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HFLBSC: Heuristic and Fuzzy Based Load Balanced, Scalable Clustering Algorithm for Wireless Sensor Network

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Abstract

In spite of the severe limitations on the resources of the sensor nodes such as memory, computational power, transmission range and battery, the application areas of Wireless Sensor Networks (WSNs) are increasing day by day. The main challenge in WSNs is energy consumption. It becomes significant when a large number of nodes are deployed. Although clustering is one of the solutions to cater to this problem, but it suffers from severe energy consumption due to the non-uniform selection of CHs and frequent re-clustering. In this paper, we propose a heuristic and fuzzy based load balanced, scalable clustering algorithm for WSNs called HFLBSC. In this algorithm, we have segregated the network into a layered structure using the area under intersection over union curve. We have selected the CHs by considering residual energy and distance threshold. We have stalled the frequent re-clustering by utilizing the decision made with the help of fuzzy logic. Our proposed scheme is capable enough to elongate the network lifetime. Simulation results confirm that on an average, HFLBSC is 32% better in terms of FND, 38% less energy consumption, 25% more alive nodes, 72% less deviation in residual energy than LEACH, FM-SCHEL, and MIWOCA.

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References

  1. Pathak, A. (2020). A proficient bee colony-clustering protocol to prolong lifetime of wireless sensor networks. Journal of Computer Networks and Communications 2020.

  2. Daanoune, I., Abdennaceur, B., & Ballouk, A. (2021). A comprehensive survey on LEACH-based clustering routing protocols in wireless sensor networks. In: Ad Hoc Networks, p. 102409.

  3. Mahabal, C., & Fang, H. (2020). Smart spectrum switching and beamforming for wireless body area networks in dynamic environment. Journal of Communications and Information Networks, 5(2), 204–216.

    Article  Google Scholar 

  4. Zhao, Z., Li, G., & Xu, M. (2019). An improved algorithm based on LEACH routing protocol. In: Proceedings of the 2019 IEEE 19th international conference on communication technology (ICCT). IEEE, pp. 1248–1251.

  5. Visu, P., et al. (2020). Bio-inspired dual cluster heads optimized routing algorithm for wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, pp. 1–9.

  6. Kortas, M., et al. (2020). The energy-aware matrix completion-based data gathering scheme for wireless sensor networks. IEEE Access, 8, 30772–30788.

    Article  Google Scholar 

  7. Ullah, Z. (2020). A survey on hybrid, energy efficient and distributed (HEED) based energy efficient clustering protocols for wireless sensor networks. In: Wireless personal communications, pp. 1–29.

  8. Gupta, G.P., & Saha, B. (2020). Load balanced clustering scheme using hybrid metaheuristic technique for mobile sink based wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing.

  9. Wang, M., Wang, S., & Zhang, B. (2020). APTEEN routing protocol optimization in wireless sensor networks based on combination of genetic algorithms and fruit fly optimization algorithm. Ad Hoc Networks, p. 102138.

  10. El Fissaoui, M., Beni-Hssane, A., & Saadi, M. (2019). Energy efficient and fault tolerant distributed algorithm for data aggregation in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 10(2), 569–578.

    Article  Google Scholar 

  11. Mehmood, A., et al. (2017). ELDC: An artificial neural network based energy-efficient and robust routing scheme for pollution monitoring in WSNs. In: IEEE Transactions on Emerging Topics in Computing.

  12. 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 

  13. Zhang, C., Patras, P., & Haddadi, H. (2019). Deep learning in mobile and wireless networking: A survey. IEEE Communications Surveys and Tutorials, 21(3), 2224–2287.

    Article  Google Scholar 

  14. Rabiner Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd annual Hawaii international conference on system sciences. IEEE, p. 10.

  15. Kumar Singh, N., Kasana, A., & Kumar Sachan, V. (2016). Enhancement in lifetime of sensor node using data reduction technique in wireless sensor network. International Journal of Computer Applications, 145(11), 1–5.

