Energy Efficient Clustering and Congestion Control in WSNs with Mobile Sinks

  • Mohammad MasdariEmail author


Large scale Wireless Sensor Networks (WSNs) often utilize multiple mobile sink nodes to improve the network lifetime and scalability. However, most of the studies conducted in this context, consider unlimited buffer capacity for the sink nodes. But, this model cannot truly describe the behavior of WSNs and causes congestion in the sink nodes. To solve this problem, in this paper, we use limited buffer capacity for each mobile sink node in WSNs and present a two-level Fuzzy Logic Controller (FLC)-based dynamic clustering scheme and congestion prevention. In this scheme, sink nodes try to predict current load based on their loads in previous rounds by using ARIMA method and based on it, the first FLC selects the nearest uncongested sink node from multiple available mobile sink nodes. Then, the second FLC applies the output of the first FLC to select appropriate nodes as cluster heads to mitigate the energy consumption in the network. Extensive simulation results indicate the effectiveness of the proposed fuzzy logic-based solution in reducing congestion in the mobile sink nodes and improving load balancing in them which these result in the network lifetime improvement and decreasing the number of retransmissions.


WSN Sink selection Clustering Fuzzy logic Load balancing Energy 



  1. 1.
    Masdari, M., & Ahmadzadeh, S. (2017). A survey and taxonomy of the authentication schemes in telecare medicine information systems. Journal of Network and Computer Applications, 87, 1–19.CrossRefGoogle Scholar
  2. 2.
    Masdari, M., Ahmadzadeh, S., & Bidaki, M. (2017). Key Management in Wireless Body Area Network: Challenges and Issues. Journal of Network and Computer Applications, 91, 36–51.CrossRefGoogle Scholar
  3. 3.
    Masdari, M., & Ahmadzadeh, S. (2016). Comprehensive analysis of the authentication methods in wireless body area networks. Security and Communication Networks, 9, 4777–4803.CrossRefGoogle Scholar
  4. 4.
    Masdari, M., Bazarchi, S. M., & Bidaki, M. (2013). Analysis of secure LEACH-based clustering protocols in wireless sensor networks. Journal of Network and Computer Applications, 36, 1243–1260.CrossRefGoogle Scholar
  5. 5.
    Gherbi, C., Aliouat, Z., & Benmohammed, M. (2016). An adaptive clustering approach to dynamic load balancing and energy efficiency in wireless sensor networks. Energy, 114, 647–662.CrossRefGoogle Scholar
  6. 6.
    Godbole, V. (2012). FCA-an approach on leach protocol of wireless sensor networks using fuzzy logic. International Journal of Computer Communications and Networks (IJCCN), 3, 1–13.Google Scholar
  7. 7.
    Lee, J.-S., & Cheng, W.-L. (2012). Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication. IEEE Sensors Journal, 12, 2891–2897.CrossRefGoogle Scholar
  8. 8.
    Mhemed, R., Aslam, N., Phillips, W., & Comeau, F. (2012). An energy efficient fuzzy logic cluster formation protocol in wireless sensor networks. Procedia Computer Science, 10, 255–262.CrossRefGoogle Scholar
  9. 9.
    Nayak, P., & Devulapalli, A. (2016). A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sensors Journal, 16, 137–144.CrossRefGoogle Scholar
  10. 10.
    Singh, A. K., Purohit, N., & Varma, S. (2013). Fuzzy logic based clustering in wireless sensor networks: a survey. International Journal of Electronics, 100, 126–141.CrossRefGoogle Scholar
  11. 11.
    Logambigai, R., & Kannan, A. (2016). Fuzzy logic based unequal clustering for wireless sensor networks. Wireless Networks, 22, 945–957.CrossRefGoogle Scholar
  12. 12.
    Mao, S., Zhao, C., Zhou, Z., & Ye, Y. (2013). An improved fuzzy unequal clustering algorithm for wireless sensor network. Mobile Networks and Applications, 18, 206–214.CrossRefGoogle Scholar
  13. 13.
    Nguyen, T.-T., Shieh, C.-S., Dao, T.-K., Wu, J.-S., & Hu, W.-C. (2013). Prolonging of the network lifetime of WSN using fuzzy clustering topology. In 2013 second international conference on robot, vision and signal processing (pp. 13–16).Google Scholar
  14. 14.
