Skip to main content

Advertisement

Log in

FSCVG: A Fuzzy Semi-Distributed Clustering Using Virtual Grids in WSN

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless sensor network comprises of tiny devices which are powered by limited energy resources. Therefore, providing methods to reduce energy consumption is essential to develop this sort of networks. Clustering is one of the major techniques which is introduced to increase wireless sensor network lifetime through providing hierarchy structure. This article represents a semi-distributed fuzzy algorithm to cluster homogeneous nodes by using virtual grids in wireless sensor networks. First phase of FSCVG clustering includes selecting the initial cluster heads and determining virtual grids which are done in a centralized approach by the base station. The second phase follows a distributed approach, as all of the nodes involve in the cluster head selection process. FSCVG uses remaining energy, distance to base station and centrality as the fuzzy logic parameters to select the cluster heads in both phases. FSCVG utilizes multi-hop cluster based routing and also an adaptive threshold with the aim of prolonging of network lifetime. FSCVG algorithm is compared to other methods in five scenarios. The assessment criteria used in the comparison include the network remaining energy, the number of dead nodes, the first dead node, half of dead nodes and the last dead node. The results show that proposed algorithm could reduce network energy consumption and prolong network lifetime.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

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
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28

Similar content being viewed by others

References

  1. Lin, H., Wang, L., & Kong, R. (2015). Energy efficient clustering protocol for large-scale sensor networks. IEEE Sensors Journal, 15(12), 7150–7160.

    Article  Google Scholar 

  2. Elhoseny, M., Farouk, A., Zhou, N., Wang, M. M., Abdalla, S., & Batle, J. (2017). Dynamic multi-hop clustering in a wireless sensor network: Performance improvement. Wireless Personal Communications, 95(4), 3733–3753.

    Article  Google Scholar 

  3. Nayak, P., & Devulapalli, A. (2015). A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sensors Journal, 16(1), 137–144.

    Article  Google Scholar 

  4. WohweSambo, D., Yenke, B. O., Förster, A., & Dayang, P. (2019). Optimized clustering algorithms for large wireless sensor networks: A review. Sensors, 19(2), 322.

    Article  Google Scholar 

  5. Yenke, B. O., Sambo, D. W., Ari, A. A. A., & Gueroui, A. (2016). MMEDD: Multithreading model for an efficient data delivery in wireless sensor networks. International Journal of Communication Networks and Information Security, 8(3), 179.

    Google Scholar 

  6. Fanian, F., & Rafsanjani, M. K. (2019). Cluster-based routing protocols in wireless sensor networks: A survey based on methodology. Journal of Network and Computer Applications, 142, 111–142.

    Article  Google Scholar 

  7. Bagci, H., & Yazici, A. (2010). An energy aware fuzzy unequal clustering algorithm for wireless sensor networks. In International conference on fuzzy systems (pp. 1–8). IEEE.

  8. Harizan, S., & Kuila, P. (2020). Evolutionary algorithms for coverage and connectivity problems in wireless sensor networks: A study. In: Design frameworks for wireless networks (pp. 257–280). Springer, Singapore

  9. Phoemphon, S., So‑In, C., Aimtongkham, P., & Nguyen, T. G. (2020). An energy-efficient fuzzy-based scheme for unequal multihop clustering in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02090-z.

    Article  Google Scholar 

  10. Afsar, M. M., & Younis, M. (2019). A load-balanced cross-layer design for energy-harvesting sensor networks. Journal of Network and Computer Applications, 145, 102390.

    Article  Google Scholar 

  11. Zhang, J., Feng, X., & Liu, Z. (2018). A grid-based clustering algorithm via load analysis for industrial Internet of things. IEEE Access, 6, 13117–13128.

    Article  Google Scholar 

  12. Lalitha, K., Thangarajan, R., Udgata, S. K., Poongodi, C., & Sahu, A. P. (2017). GCCR: An efficient grid based clustering and combinational routing in wireless sensor networks. Wireless Personal Communications, 97(1), 1075–1095.

    Article  Google Scholar 

  13. Zhou, Y., Wang, N., & Xiang, W. (2016). Clustering hierarchy protocol in wireless sensor networks using an improved PSO algorithm. IEEE Access, 5, 2241–2253.

    Article  Google Scholar 

  14. 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 

  15. Moussa, N., Hamidi-Alaoui, Z., & El Alaoui, A. E. B. (2020). ECRP: An energy-aware cluster-based routing protocol for wireless sensor networks. Wireless Networks,. https://doi.org/10.1007/s11276-019-02247-5.

    Article  Google Scholar 

  16. Javaid, N., Rasheed, M. B., Imran, M., Guizani, M., Khan, Z. A., Alghamdi, T. A., & Ilahi, M. (2015). An energy-efficient distributed clustering algorithm for heterogeneous WSNs. EURASIP Journal on Wireless Communications and Networking, 2015(1), 1–11.

    Article  Google Scholar 

  17. Lee, J. S., & Kao, T. Y. (2016). An improved three-layer low-energy adaptive clustering hierarchy for wireless sensor networks. IEEE Internet of Things Journal, 3(6), 951–958.

    Article  Google Scholar 

  18. Cenedese, A., Luvisotto, M., & Michieletto, G. (2016). Distributed clustering strategies in industrial wireless sensor networks. IEEE Transactions on Industrial Informatics, 13(1), 228–237.

    Article  Google Scholar 

  19. Das, S. K., & Tripathi, S. (2019). Energy efficient routing formation algorithm for hybrid ad-hoc network: A geometric programming approach. Peer-to-Peer Networking and Applications, 12(1), 102–128.

    Article  Google Scholar 

  20. Sert, S. A., Bagci, H., & Yazici, A. (2015). MOFCA: Multi-objective fuzzy clustering algorithm for wireless sensor networks. Applied Soft Computing, 30, 151–165.

    Article  Google Scholar 

  21. Baranidharan, B., & Santhi, B. (2016). DUCF: Distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach. Applied Soft Computing, 40, 495–506.

    Article  Google Scholar 

  22. Akila, I. S., & Venkatesan, R. (2016). A cognitive multi-hop clustering approach for wireless sensor networks. Wireless Personal Communications, 90(2), 729–747.

    Article  Google Scholar 

  23. Balakrishnan, B., & Balachandran, S. (2017). FLECH: Fuzzy logic based energy efficient clustering hierarchy for nonuniform wireless sensor networks. Wireless Communication Mobile Computing, 2017, 1214720.

    Article  Google Scholar 

  24. Agrawal, D., & Pandey, S. (2018). FUCA: Fuzzy-based unequal clustering algorithm to prolong the lifetime of wireless sensor networks. International Journal of Communication Systems, 31(2), e3448.

    Article  Google Scholar 

  25. Mazumdar, N., & Om, H. (2017). Distributed fuzzy logic based energy-aware and coverage preserving unequal clustering algorithm for wireless sensor networks. International Journal of Communication Systems, 30(13), e3283.

    Article  Google Scholar 

  26. Mazinani, A., Mazinani, S. M., & Mirzaie, M. (2019). FMCR-CT: An energy-efficient fuzzy multi cluster-based routing with a constant threshold in wireless sensor network. Alexandria Engineering Journal, 58(1), 127–141.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sayyed Majid Mazinani.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mazinani, A., Mazinani, S.M. & Hasanabadi, S. FSCVG: A Fuzzy Semi-Distributed Clustering Using Virtual Grids in WSN. Wireless Pers Commun 118, 1017–1038 (2021). https://doi.org/10.1007/s11277-020-08056-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-08056-w

Keywords

Navigation