Skip to main content

Advertisement

Springer Nature Link
Log in

Dynamic Multi-hop Clustering in a Wireless Sensor Network: Performance Improvement

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

A cluster-based model is preferable in wireless sensor network due to its ability to reduce energy consumption. However, managing the nodes inside the cluster in a dynamic environment is an open challenge. Selecting the cluster heads (CHs) is a cumbersome process that greatly affects the network performance. Although there are several studies that propose CH selection methods, most of them are not appropriate for a dynamic clustering environment. To avoid this problem, several methods were proposed based on intelligent algorithms such as fuzzy logic, genetic algorithm (GA), and neural networks. However, these algorithms work better within a single-hop clustering model framework, and the network lifetime constitutes a big issue in case of multi-hop clustering environments. This paper introduces a new CH selection method based on GA for both single-hop and the multi-hop cluster models. The proposed method is designed to meet the requirements of dynamic environments by electing the CH based on six main features, namely, (1) the remaining energy, (2) the consumed energy, (3) the number of nearby neighbors, (4) the energy aware distance, (5) the node vulnerability, and (6) the degree of mobility. We shall see how the corresponding results show that the proposed algorithm greatly extends the network lifetime.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Younis, M., & Akkaya, K. (2008). Strategies and techniques for node placement in wireless sensor networks: A survey. Ad Hoc Networks, 6, 621–655.

    Article  Google Scholar 

  2. Elhoseny, M., Yuan, X., Yu, Z., Mao, C., ElMinir, H. K., & Riad, A. M. (2014). Balancing energy consumption in heterogeneous wireless sensor networks using genetic algorithm. IEEE Communications Letters, 99, 1–4.

    Google Scholar 

  3. Yuan, X., Elhoseny, M., ElMinir, H., & Riad, A. (2016). A genetic algorithm-based, dynamic clustering method towards improved wsn longevity. Journal of Network and Systems Management, 1–26, 2016.

    Google Scholar 

  4. Rahman, A., Anwar, S., Pramanik, I., & Rahman, F. (2013). A survey on energy efficient routing techniques in wireless sensor network. In International conference of Advanced communication Technology (pp. 200–205).

  5. Ali, J., Kumar, G., & Rai, M. (2013). Major energy efficient routing schemes in wireless sensor networks. International Journal of Computers and Technology, 4(2), 261–266.

    Google Scholar 

  6. Elhoseny, M., Elminir, H., Riad, A., & Yuan, X. (2014). Recent advances of secure clustering protocols in wireless sensor networks. International Journal of Computer Networks and Communications Security, 2(11), 400–413.

    Google Scholar 

  7. Pantazis, N., Nikolidakis, S., & Vergados, D. (2013). Energy-efficient routing protocols in wireless sensor networks: A survey. Communications Surveys and Tutorials, 15(2), 551–591.

    Article  Google Scholar 

  8. Bhattacharjee, A., Bhallamudi, B., & Maqbool, Z. (2013). Energy-efficient hierarchical cluster based routing algorithm in WSN: A survey. International Journal of Engineering Research and Technology, 2(5), 302–311.

    Google Scholar 

  9. Du, T., Qu, S., Liu, F., & Wang, Q. (2015). An energy efficiency semi-static routing algorithm for WSNs based on HAC clustering method. Information Fusion, 21(1):18–29.

    Google Scholar 

  10. Tyagia, S., & Kumarb, 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(2), 623–645.

    Article  Google Scholar 

  11. Attea, B. A., & Khalil, E. A. (2012). A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Applied Soft Computing, 12(7), 1950–1957.

    Article  Google Scholar 

  12. Ruan, F., Yin, C., Chen, J., Wang, J., & Xue, S. (2013). A distance clustering routing algorithm considering energy for wireless sensor networks. International Journal of Future Generation Communication and Networking, 6(5), 73–80.

    Article  Google Scholar 

  13. Iqbal, A., Akbar, M., Javaid, N., Bouk, S., Ilahi, M., & Khan, R. (2013). Advanced LEACH: A static clustering-based heterogeneous routing protocol for WSNs. Journal of Basic and Applied Scientific Research, 3(5), 864–872.

    Google Scholar 

  14. Elhoseny, M., Yuan, X., ElMinir, H., & Riad, A. (2014). Extending self-organizing network availability using genetic algorithm. In  International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, doi:10.1109/ICCCNT.2014.6963059.

  15. Elhoseny, M., Elleithy, K., Elminir, H., Yuan, X., & Riad, A. (2015). Dynamic clustering of heterogeneous wireless sensor networks using a genetic algorithm, towards balancing energy exhaustion. International Journal of Scientific and Engineering Research, 6(8), 1243–1252.

    Google Scholar 

  16. Kang, S., & Nguyen, T. (2012). Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Communications Letters, 16(9), 1396–1399.

    Article  Google Scholar 

  17. Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.

    Article  Google Scholar 

  18. Guo, W., & Zhang, W. (2014). A survey on intelligent routing protocols in wireless sensor networks. Journal of Network and Computer Applications, 38, 185–201.