    Gajjar, S., Sarkar, M., & Dasgupta, K. (2016). FAMACROW: fuzzy and ant colony optimization based combined mac, routing, and unequal clustering cross-layer protocol for wireless sensor networks. Applied Soft Computing, 43, 235–247.CrossRefGoogle Scholar
  15. 15.
    Masdari, M., & Jalali, M. (2016). A survey and taxonomy of DoS attacks in cloud computing. Security and Communication Networks, 9, 3724–3751.CrossRefGoogle Scholar
  16. 16.
    Masdari, M., Salehi, F., Jalali, M., & Bidaki, M. (2017). A survey of PSO-based scheduling algorithms in cloud computing. Journal of Network and Systems Management, 25, 1–37.CrossRefGoogle Scholar
  17. 17.
    Masdari, M., Nabavi, S. S., & Ahmadi, V. (2016). An overview of virtual machine placement schemes in cloud computing. Journal of Network and Computer Applications, 66, 106–127.CrossRefGoogle Scholar
  18. 18.
    Masdari, M., ValiKardan, S., Shahi, Z., & Azar, S. I. (2016). Towards workflow scheduling in cloud computing: a comprehensive analysis. Journal of Network and Computer Applications, 66, 64–82.CrossRefGoogle Scholar
  19. 19.
    Santos, A. C., Duhamel, C., & Belisário, L. S. (2016). Heuristics for designing multi-sink clustered WSN topologies. Engineering Applications of Artificial Intelligence, 50, 20–31.CrossRefGoogle Scholar
  20. 20.
    Isik, S., Donmez, M. Y., & Ersoy, C. (2012). Multi-sink load balanced forwarding with a multi-criteria fuzzy sink selection for video sensor networks. Computer Networks, 56, 615–627.CrossRefGoogle Scholar
  21. 21.
    Jain, T. K., Saini, D. S., & Bhooshan, S. V. (2015). Lifetime optimization of a multiple sink wireless sensor network through energy balancing. Journal of Sensors 2015. Scholar
  22. 22.
    Box, G. E., & Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American statistical Association, 65, 1509–1526.MathSciNetCrossRefGoogle Scholar
  23. 23.
    Luo, D., Zuo, D., & Yang, X. (2008). An optimal sink selection scheme for multi-sink wireless sensor networks. In ICCSIT’08. international conference on computer science and information technology, 2008 (pp. 544–548)Google Scholar
  24. 24.
    Ghaffari, A. (2015). Congestion control mechanisms in wireless sensor networks: A survey. Journal of Network and Computer Applications, 52, 101–115.CrossRefGoogle Scholar
  25. 25.
    Fang, W.-W., Chen, J.-M., Shu, L., Chu, T.-S., & Qian, D.-P. (2010). Congestion avoidance, detection and alleviation in wireless sensor networks. Journal of Zhejiang University Science C, 11, 63–73.CrossRefGoogle Scholar
  26. 26.
    Silva, A. P., Burleigh, S., Hirata, C. M., & Obraczka, K. (2015). A survey on congestion control for delay and disruption tolerant networks. Ad Hoc Networks, 25, 480–494.CrossRefGoogle Scholar
  27. 27.
    Bagci, H., & Yazici, A. (2013). An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Applied Soft Computing, 13, 1741–1749.CrossRefGoogle Scholar
  28. 28.
    Taheri, H., Neamatollahi, P., Younis, O. M., Naghibzadeh, S., & Yaghmaee, M. H. (2012). An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic. Ad Hoc Networks, 10, 1469–1481.CrossRefGoogle Scholar
  29. 29.
    Sert, S. A., Bagci, H., & Yazici, A. (2015). MOFCA: Multi-objective fuzzy clustering algorithm for wireless sensor networks. Applied Soft Computing, 30, 151–165.CrossRefGoogle Scholar
  30. 30.
    Baranidharan, B., & Santhi, B. (2016). DUCF: Distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach. Applied Soft Computing, 40, 495–506.CrossRefGoogle Scholar
  31. 31.
    Ran, G., Zhang, H., & Gong, S. (2010). Improving on LEACH protocol of wireless sensor networks using fuzzy logic. Journal of Information and Computational Science, 7, 767–775.Google Scholar
  32. 32.
    Soro, S., & Heinzelman, W. B. (2005). Prolonging the lifetime of wireless sensor networks via unequal clustering. In 19th IEEE international parallel and distributed processing symposium, 2005. Proceedings Google Scholar
  33. 33.
    Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7, 1–13.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Engineering, Urmia BranchIslamic Azad UniversityUrmiaIran

Personalised recommendations