Wireless Personal Communications

, Volume 97, Issue 3, pp 4145–4159 | Cite as

Energy-Balanced Strategy for Wireless Sensor Networks by Utilizing Complex Networks Synchronization Theory

  • Jing ZhangEmail author
  • Xin Feng
  • Zhuang Liu


The network lifetime is limited by key nodes in wireless sensor networks (WSNs). These nodes around the base station undertake a heavy load and die earlier than the others, so an energy hole emerges. In this study, an energy-balanced strategy that contributes to redistributing energy consumption of the network is proposed for WSNs through the innovative use of complex networks synchronization theory. The betweenness centrality in complex networks is used to measure the importance of the node in the network. Constructing energy consumption synchronization functions and decreasing the maximum betweenness of the node in this study improves the synchronization capacity of energy consumption and weakens the effect of key nodes on the performance of the network, the effect of key edges is also investigated in this study. Experiment results show that compared with other algorithms, the proposed strategy delays the appearance of the first death node, therefore prolongs the lifetime of the network effectively and helps achieve balance in the energy consumption of nodes.


Wireless sensor network Complex networks synchronization theory Betweenness centrality Energy-balanced Network lifetime 



The authors express sincere appreciation to the editors and the anonymous reviewers for their helpful comments.

Funding was provided by People’s Government of Jilin Province (Grant No. 20140204063GX).


