Skip to main content

Advertisement

SpringerLink
Log in

Load balanced cluster formation to avoid energy hole problem in WSN using fuzzy rule-based system

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

In wireless sensor networks, the energy efficiency is a big challenge and this can be defeated using various techniques such as clustering and routing. In the clustering approach, clusters are made up of the sensor nodes. In most of the applications, the sensor nodes are battery-powered. The sensor nodes may carry an uneven load of data and consume an unequal amount of energy. This leads to the formation of an energy hole in the network. Therefore, load balancing in the network with respect to the distance of data transmission is important in WSN. Hence, this paper proposes an energy-efficient as well as Load Balanced Clustering algorithm using Fuzzy logic (LBCF). The values of load and distance between the sensor node and BS are associated using rule-based fuzzy logic. The output values of the fuzzy system decides the cluster heads (CHs) and the size of the cluster. The sensor nodes get assigned to the CHs with respect to the capacity of CH. The simulations are conducted under different scenarios and network parameters and the proposed LBCF shows out performance to the state-of-the-art algorithms.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Data availability

Not applicable

Code availability

Not applicable

References

  1. Akyildiz, I. F., Weilian, S., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  2. Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30(14–15), 2826–2841.

    Article  Google Scholar 

  3. Rostami, A. S., Badkoobe, M., Mohanna, F., Hosseinabadi, A. A. R., & Sangaiah, A. K. (2018). Survey on clustering in heterogeneous and homogeneous wireless sensor networks. The Journal of Supercomputing, 74(1), 277–323.

    Article  Google Scholar 

  4. Sundararaj, V., Muthukumar, S., & Kumar, R. S. (2018). An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Computers & Security, 77, 277–288.

    Article  Google Scholar 

  5. Edla, D. R., Lipare, A., & Cheruku, R. (2018). Shuffled complex evolution approach for load balancing of gateways in wireless sensor networks. Wireless Personal Communications, 98(4), 3455–3476.

    Article  Google Scholar 

  6. Edla, D. R., Kongara, M. C., & Cheruku, R. (2019). SCE-PSO based clustering approach for load balancing of gateways in wireless sensor networks. Wireless Networks, 25(3), 1067–1081.

    Article  Google Scholar 

  7. Shaikh, F. K., & Zeadally, S. (2016). Energy harvesting in wireless sensor networks: A comprehensive review. Renewable and Sustainable Energy Reviews, 55, 1041–1054.

    Article  Google Scholar 

  8. Yetgin, H., Cheung, K. T. K., El-Hajjar, M., & Hanzo, L. H. (2017). A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Communications Surveys & Tutorials, 19(2), 828–854.

    Article  Google Scholar 

  9. Edla, D. R., Lipare, A., Cheruku, R., & Kuppili, V. (2017). An efficient load balancing of gateways using improved shuffled frog leaping algorithm and novel fitness function for WSNs. IEEE Sensors Journal, 17(20), 6724–6733.

    Article  Google Scholar 

  10. Lipare, A., & Edla, D. R. (2018). Novel fitness function for SCE algorithm based energy efficiency in WSN. In 9th international conference on computing, communication and networking technologies (ICCCNT) (pp. 1-7). IEEE.

  11. Edla, D. R., Kongara, M. C., & Cheruku, R. (2019). A PSO based routing with novel fitness function for improving lifetime of WSNs. Wireless Personal Communications, 104(1), 73–89.

    Article  Google Scholar 

  12. Lipare, A., Edla, D. R., & Kuppili, V. (2019). Energy efficient load balancing approach for avoiding energy hole problem in WSN using grey wolf optimizer with novel fitness function. Applied Soft Computing, 105–706.

  13. Lipare, A., & Edla, D. R. (2019). Cluster head selection and cluster construction using fuzzy logic in WSNs. In 2019 IEEE 16th India council international conference (INDICON).

  14. Bagci, H., & Yazici, A. (2013). An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Applied Soft Computing, 13(4), 1741–1749.

    Article  Google Scholar 

  15. Handy, M. J., Haase, M., & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In 4th international workshop on mobile and wireless communications network (pp. 368–372). IEEE.

  16. Kumar, N., & Kaur, J. (2011). Improved leach protocol for wireless sensor networks. In 7th international conference on wireless communications, networking and mobile computing (pp. 1–5). IEEE.

  17. Kim, J.-M., Park, S.-H., Han, Y.-J., & Chung, T.-M. (2008). CHEF: Cluster head election mechanism using fuzzy logic in wireless sensor networks. In 10th international conference on advanced communication technology (pp. 654–659). IEEE.

  18. 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 

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

  20. Siqing, Z., Yang, T., & Feiyue, Y. (2018). Fuzzy logic-based clustering algorithm for multi-hop wireless sensor networks. Procedia Computer Science, 131, 1095–1103.

    Article  Google Scholar 

  21. 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 

  22. 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 

  23. Mamdani, E. H. (1976). Application of fuzzy logic to approximate reasoning using linguistic synthesis. In Proceedings of the sixth international symposium on Multiple-valued logic (pp. 196–202). IEEE Computer Society Press.

  24. Lipare, A., Edla, D. R., & Dharavath, R. (2020). Energy efficient routing structure to avoid energy hole problem in multi-layer network model, Wireless Personal Communications.

Download references

Funding

Not applicable

Author information

Authors and Affiliations

  1. National Institute of Technology, Ponda, Goa, 403401, India

    Damodar Reddy Edla & Saidi Reddy Parne

  2. Computer Science and Engineering, Indian Institute of Information Technology Pune, Pune, India

    Amruta Lipare

Authors
  1. Damodar Reddy Edla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amruta Lipare
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saidi Reddy Parne
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Damodar Reddy Edla.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Edla, D.R., Lipare, A. & Parne, S.R. Load balanced cluster formation to avoid energy hole problem in WSN using fuzzy rule-based system. Wireless Netw 29, 1299–1310 (2023). https://doi.org/10.1007/s11276-022-03200-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-022-03200-9

Keywords

Search

Navigation