Advertisement

A Novel Approach for Cluster Head Selection By Applying Fuzzy Logic in Wireless Sensor Networks with Maintaining Connectivity

  • Aaditya JainEmail author
  • Bhuwnesh Sharma
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 98)

Abstract

The problem of increasing network lifetime by reducing energy consumption becomes more significant as the topology of the wireless sensor network is not fixed and sensor nodes are located randomly within the networks. This paper focuses on maintaining the network connectivity as long as possible. A clustering method that checks connectivity during topology formation is proposed. A fuzzy inference system is proposed with a specific consideration on the node energy, distance from base station and number of alive neighbours to decide the probability of a node, which has to be appointed a cluster head and also decides the size of cluster it may have.

Keywords

Cluster head selection Fuzzy inference system CUCF WSN Energy efficient protocol 

References

  1. 1.
    Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Hawaii International Conference on System Sciences, vol. 8, pp. 8020 (2000)Google Scholar
  2. 2.
    Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: An application-specific protocol architecture for wireless microsensor networks. IEEE Trans. Wirel. Commun. 1(4), 660–670 (2002)CrossRefGoogle Scholar
  3. 3.
    Kim, J., Byun, T.: A density-based clustering scheme for wireless sensor networks. In: Advanced Computer Science and Information Technology, pp. 267–276 (2011)Google Scholar
  4. 4.
    Hong, J., Kook, J., Lee, S., Kwon, D., Yi, S.: T-LEACH: the method of threshold-based cluster head replacement for wireless sensor networks. Inf. Systems Front. 11, 513–521 (2009)CrossRefGoogle Scholar
  5. 5.
    Ye, M., Li, C., Chen, G., Wu, J.: EECS: an energy efficient clustering scheme in wireless sensor networks. In: Proceedings of the 24th IEEE International Performance, Computing and Communications Conference (IPCCC), pp. 535–540 (2005)Google Scholar
  6. 6.
    Qing, L., Zhu, Q., Wang, M.: Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Comput. Commun. 29, 2230–2237 (2006)CrossRefGoogle Scholar
  7. 7.
    Xu, Z., Yin, Y., Wang, J.: A density-based energy-efficient routing algorithm in wireless sensor networks using game theory. Int. J. Future Gener. Commun. Netw. 5(4), 62–70 (2012)Google Scholar
  8. 8.
    Gupta, I., Riordan, D., Sampalli, S.: Cluster-head election using fuzzy logic for wireless sensor networks. In Proceedings of the 3rd Annual Communication Networks and Services Research Conference 2005, pp. 255–260 (2005)Google Scholar
  9. 9.
    Kim, J.M., Park, S.H., Han, Y.J., Chung, T.M.: CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks. In: Proceedings of the ICACT, pp. 654–659 (2008)Google Scholar
  10. 10.
    Bagci, H., Yazici, A.: An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Appl. Soft Comput. Sci. Publishers 13(4), 1741–1749 (2013)CrossRefGoogle Scholar
  11. 11.
    Baranidharan, B., Santhi, B.: DUCF: distributed load balancing Unequal Clustering in wireless sensor networks using Fuzzy approach. Appl. Soft Comput. 40, 495–506 (2016)CrossRefGoogle Scholar
  12. 12.
    Logambigai, R., Kannan, A.: Fuzzy logic based unequal clustering for wireless sensor networks. Wirel. Netw. 22(3), 945–957 (2015)CrossRefGoogle Scholar
  13. 13.
    Sert, S.A., Bagci, H., Yazici, A.: MOFCA: multi-objective fuzzy clustering algorithm for wireless sensor networks. Appl. Soft Comput. 30, 151–165 (2015)CrossRefGoogle Scholar
  14. 14.
    Shokouhifar, M., Jalali, A.: Optimized sugeno fuzzy clustering algorithm for wireless sensor networks. Eng. Appl. Artif. Intell. 16, 16–25 (2017)CrossRefGoogle Scholar
  15. 15.
    Wang, Y., Zhang, Y., Liu, J., Bhandari, R.: Coverage, connectivity, and deployment in wireless sensor networks. In: Patnaik, S., et al. (eds.) Recent Development in Wireless Sensor and Ad-hoc Networks, Signals and Communication Technology. Springer (2015)Google Scholar
  16. 16.
    Goratti, L., Baykas, T., Rasheed, T., Kato, S.: NACRP: a connectivity protocol for star topology wireless sensor networks. IEEE Wirel. Commun. Lett. 5(2), 120–123 (2016)CrossRefGoogle Scholar
  17. 17.
    Jain, A., Pardikar, V., Pratihast, S.R.: Tracing based solution for ubiquitous connectivity of mobile nodes for NDN: a RA kite. In: 8th IEEE International Conference on Computing, Communication and Networking Technologies, IIT, Delhi, PP. 1–7, 3–5 July.  https://doi.org/10.1109/icccnt.2017.8204191
  18. 18.
    Mekkis, P.-V., Kartsakli, E., Antonopoulos, A., Alonso, L., Verikoukis, C.: Connectivity analysis in clustered wireless sensor networks powered by solar energy. IEEE Trans. Wirel. Commun. 17(4), 2389–2401 (2018)CrossRefGoogle Scholar
  19. 19.
    Kuhn, F., Moscibroda, T., Wattenhofer, R.: Initializing newly deployed ad hoc and sensor networks. In: Proceedings of the 10th Annual International Conference on Mobile Computing and Networking, PP. 260–274 (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of CSER. N. Modi Engineering College, Rajasthan Technical UniversityKotaIndia

Personalised recommendations