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Energy and Network Balanced Distributed Clustering in Wireless Sensor Network

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Abstract

Balancing energy consumption of sensor nodes to extend the network lifetime is a major concern for the energy constrained wireless sensor network. Improper load balance and disproportionate energy consumption of sensor nodes during network activities such as transmitting and receiving of data cause energy hole problem and shorten the lifetime of the network. Many research works cited that clustering mechanism and the use of mobile sink can be effective to mitigate the energy hole problem and can improve the network lifetime. But using a mobile sink causes extra delay which is a major concern if the network is delay bound. In this work, we give deep insight into the problem of disproportionate energy consumption and aim to improve the network load balance and increase the network lifetime by applying efficient distributed clustering method with the help of a mobile sink. The proposed scheme named Energy Balanced Distributed Clustering Protocol (EBDCP) guarantees to transmit the sensed data to the base station within the tour deadline with the aid of a mobile sink. For this purpose, an efficient sojourn point determination algorithm has also been proposed. The simulation results prove that the proposed scheme performs significantly better than the existing works in terms of the energy distribution in the network, clustering overhead, residual energy of the network, number of alive nodes and network lifetime.

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References

  1. Abasıkeleş-Turgut, İ., & Hafif, O. G. (2016). Nodic: A novel distributed clustering routing protocol in WSNS by using a time-sharing approach for CH election. Wireless Networks, 22(3), 1023–1034.

    Article  Google Scholar 

  2. Abo-Zahhad, M., Ahmed, S. M., Sabor, N., & Sasaki, S. (2015). Mobile sink-based adaptive immune energy-efficient clustering protocol for improving the lifetime and stability period of wireless sensor networks. IEEE Sensors Journal, 15(8), 4576–4586.

    Article  Google Scholar 

  3. Ahmed, G., Zou, J., Fareed, M. M. S., & Zeeshan, M. (2016). Sleep-awake energy efficient distributed clustering algorithm for wireless sensor networks. Computers and Electrical Engineering, 56(Supplement C), 385–398.

    Article  Google Scholar 

  4. Al-Ma’aqbeh, F., Banimelhem, O., Taqieddin, E., Awad, F., & Mowafi, M. (2012). Fuzzy logic based energy efficient adaptive clustering protocol. In Proceedings of the 3rd international conference on information and communication systems, ICICS ’12 (pp. 21:1–21:5). New York, NY: ACM.

  5. Arghavani, M., Esmaeili, M., Esmaeili, M., Mohseni, F., & Arghavani, A. (2017). Optimal energy aware clustering in circular wireless sensor networks. Ad Hoc Networks, 65(Supplement C), 91–98.

    Article  Google Scholar 

  6. Gu, Y., Ren, F., Ji, Y., & Li, J. (2016). The evolution of sink mobility management in wireless sensor networks: A survey. IEEE Communications Surveys Tutorials, 18(1), 507–524. Firstquarter.

    Article  Google Scholar 

  7. 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 

  8. Jamalabdollahi, M., & Zekavat, S. A. R. (2015). Joint neighbor discovery and time of arrival estimation in wireless sensor networks via ofdma. IEEE Sensors Journal, 15(10), 5821–5833.

    Article  Google Scholar 

  9. Jia, D., Zhu, H., Zou, S., & Hu, P. (2016). Dynamic cluster head selection method for wireless sensor network. IEEE Sensors Journal, 16(8), 2746–2754.

    Article  Google Scholar 

  10. Laouid, A., Dahmani, A., Bounceur, A., Euler, R., Lalem, F., & Tari, A. (2017). A distributed multi-path routing algorithm to balance energy consumption in wireless sensor networks. Ad Hoc Networks, 64(Supplement C), 53–64.

    Article  Google Scholar 

  11. Liao, Y., Qi, H., & Li, W. (2013). Load-balanced clustering algorithm with distributed self-organization for wireless sensor networks. IEEE Sensors Journal, 13(5), 1498–1506.