    Article  Google Scholar 

  19. Ahmed, G., Khan, N., & Ramer, R. (2008). Cluster head selection using evolutionary computing in wireless sensor networks. In Progress in electromagnetics research symposium (pp. 883–886).

  20. Bhaskar, N., Subhabrata, B., & Soumen, P. (2010). Genetic algorithm based optimization of clustering in ad-hoc networks. International Journal of Computer Science and Information Security, 7(1), 165–169.

    Google Scholar 

  21. Asim, M., & Mathur, V. (2013). Genetic algorithm based dynamic approach for routing protocols in mobile ad hoc networks. Journal of Academia and Industrial Research, 2(7), 437–441.

    Google Scholar 

  22. Karimi, A., Abedini, S., Zarafshan, F., & Al-Haddad, S. (2013). Cluster head selection using fuzzy logic and chaotic based genetic algorithm in wireless sensor network. Journal of Basic and Applied Scientific Research, 3(4), 694–703.

    Google Scholar 

  23. Rana, K., & Zaveri, M. (2013). Synthesized cluster head selection and routing for two tier wireless sensor network. Journal of Computer Networks and Communications, 13(3). doi:10.1155/2013/578241.

  24. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In The Hawaii International Conference on System Sciences, Maui, Hawaii.

  25. Nadeem, Q., Rasheed, M., Javaid1, N., Khan, Z., Maqsood, Y., & Din, A. (2013). M-GEAR gateway-based energy-aware multi-hop routing protocol for WSNs. In Eighth international conference on broadband and wireless computing and communication and applications (pp. 164–169).

  26. Li, Q., & Qingxin, Z. (2006). Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Computer Communications, 29(12), 2230–2237.

    Article  Google Scholar 

  27. Lindsey, S., & Raghavendra, C. (2002). Pegasis power-efficient gathering in sensor information systems. IEEE Aerospace Conference Proceedings, 3, 1125–1130.

    Google Scholar 

  28. Kashaf, A., Javaid, N., Khan, Z., & Khan, I. (2012). TSEP: Threshold-sensitive stable election protocol for WSNs. In Conference on Frontiers of information technology (pp. 164–168)

  29. Elbhiri, B., Rachid, S., & Elfkihi, S. (2010). Developed distributed energy-effecient clustering (DDEEC) for heterogeneous wireless sensor. In Communications and Mobile Network (pp. 1–4). Rabat.

  30. Qiang, Y., Pei, Bo., Wei, W., & Li, Y. (2015). An efficient cluster head selection approach for collaborative data processing in wireless sensor networks. International Journal of Distributed Sensor Networks, 2015. doi:10.1155/2015/794518.

  31. Pala, V., Yogita, Y., Singh, G., & Yadav, P. (2015). Cluster head selection optimization based on genetic algorithm to prolong lifetime of wireless sensor networks. In Third international conference on recent trends in computing (pp. 1417–1423). Elsevier.

  32. Batra, P., & Kant, K. (2016). Leach-mac a new cluster head selection algorithm for wireless sensor networks. Wireless Networks, 22(1), 49–60.

    Article  Google Scholar 

  33. Diallo, C., Marot, M., & Becker, M. (2010). Single-node cluster reduction in WSN and energy-efficiency during cluster formation. In The 9th IFIP annual mediterranean ad hoc networking conference, France.

  34. Chengfa, L., Mao, Y., Guihai, C., & Lie, W. (2005). An energy-efficient unequal clustering mechanism for wireless sensor networks. In IEEE international conference on mobile adhoc and sensor systems, Washington, DC.

  35. Ahmed, G., Khan, N., & Khalid, Z. (2014). Cluster head selection using decision trees for wireless sensor networks. In Sensor Networks and Information Processing (pp. 173–178).

  36. Tian, J., Gao, M., & Ge, G. (2016). Wireless sensor network node optimal coverage based on improved genetic algorithm and binary ant colony algorithm. EURASIP Journal on Wireless Communications and Networking, 2016, 104. doi:10.1186/s13638-016-0605-5.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt

    Mohamed Elhoseny

  2. Department of Information Technology, Alzahra Collage for Women, Muscat, Oman

    Ahmed Farouk

  3. Department of Electronic Information Engineering, Nanchang University, Nanchang, 330031, China

    Nanrun Zhou

  4. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Ming-Ming Wang

  5. Department of Physics, King Abdulaziz University, Jeddah, Saudi Arabia

    Soliman Abdalla

  6. Department of Physics, Universitat de les Illes Balears, 07122, Palmade Mallorca, Balearic Islands, Spain

    Josep Batle

Authors
  1. Mohamed Elhoseny
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ahmed Farouk
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Nanrun Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ming-Ming Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Soliman Abdalla
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Josep Batle
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Mohamed Elhoseny.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Elhoseny, M., Farouk, A., Zhou, N. et al. Dynamic Multi-hop Clustering in a Wireless Sensor Network: Performance Improvement. Wireless Pers Commun 95, 3733–3753 (2017). https://doi.org/10.1007/s11277-017-4023-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-4023-8

Keywords