Skip to main content

Advertisement

SpringerLink
Log in

An Unequal Cluster-Radius Approach Based on Node Density in Clustering for Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Lifetime is a major challenge in wireless sensor networks which depends on the energy consumption of the nodes. Energy limitation in nodes leads to many researches to find out new solutions for this problem. Clustering is an efficient solution for this issue. In clustering, all nodes that are distributed in an application zone should be partitioned into some virtual sections that are called clusters. Each cluster must have one node for managing intra-cluster tasks that is called cluster-head (CH). CHs are used for transmitting collected data to the base-station (BS). The multi-hop is one way of transferring CHs data to the BS which increases the data traffic on CHs near the BS. The unequal clustering is one of the solutions for solving bottleneck CH problems. The difference between equal and unequal clustering is in the type of cluster-radius, which could be fixed or variable. In this paper, a distributed unequal cluster-radius approach is proposed based on node density for clustering in WSNs that has two main phases: CH-selection and Member-join. CH-selection phase is based on global and local search, and member-join phase is done according to an evaluation function. Experimental results show that the proposed method leads to an increase in the lifetime of WSNs in comparison with state-of-the-art methods.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Elhabyan, R. S., & Yagoub, M. C. E. (2015). Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network. Journal of Network and Computer Applications, 52, 116–128.

    Article  Google Scholar 

  3. Amgoth, T., & Jana, P. K. (2014). Energy-aware routing algorithm for wireless sensor networks. Computers & Electrical Engineering, 41, 357–367.

    Article  Google Scholar 

  4. Al-Junaid, A. F., & Al-kamali, F. S. (2016). Efficient wireless transmission scheme based on the recent DST-MC-CDMA. Wireless Networks, 22, 813–824.

    Article  Google Scholar 

  5. Peng, S., Wang, T., & Low, C. P. (2015). Energy neutral clustering for energy harvesting wireless sensors networks. Ad Hoc Networks, 28, 1–16.

    Article  Google Scholar 

  6. Saranya, V., Shankar, S., & Kanagachidambaresan, G. R. (2018). Energy efficient clustering scheme (EECS) for wireless sensor network with mobile sink. In Wireless Personal Communications (pp. 1–15).

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

    Google Scholar 

  8. Khan, I., Belqasmi, F., Glitho, R., Crespi, N., Morrow, M., & Polakos, P. (2016). Wireless sensor network virtualization: A survey. IEEE Communications Surveys & Tutorials, 18, 553–576.

    Article  Google Scholar 

  9. Kuila, P., & Jana, P. 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 

  10. Gu, X., Yu, J., Yu, D., Wang, G., & Lv, Y. (2014). ECDC: An energy and coverage-aware distributed clustering protocol for wireless sensor networks. Computers & Electrical Engineering, 40, 384–398.

    Article  Google Scholar 

  11. Chen, W., & Lea, C.-T. (2016). Oblivious routing in wireless mesh networks. Wireless Networks, 22, 2337–2353.

    Article  Google Scholar 

  12. Tyagi, S., & Kumar, N. (2013). A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks. Journal of Network and Computer Applications, 36, 623–645.

    Article  Google Scholar 

  13. Tanwara, S., Kumarb, N., & Rodrigues, J. (2015). A systematic review on heterogeneous routing protocols for wireless sensor network. Journal of Network and Computer Applications, 53, 39–56.

    Article  Google Scholar 

  14. Shin, H., Moh, S., Chung, I., & Kang, M. (2015). Equal-size clustering for irregularly deployed wireless sensor networks. Wireless Personal Communications, 82, 995–1012.

    Article  Google Scholar 

  15. 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 

  16. Azharuddin, M., Kuila, P., & Jana, P. K. (2015). Energy efficient fault tolerant clustering and routing algorithms for wireless sensor networks. Computers & Electrical Engineering, 41, 177–190.

    Article  Google Scholar 

  17. Li, C., Ye, M., Chen, G., & Wu, J. (2005). An energy-efficient unequal clustering mechanism for wireless sensor networks. In IEEE International conference on mobile ad-hoc and sensor systems conference, (pp. 604–612).

  18. Soro, S., & Heinzelman, W. (2005). Prolonging the lifetime of wireless sensor networks via unequal clustering. In Proceedings of the 5th IEEE International workshop on algorithms for wireless, mobile, ad hoc and sensor networks (pp. 236–243).

  19. Chen, G., Li, C., Ye, M., & Wu, J. (2009). An unequal cluster-based routing protocol in wireless sensor networks. Wireless Networks, 15, 193–207.