  1. 1.
    Ramesh, M. V. (2014). Design, development, and deployment of a wireless sensor network for detection of landslides. Ad Hoc Networks, 13(SI), 2–18.CrossRefGoogle Scholar
  2. 2.
    Emanuele, I., Giovanni, G., Francesco, M., et al. (2012). Design and implementation of a landslide early warning system. Engineering Geology, 147(2012), 124–136.Google Scholar
  3. 3.
    Zulkifli, C. Z., Hassan, H. N., Ismail, W., et al. (2015). Embedded RFID and wireless mesh sensor network materializing automated production line monitoring. Acta Physica Polonica A, 128(2B), B86–B89.CrossRefGoogle Scholar
  4. 4.
    Jaguey, J. G., Villa-Medina, J. F., Lopez-Guzman, A., et al. (2015). Smartphone Irrigation Sensor. IEEE Sensors Journal, 15(9), 5122–5127.CrossRefGoogle Scholar
  5. 5.
    Unluturk, M. S., & Kaan, K. (2012). Integration of RFID and web service for assisted living. Journal of Medical Systems., 36(4), 2371–2377.CrossRefGoogle Scholar
  6. 6.
    Zhang, Z., Udyant, K., & Michael, N., et al. (2011). Design of an unobtrusive wireless sensor network for nighttime falls detection, In IEEE engineering in medicine and biology society conference proceedings (pp 5275–5278).Google Scholar
  7. 7.
    Li, J., & Mohapatra, P. (2005). An analytical model for the energy hole problem in many-to-one sensor networks. In IEEE VTS vehicular technology conference proceedings (pp. 2721–2725).Google Scholar
  8. 8.
    Nishikawa, T., Motter, A. E., Lai, Y. C., & Hoppensteadt, F. C. (2003). Heterogeneity in oscillator networks: are smaller worlds easier to synchronize? Physical Review Letters, 91(1), 014101.CrossRefGoogle Scholar
  9. 9.
    Hong, H., Kim, B. J., Choi, M. Y., & Park, H. (2004). Factors that predict better synchronizability on complex networks. Physical Review E, 69(6), 067105.CrossRefGoogle Scholar
  10. 10.
    Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of small-world networks. Nature, 393(6684), 440–442.CrossRefGoogle Scholar
  11. 11.
    Helmy, A. (2003). Small Worlds in wireless networks. IEEE Communications Letters, 7(10), 490–492.CrossRefGoogle Scholar
  12. 12.
    Zheng, G., & Liu, Q. (2010). Overview of small world network model in wireless sensor networks. Journal of Computational Information Systems, 6(10), 3471–3479.Google Scholar
  13. 13.
    Hawick, K. A., & James, H. A. (2003). Small-world effects in wireless agent networks. In Technical report CSTN-001 (pp. 155–164).Google Scholar
  14. 14.
    Meng, A., & Chen, P. (2008). Construction to heterogeneous sensor networks based on effect of small world. Computer & Digital Engineering, 36(9), 98–100.Google Scholar
  15. 15.
    Liu, M. (2007). Approach to construction of wireless sensor networks with small world effect. Electronic Measurement Technology, 30(4), 37–40.Google Scholar
  16. 16.
    Matthias, R. (2007). Small worlds: Strong clustering in wireless networks. Networking and Internet Architecture, 6(3), 1–10.Google Scholar
  17. 17.
    Zhang, X. (2009). Model design of wireless sensor network based on scale-free network theory. In 5th international conference on wireless communications, networking and mobile computing, WiCOM (pp. 1–4).Google Scholar
  18. 18.
    Zhu, H., Luo, H., Peng, H., Li, L., & Luo, Q. (2009). Complex networks-based energy-efficient evolution model for wireless sensor networks. Chaos, Solitons and Fractals, 41, 1828–1835.CrossRefGoogle Scholar
  19. 19.
    Parth, H., & Rudra Dutta, P. (2012). Centrality-based power control for hot-spot mitigation in multi-hop wireless networks. Computer Communications, 35, 1074–1085.CrossRefGoogle Scholar
  20. 20.
    Cuzzocrea, A., Papadimitriou, A., Katsaros, D., et al. (2012). Edge betweenness centrality: A novel algorithm for QoS-based topology control over wireless sensor networks. Journal of Network and Computer Applications, 35(2012), 1210–1217.CrossRefGoogle Scholar
  21. 21.
    Zhang, D., Li, G., Zheng, K., Ming, X., & Pan, Z.-H. (2014). An energy-balanced routing method based on forward-aware factor for wireless sensor networks. IEEE Transactions on Industrial Informatics, 10(1), 766–773.CrossRefGoogle Scholar
  22. 22.
    Chen, G., Li, C., Ye, M., & Jie, W. (2009). An unequal cluster-based routing protocol in wireless sensor networks. Wireless Networks, 15, 193–207.CrossRefGoogle Scholar
  23. 23.
    Zhao, H., Guo, S., Wang, X., et al. (2015). Energy-efficient topology control algorithm for maximizing networklifetime in wireless sensor networks with mobile sink. Applied Soft Computing, 34, 539–550.CrossRefGoogle Scholar
  24. 24.
    Tunca, C., Isik, S., Donmez, M. Y., et al. (2015). Ring routing: An energy-efficient routing protocol for wireless sensor networks with a mobile sink. IEEE Transactions on Mobile Computing, 14(9), 1947–1960.CrossRefGoogle Scholar
  25. 25.
    Liu, T., Li, Q., & Liang, P. (2012). An energy-balancing clustering approach for gradient-based routing in wireless sensor networks. Computer Communications, 35, 2150–2161.CrossRefGoogle Scholar
  26. 26.
    Abd, M. A., Majed Al-Rubeaai, S. F., Singh, B. K., et al. (2015). Extending wireless sensor network lifetime with global energy balance. IEEE Sensors Journal, 15(9), 5053–5063.CrossRefGoogle Scholar
  27. 27.
    Zhang, J., Liu, Y., & Li, B., et al. (2015). Achieving energy-balance with unequal clustering under single-hop transmission in wireless sensor networks. In The 14th international conference on wireless networks, ICWN’15 (pp. 146–152).Google Scholar
  28. 28.
    Chen, L., Mao, Y., Chen, D., et al. (2007). Topology control of wireless sensor networks under an average degree constraint. Chinese Journal of computer, 30(9), 1544–1550.MathSciNetGoogle Scholar
  29. 29.
    Heinzelman, W. B. (2000). Application-specific protocol architectures for wireless networks. PhD thesis, Massachusetts Institute of Technology.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyChang Chun University of Science and TechnologyChangchunChina

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