    Article  Google Scholar 

  16. Mehmood, A., et al. (2015). Energy-efficient multi-level and distance-aware clustering mechanism for WSNs. International Journal of Communication Systems, 28(5), 972–989.

    Article  Google Scholar 

  17. Marappan, P., & Rodrigues, P. (2016). An energy efficient routing protocol for correlated data using CL-LEACH in WSN. Wireless Networks, 22(4), 1415–1423.

    Article  Google Scholar 

  18. Ding, X.-X., et al. (2017). Dk-leach: An optimized cluster structure routing method based on leach in wireless sensor networks. Wireless Personal Communications, 96(4), 6369–6379.

    Article  Google Scholar 

  19. Thiagarajan, R., et al. (2020). Energy consumption and network connectivity based on Novel-LEACH-POS protocol networks. Computer Communications, 149, 90–98.

    Article  Google Scholar 

  20. Sajwan, M., Gosain, D., & Ajay, K. S. (2018). Hybrid energy-efficient multi-path routing for wireless sensor networks. Computers and Electrical Engineering, 67, 96–113.

    Article  Google Scholar 

  21. Rajaram, V., & Kumaratharan, N. (2021). Multi-hop optimized routing algorithm and load balanced fuzzy clustering in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 12(3), 4281–4289.

    Article  Google Scholar 

  22. Farahani, M., & Ghaffarpour Rahbar, A. (2019). Double leveled unequal clustering with considering energy efficiency and load balancing in dense iot networks. Wireless Personal Communications, 106(3), 1183–1207.

    Article  Google Scholar 

  23. Sharma, R., Vashisht, V., Singh, U. (2019). Fuzzy modelling based energy aware clustering in wireless sensor networks using modified invasive weed optimization. Journal of King Saud University-Computer and Information Sciences.

  24. Al-Baz, A., & El-Sayed, A. (2018). A new algorithm for cluster head selection in LEACH protocol for wireless sensor networks. International Journal of Communication Systems, 31(1), e3407.

    Article  Google Scholar 

  25. Panchal, A., & Kumar Singh, R. (2021). EHCR-FCM: Energy efficient hierarchical clustering and routing using fuzzy C-means for wireless sensor networks. Telecommunication Systems, 76(2), 251–263.

    Article  Google Scholar 

  26. Phoemphon, S., et al. (2020). An energy-efficient fuzzy-based scheme for unequal multihop clustering in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, pp. 1–23.

  27. Selvi, M., et al. (2021). An energy efficient clustered gravitational and fuzzy based routing algorithm in WSNs. Wireless Personal Communications, 116(1), 61–90.

    Article  Google Scholar 

  28. Zhang, Y., et al. (2017). Fuzzy-logic based distributed energy-efficient clustering algorithm for wireless sensor networks. Sensors, 17(7), 1554.

    Article  Google Scholar 

  29. Uma Maheswari, D., & Sudha, S. (2019). Node degree based energy efficient two-level clustering for wireless sensor networks. Wireless Personal Communications, 104(3), 1209–1225.

    Article  Google Scholar 

  30. Chen, G., et al. (2009). An unequal cluster-based routing protocol in wireless sensor networks. Wireless Networks, 15(2), 193–207.

    Article  Google Scholar 

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Acknowledgements

Priti Maratha acknowledges the support from University Grant Commission, New Delhi under National Eligibility Test-Junior Research Fellowship scheme with Reference ID-3361/(NET-JUNE 2015).

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Maratha, P., Gupta, K. HFLBSC: Heuristic and Fuzzy Based Load Balanced, Scalable Clustering Algorithm for Wireless Sensor Network. Wireless Pers Commun 125, 281–304 (2022). https://doi.org/10.1007/s11277-022-09550-z

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  • DOI: https://doi.org/10.1007/s11277-022-09550-z

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