Skip to main content

Advertisement

SpringerLink
Log in

A Distributed Load Balancing Clustering Algorithm for Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In wireless sensor networks, energy consumption is one of the main constraints that limit the effects of their applicabilities. Clustering provides a practical solution to improve energy efficiency. However, the loads of each cluster head are not balance. Thus, the energy cannot be consumed evenly in each cluster head. To overcome this problem, we propose a distributed load balancing clustering algorithm (DLBCA). Without relying on central nodes, the sensor nodes can separately determine their roles (cluster heads or cluster members) and the clustering structure. The determination of cluster heads is based on the residual energy of sensor nodes and the distance to other nodes. DLBCA defines three matrixes (DD, Flag and FlagDis), which are related to the clustering. Through the matrixes, DLBCA can assign balanced and appropriate member nodes for the CHs. Our experimental results show that the DLBCA has better load balancing, longer life cycle and higher energy efficiency compared with existing 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

References

  1. Gu, Y., & He, T. (2011). Dynamic switching-based data forwarding for low-duty-cycle wireless sensor networks. IEEE Transactions on Mobile Computing, 10, 1741–1754.

    Article  Google Scholar 

  2. Survey, A., Ian, Akyildiz, Su, W. Y., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks. Computer Networks, 38, 393–422.

    Article  Google Scholar 

  3. Gao, Yating, Guixia, K. A. N. G., Jianming, Cheng, & Ningbo, Zhang. (2017). A new energy efficient clustering algorithm based on routing spanning tree for wireless sensor network. IEICE Transactions on Communications, E100–B, 2110–2120.

    Article  Google Scholar 

  4. Abbasi, Ameer Ahmed, & Younis, Mohamed. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30, 2826–2841.

    Article  Google Scholar 

  5. Wei, D., Jin, Y., Vural, S., Moessner, K., & Tafazolli, R. (2015). An energy-efficient clustering solution for wireless sensor networks. IEEE Transactions on Wireless Communications, 10, 3973–3983.

    Article  Google Scholar 

  6. Gupta, Suneet K., & Jana, Prasanta K. (2015). Energy efficient clustering and routing algorithms for wireless sensor networks: GA based approach. Wireless Personal Communications, 83, 2403–2423.

    Article  Google Scholar 

  7. Jung, Woo-Sung., Lim, Keun-Woo., Ko, Young-Bae., & Park, Sang-Joon. (2011). Efficient clustering-based data aggregation techniques for wireless sensor networks. Wireless Networks, 17, 1387–1400.

    Article  Google Scholar 

  8. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan H. (2000).Energy-efficient communication protocol for wireless microsensor networks.Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

  9. Batra, Payal, & Kant, Krishna. (2016). LEACH-MAC: a new cluster head selection algorithm for wireless sensor networks. Wireless Networks, 22, 49–60.

    Article  Google Scholar 

  10. Konstantopoulos, C., Pantziou, G., Gavalas, D., Mpitziopoulos, A., & Mamalis, B. (2012). A rendezvous-based approach enabling energy-efficient sensory data collection with mobile sinks. IEEE Transactions on Parallel and Distributed Systems, 23, 809–817.

    Article  Google Scholar 

  11. Souissi, Manel, & Meddeb, Aref. (2017). Optimal load balanced clustering in homogeneous wireless sensor networks. International Journal of Communication Systems, 30, e3229.

    Article  Google Scholar 

  12. Ammari, Habib M., & Das, Sajal K. (2012). Centralized and clustered K-coverage protocols for wireless sensor networks. IEEE Transactions on Computers, 61, 0018–9340.

    Article  MathSciNet  Google Scholar 

  13. Muruganathan, S. D., Ma, D. C. F., Bhasin, R. I., & Fapojuwo, A. O. (2005). A centralized energy-efficient routing protocol for wireless sensor networks. IEEE Communications Magazine, 43, S8-13.

    Article  Google Scholar 

  14. Sasikumar, P., & Sibaram, K. (2012). K-means clustering in wireless sensor networks. Proceedings of the 2012 Fourth International Conference on Computational Intelligence and Communication Networks.

  15. Chen, Jian, Li, Zhen, & Kuo, Yong-Hong. (2013). A centralized balance clustering routing protocol for wireless sensor network. Wireless Personal Communications, 72, 623–634.

    Article  Google Scholar 

  16. Liu, Xuxun. (2012). A survey on clustering routing protocols in wireless sensor networks. Sensors (Basel, Switzerland), 12, 11113–11153.

