selection and reliable transmission protocol for clustered heterogeneous wireless sensor networks. Computer Communication, 33(15), 1843–1849.

    Article  Google Scholar 

  22. Khalil, E. A., & Attea, B. A. (2011). Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm and Evolutoinary Computation, 1(4), 195–203.

    Article  Google Scholar 

  23. Liu, T., Li, Q., & Liang, P. (2012). An energy-balancing clustering approach for gradient-based routing in wireless sensor networks. Computer Communication, 35(17), 2150–2161.

    Article  Google Scholar 

  24. Huang, Y., Su, B., & Wang, M. (2008). Localized and load-balanced clustering for energy saving in wireless sensor networks. International Journal of Communication Systems, 21(8), 799–814.

    Article  Google Scholar 

  25. Singh, B., & Lobiyal, D. K. (2012). An energy-efficient adaptive clustering algorithm with load balancing for wireless sensor network. International Journal of Sensor Networks, 12(1), 37–52.

    Article  Google Scholar 

  26. Hong, J., Kook, J., Lee, S., Kwon, D., & Yi, S. (2009). T-LEACH: The method of threshold-based cluster head replacement for wireless sensor networks[J]. Information System Frontiers, 11(5), 513–521.

    Article  Google Scholar 

  27. Lindsey, S., & Raghavendra, C. S. (2001). PEGASIS: Power-efficient gathering in sensor information systems. In Proceedings of IEEE aerospace conference (pp. 1125–1130).

  28. Liu, Y., Xiong, N., Zhao, Y., Vasilakos, A. V., Gao, J., et al. (2010). Multi-layer clustering routing algorithm for wireless vehicular sensor networks. IET Communications, 4(7), 810–816.

    Article  Google Scholar 

  29. Meng, T., Yang, Z., Chen, G., et al. (2015). Spatial reusability-aware routing in multi-hop wireless networks. IEEE Transactions on Computers,. doi:10.1109/TC.2417543.

    Google Scholar 

  30. Yao, Y., Cao, Q., & Vasilakos, A. V. (2015). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking, 23(6), 810–823.

    Article  Google Scholar 

  31. Zeng, Y., Xiang, K., Li, D., & Vasilakos, A. V. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.

    Article  Google Scholar 

  32. Busch, C., Kannan, R., & Vasilakos, A. V. (2012). Approximating congestion + dilation in networks via “quality of routing” games. IEEE Transactions on Computers, 61(99), 1.

    MathSciNet  Google Scholar 

  33. Lin, J., Xiong, N., Vasilakos, A. V., Chen, G., & Guo, W. (2011). Evolutionary game-based data aggregation model for wireless sensor networks. IET Communications, 5(12), 1691–1697.

    Article  MathSciNet  Google Scholar 

  34. Kim, S. (2011). Adaptive online power control scheme based on the evolutionary game theory. IET Communication, 5(18), 2648–2655.

    Article  MathSciNet  MATH  Google Scholar 

  35. Xie, S. (2001). Economic game theory. Shanghai: Fudan University Press.

    Google Scholar 

  36. Yang, M., Li, Y., Lin, D., et al. (2014). Software-defined and virtualized future mobile and wireless networks: a survey. Mobile Networks & Applications, 20(1), 4–18.

    Article  Google Scholar 

  37. Xiang, L., Luo, J., & Vasilakos, AV. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In Proceedings of the 8th annual IEEE communications society conference on sensor, mesh and Ad hoc communications and networks (SECON’11) (pp. 46–54).

  38. Liu, X. Y., Zhu, Y., Kong, L., Liu, C., Gu, Y., et al. (2015). CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26(8), 2188–2197.

    Article  Google Scholar 

  39. Xu, X., Ansari, R., Khokhar, A., & Vasilakos, A. V. (2015). Hierarchical data aggregation using compressive sensing (HDACS) in WSNs. ACM Transactions on Sensor Networks, 11(3), 1–25.