    Article  Google Scholar 

  12. Manjeshwar, A., & Agrawal, D. P. (2001) Teen: A routing protocol for enhanced efficiency in wireless sensor networks. In Proceedings 15th international parallel and distributed processing symposium. IPDPS 2001 (pp. 2009–2015).

  13. Maróti, M., Kusy, B., Simon, G., & Lédeczi, Á. (2004). The flooding time synchronization protocol. In Proceedings of the 2nd international conference on embedded networked sensor systems, SenSys ’04 (pp. 39–49). ACM: New York, NY.

  14. Qing, L., Zhu, Q., & Wang, M. (2006). Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Computer Communications, 29(12), 2230–2237.

    Article  Google Scholar 

  15. Sabet, M., & Naji, H. (2016). An energy efficient multi-level route-aware clustering algorithm for wireless sensor networks: A self-organized approach. Computers and Electrical Engineering, 56(Supplement C), 399–417.

    Article  Google Scholar 

  16. Salarian, H., Chin, K. W., & Naghdy, F. (2014). An energy-efficient mobile-sink path selection strategy for wireless sensor networks. IEEE Transactions on Vehicular Technology, 63(5), 2407–2419.

    Article  Google Scholar 

  17. Sundareswaran, P., Vardharajulu, K. N., & Rajesh, R. S. (2015). Dech: Equally distributed cluster heads technique for clustering protocols in wsns. Wireless Personal Communications, 84(1), 137–151.

    Article  Google Scholar 

  18. Tong, M., & Tang, M. (2010 Sept) Leach-b: An improved leach protocol for wireless sensor network. In 2010 6th international conference on wireless communications networking and mobile computing (WiCOM) (pp. 1–4).

  19. Wang, J., Cao, Y., Li, B., Kim, H., & Lee, S. (2017). Particle swarm optimization based clustering algorithm with mobile sink for WSNS. Future Generation Computer Systems, 76(Supplement C), 452–457.

    Article  Google Scholar 

  20. Wang, W., Du, F., & Xu, Q. (2009 Sept) An improvement of leach routing protocol based on trust for wireless sensor networks. In 2009 5th international conference on wireless communications, networking and mobile computing (pp. 1–4).

  21. Yadav, R. K., Gupta, D., & Lobiyal, D. K. (2017). Energy efficient probabilistic clustering technique for data aggregation in wireless sensor network. Wireless Personal Communications, 96(3), 4099–4113.

    Article  Google Scholar 

  22. Yan, J., Zhou, M., & Ding, Z. (2016). Recent advances in energy-efficient routing protocols for wireless sensor networks: A review. IEEE Access, 4, 5673–5686.

    Article  Google Scholar 

  23. Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.

    Article  Google Scholar 

  24. Zhao, M., & Yang, Y. (2012). Bounded relay hop mobile data gathering in wireless sensor networks. IEEE Transactions on Computers, 61(2), 265–277.

    Article  MathSciNet  MATH  Google Scholar 

  25. Zhao, M., Yang, Y., & Wang, C. (2015). Mobile data gathering with load balanced clustering and dual data uploading in wireless sensor networks. IEEE Transactions on Mobile Computing, 14(4), 770–785.

    Article  Google Scholar 

  26. Zhu, C., Wu, S., Han, G., Shu, L., & Wu, H. (2015). A tree-cluster-based data-gathering algorithm for industrial wsns with a mobile sink. IEEE Access, 3, 381–396.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, 711103, India

    Srijit Chowdhury & Chandan Giri

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  1. Srijit Chowdhury
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  2. Chandan Giri
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Correspondence to Srijit Chowdhury.

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Chowdhury, S., Giri, C. Energy and Network Balanced Distributed Clustering in Wireless Sensor Network. Wireless Pers Commun 105, 1083–1109 (2019). https://doi.org/10.1007/s11277-019-06137-z

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  • DOI: https://doi.org/10.1007/s11277-019-06137-z

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