    Article  Google Scholar 

  17. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2000). Application-specific protocol architectures for wireless networks. Cambridge: Massachusetts Institute of Technology.

    Google Scholar 

  18. Darabkh, Khalid A., Al-Rawashdeh, Wala’a S., Al-Zubi, Raed T., & Alnabelsi, Sharhabeel H. (2017). C-DTB-CHR: Centralized density- and threshold-based cluster head replacement protocols for wireless sensor networks. Journal of Supercomputing, 73, 5332–5353.

    Article  Google Scholar 

  19. Elhoseny, M., Yuan, X., Yu, Z., Mao, C., El-Minir, H. K., & Riad, A. M. (2015). Balancing energy consumption in heterogeneous wireless sensor networks using genetic algorithm. IEEE Communications Letters, 19, 2194–2197.

    Article  Google Scholar 

  20. Kuila, Pratyay, & Jana, Prasanta K. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127–140.

    Article  Google Scholar 

  21. Hacioglu, Gokce, Kand, V. F. A., & Sesli, E. (2016). Multi objective clustering for wireless sensor networks. Engineering Applications of Artificial Intelligence, 59, 86–100.

    Google Scholar 

  22. RejinaParvin, J., & Vasanthanayaki, C. (2015). Particle swarm optimization-based clustering by preventing residual nodes in wireless sensor networks. IEEE Sensors Journal, 15, 4264–4274.

    Article  Google Scholar 

  23. Ding, Xu-Xing., Ling, Min, Wang, Zai-Jian., & Song, Feng-Lou. (2017). DK-LEACH: An optimized cluster structure routing method based on LEACH in wireless sensor networks. Wireless Personal Communications, 96, 6369–6379.

    Article  Google Scholar 

  24. Sohn, I., Lee, J., & Lee, S. H. (2016). Low-energy adaptive clustering hierarchy using affinity propagation for wireless sensor networks. IEEE Communications Letters, 20, 558–561.

    Article  Google Scholar 

  25. Gao, Ying, Wkram, Chris Hadri, Duan, Jiajie, & Chou, Jarong. (2015). A novel energy-aware distributed clustering algorithm for heterogeneous wireless sensor networks in the mobile environment. Sensors, 15, 31108–31124.

    Article  Google Scholar 

  26. Loscri, V., Morabito, G., & Marano, S. (2005). A two-levels hierarchy for low-energy adaptive clustering hierarchy (TL-LEACH). 2005 IEEE 62nd Vehicular Technology Conference.

  27. Younis, O., & Fahmy, S. (2004). HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3, 366–379.

    Article  Google Scholar 

  28. Smaragdakis, G., Matta, I., & Bestavros, A. (2004). SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. Second International Workshop on Sensor and Actor Network Protocols and Applications.

  29. Mehmood, Amjad, Lloret, Jaime, Noman, M., & Song, Houbing. (2015). Improvement of the wireless sensor network lifetime using LEACH with vice-cluster head. Ad-Hoc and Sensor Wireless Networks, 28, 1–17.

    Google Scholar 

  30. Yang, Liu, Lu, Yin-Zhi., Zhong, Yuan-Chang., & Yang, Simon X. (2018). An unequal cluster-based routing scheme for multi-level heterogeneous wireless sensor networks. Telecommunication Systems, 68, 11–26.

    Article  Google Scholar 

  31. Tripathi, R. K., Singh, Y. N., & Verma, N. K. (2012). N-LEACH, a balanced cost cluster-heads selection algorithm for wireless sensor network. National Conference on Communications (NCC), 2012, 1–5.

    Google Scholar 

Download references

Acknowledgements

The research in the paper is supported by the National Key Research and Development Program of China (2017YFC1703500), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 20KJB510021), the National Natural Science Foundation of China (82074580).

Author information

Authors and Affiliations

  1. School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China

    Tianshu Wang, Kongfa Hu & Gongxuan Zhang

  2. School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing, China

    Xichen Yang

Authors
  1. Tianshu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xichen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kongfa Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gongxuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tianshu Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Wang, T., Yang, X., Hu, K. et al. A Distributed Load Balancing Clustering Algorithm for Wireless Sensor Networks. Wireless Pers Commun 120, 3343–3367 (2021). https://doi.org/10.1007/s11277-021-08617-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08617-7

Keywords

Search

Navigation