    Article  Google Scholar 

  40. Wei, G., Ling, Y., Guo, B., Xiao, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: combining grey model and kalman filter. Computer Communications, 34(6), 793–802.

    Article  Google Scholar 

  41. Mandala, D., Du, X., Dai, F., & You, C. (2008). Load balance and energy efficient data gathering in wireless sensor networks[J]. Wireless Communication and Mobile Computing, 8(5), 645–659.

    Article  Google Scholar 

  42. Chilamkurti, N., Zeadally, S., Vasilakos, A., & Sharma, V. (2009). Cross-layer support for energy efficient routing in wireless sensor networks. Journal of Sensors, 2009(2009), 357–361.

    Google Scholar 

  43. Bhuiyan, M. Z. A., Wang, G., & Vasilakos, A. V. (2015). Local area prediction-based mobile target tracking in wireless sensor networks. IEEE Transactions on Computers, 64(6), 1968–1982.

    Article  MathSciNet  Google Scholar 

  44. Song, Y., Liu, L., Ma, H., & Vasilakos, A. V. (2014). A biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network and Service Management, 11(3), 417–430.

    Article  Google Scholar 

  45. Liu, L., Song, Y., Zhang, H., et al. (2015). Physarum optimization: A biology-inspired algorithm for the steiner tree problem in networks. IEEE Transactions on Computers, 64(3), 819–832.

    MathSciNet  Google Scholar 

  46. Zhou, L., Xiong, N., Shu, L., et al. (2010). Context-aware middleware for multimedia services in heterogeneous networks. IEEE Intelligent Systems, 25(2), 40–47.

    Article  Google Scholar 

  47. Ok, C., Lee, S., Mitra, P., & Kumara, S. (2009). Distributed energy balanced routing for wireless sensor networks. Computer and Industrial Engineering, 57(1), 125–135.

    Article  Google Scholar 

  48. Ok, C., Lee, S., Mitra, P., & Kumara, S. (2010). Distributed routing in wireless sensor networks using energy welfare metric. Information Sciences, 180(9), 1656–1670.

    Article  Google Scholar 

  49. Yang, Y., Fonoage, M. I., & Cardei, M. (2010). Improving network lifetime with mobile wireless sensor networks. Computer Communications, 33(4), 409–419.

    Article  Google Scholar 

  50. Alshawi, I. S., Yan, L., Pan, W., & Luo, B. (2012). Lifetime enhancement in wireless sensor networks using fuzzy approach and A-star algorithm. IEEE Sensors Journal, 12(10), 3010–3018.

    Article  Google Scholar 

  51. Vasilakos, A. V., Li, Z., Simon, G., et al. (2015). Information centric network: Research challenges and opportunities. Journal of Networks & Computer Applications, 52, 1–10.

    Article  Google Scholar 

  52. Tao, M., Lu, D., & Yang, J. (2012). An adaptive energy-aware multi-path routing protocol with load balance for wireless sensor networks. Wireless Personal Communications, 63(4), 823–846.

    Article  Google Scholar 

  53. Lin, D., Wang, Q., Lin, D., et al. (2015). An energy-efficient clustering routing protocol based on evolutionary game theory in wireless sensor networks[J]. International Journal of Distributed Sensor Networks,. doi:10.1155/2015/40593.

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank the reviewers for their detailed reviews and constructive comments, which have helped to improve the quality of this paper. This work is supported by the National Natural Science Foundation of China (Program ID 61572385).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, No. 2, South Taibai Road, Yanta District, Xi’an, Shannxi, China

    Deyu Lin & Quan Wang

Authors
  1. Deyu Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Quan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deyu Lin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, D., Wang, Q. A game theory based energy efficient clustering routing protocol for WSNs. Wireless Netw 23, 1101–1111 (2017). https://doi.org/10.1007/s11276-016-1206-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-016-1206-2

Keywords

Search

Navigation