    Article  Google Scholar 

  20. Selvi, G. V., & Manoharan, R. (2013). A survey of energy efficient unequal clustering algorithms for wireless sensor networks. International Journal of Computer Applications, 79, 1–4.

    Google Scholar 

  21. Guiloufi, A. B. F., Nasri, N., & Kachouri, A. (2016). An energy-efficient unequal clustering algorithm using ‘Sierpinski triangle’ for WSNs”. Wireless Personal Communications, 88, 449–465.

    Article  Google Scholar 

  22. Shokouhifar, Mohammad, & Jalali, Ali. (2017). Optimized sugeno fuzzy clustering algorithm for wireless sensor networks. Engineering Applications of Artificial Intelligence, 60, 16–25.

    Article  Google Scholar 

  23. Logambigai, R., & Kannan, A. (2016). Fuzzy logic based unequal clustering for wireless sensor networks. Wireless Networks, 22, 945–957.

    Article  Google Scholar 

  24. Lee, S., Choe, H., Park, B., Song, Y., & Kim, C. (2011). An energy-efficient unequal clustering algorithm using location information for wireless sensor networks. Wireless Personal Communications, 56, 715–731.

    Article  Google Scholar 

  25. Arjunan, S., & Pothula, S. (2017). A survey on unequal clustering protocols in wireless sensor networks. Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2017.03.006.

    Google Scholar 

  26. Agrawal, D., & Pandey, S. (2018). “FUCA: Fuzzy-based unequal clustering algorithm to prolong the lifetime of wireless sensor networks. International Journal of Communication Systems. https://doi.org/10.1002/dac.3448.

    Google Scholar 

  27. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). “Energy-efficient communication protocol for wireless micro sensor networks. In Hawaii international conference on system sciences (HICSS) (pp. 10–19).

  28. 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 

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

    Article  Google Scholar 

  30. Chang-jiang, J., Wei-ren, S., Min, X., Xian-lun, T. (2010). Energy-balanced unequal clustering protocol for wireless sensor networks. The journal of China universities of posts and telecommunications (pp. 94–99).

  31. Logambigai, R., & Kannan, A. (2016). Fuzzy logic based unequal clustering for wireless sensor networks. Wireless Networks, 22(3), 945–957.

    Article  Google Scholar 

  32. Baniata, M., & Jiman, H. (2017). Energy-efficient unequal chain length clustering for wireless sensor networks in smart cities. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2017/5790161.

    Google Scholar 

  33. Corn, J., & Bruce, J. W. (2017). Clustering algorithm for improved network lifetime of mobile wireless sensor networks. In 2017 International Conference on Computing, Networking and Communications (ICNC). IEEE.

  34. Baranidharan, B., & Balachandran, S. (2017). “FLECH: Fuzzy logic based energy efficient clustering hierarchy for nonuniform wireless sensor networks. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2017/1214720.

    Google Scholar 

  35. Bhatia, A., & Hansdah, R. C. (2015). A distributed TDMA slot scheduling algorithm for spatially correlated contention in WSNs. Mobile Information Systems. https://doi.org/10.1155/2015/234143.

    Google Scholar 

  36. Gennert, M. A., & Yuille, A. L. (1998). Determining the optimal weights in multiple objective function optimization. In ICCV (pp. 87–89).

  37. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless micro sensor networks. IEEE Transactions on Wireless Communications, 1, 660–670.

    Article  Google Scholar 

  38. Gong, D., Yang, Y., & Pan, Z. (2013). Energy-efficient clustering in lossy wireless sensor networks. Journal of Parallel and Distributed Computing, 73, 1323–1336.

    Article  Google Scholar 

  39. Handy, M., 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).

  40. Albath, J., Thakur, M., & Madria, S. (2013). Energy constraint clustering algorithms for wireless sensor Networks. Ad Hoc Networks, 11, 2512–2525.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Engineering and Information Technology, Sadjad University of Technology, Mashhad, Iran

    Javad Hamidzadeh & Mohammad Hadi Ghomanjani

Authors
  1. Javad Hamidzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Hadi Ghomanjani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Javad Hamidzadeh.

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

Hamidzadeh, J., Ghomanjani, M.H. An Unequal Cluster-Radius Approach Based on Node Density in Clustering for Wireless Sensor Networks. Wireless Pers Commun 101, 1619–1637 (2018). https://doi.org/10.1007/s11277-018-5779-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-018-5779-1

Keywords

Search

Navigation