Advertisement

Wireless Networks

, Volume 25, Issue 1, pp 455–470 | Cite as

An adaptive clustering algorithm for dynamic heterogeneous wireless sensor networks

  • Jingxia Zhang
  • Junjie ChenEmail author
Article
  • 135 Downloads

Abstract

In the heterogeneous wireless sensor networks, most algorithms assume that nodes are heterogeneous in terms of their initial energy (we refer to as static energy heterogeneity). However, little research focuses on dynamic energy heterogeneity, which means that energy heterogeneity of nodes results from adding a percentage of the population of sensor nodes to the network when the operation of the network evolves. In this paper, we combine the idea of static energy heterogeneity with that of dynamic energy heterogeneity and then propose a dynamic model for heterogeneous wireless sensor networks. We refer to this dynamic model as dynamic heterogeneous wireless sensor networks (DHWSNs). Furthermore, we give a detailed estimation and analysis of this dynamic model in terms of the lifetime and data packets of the network. Moreover, we optimize the number of clusters for DHWSNs. In order to adapt the dynamic change of topology in DHWSNs, an adaptive clustering algorithm for dynamic heterogeneous wireless sensor networks (ACDHs) is proposed. In ACDHs, the cluster head is elected according to the initial energy in each node, the remaining energy in each node, and the average energy of the network. Simulations show that by adjusting dynamic parameters and heterogeneity parameters, ACDHs yields longer lifetime and more data packets of the network compared with current homogeneous and heterogeneous clustering algorithms.

Keywords

Wireless sensor networks Clustering Energy efficiency Heterogeneity Routing protocol 

Notes

Acknowledgments

This work was supported by the Major Project of National Science and Technology Support Program (2014BAD08B03), the Sanxin Fishery Project of Jiangsu Province (Y2016-3), the Science and Technology Special Fund of North Jiangsu Province (BN2014085) and the Agricultural Science and Technology Support Program of Jiangsu Province (BN2014312).

References

  1. 1.
    Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.CrossRefGoogle Scholar
  2. 2.
    Al-Karaki, J. N., & Kamal, A. E. (2004). Routing techniques in wireless sensor networks: A survey. IEEE Wireless Communications, 11(6), 6–28.CrossRefGoogle Scholar
  3. 3.
    Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67, 104–122.CrossRefGoogle Scholar
  4. 4.
    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(2), 623–645.CrossRefGoogle Scholar
  5. 5.
    Heinzelman, W. R., 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.Google Scholar
  6. 6.
    Manjeshwar, A., & Agrawal, D. P. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In Proceedings of 15th international parallel and distributed processing symposium. San Francisco, California, USA.Google Scholar
  7. 7.
    Manjeshwar, A., & Agrawal, D. P. (2002). APTEEN: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In Proceedings of international parallel and distributed processing symposium (IPDPS 2002). Ft. Lauderdale, Florida, USA.Google Scholar
  8. 8.
    Tanwar, S., Kumar, N., & Rodrigues, J. J. P. C. (2015). A systematic review on heterogeneous routing protocols for wireless sensor network. Journal of Network and Computer Applications, 53, 39–56.CrossRefGoogle Scholar
  9. 9.
    Elhoseny, M., Yuan, X., Yu, Z., & Mao, C. (2015). Balancing energy consumption in heterogeneous wireless sensor networks using genetic algorithm. IEEE Communications Letters, 19(12), 2194–2197.CrossRefGoogle Scholar
  10. 10.
    Qing, L., Zhu, Q., & Wang, M. (2006). Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Computer Communications, 29(12), 2230–2237.CrossRefGoogle Scholar
  11. 11.
    Smaragdakis, G., Matta, I., & Bestavros, A. (2004). SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks. In Second international workshop on sensor and actor network protocols and applications (SANPA 2004).Google Scholar
  12. 12.
    Kumar, D., Aseri, T. C., & Patel, R. B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications, 32(4), 662–667.CrossRefGoogle Scholar
  13. 13.
    Zhou, H., Wu, Y., Hu, Y., & Xie, G. (2010). A novel stable selection and reliable transmission protocol for clustered heterogeneous wireless sensor networks. Computer Communications, 33(15), 1843–1849.CrossRefGoogle Scholar
  14. 14.
    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.CrossRefGoogle Scholar
  15. 15.
    Tong, M., & Tang M. (2010). LEACH-B: An improved LEACH protocol for wireless sensor network. In 6th international conference on wireless communications networking and mobile computing (WiCOM). Chengdu, China.Google Scholar
  16. 16.
    Abdulsalam, H. M., & Ali, B. A. (2013). W-LEACH based dynamic adaptive data aggregation algorithm for wireless sensor networks. International Journal of Distributed Sensor Networks, 2013, 1–11.CrossRefGoogle Scholar
  17. 17.
    Salim, A., Osamy, W., & Khedr, A. M. (2014). IBLEACH: Intra-balanced LEACH protocol for wireless sensor networks. Wireless Networks, 20(6), 1515–1525.CrossRefGoogle Scholar
  18. 18.
    Soro, S., & Heinzelman, W. B. (2005). Prolonging the lifetime of wireless sensor networks via unequal clustering. In Proceedings of the 19th IEEE international parallel and distributed processing symposium (IPDPS’05). Denver, Colorado.Google Scholar
  19. 19.
    Chen, G., Li, C., Ye, M., & Wu, J. (2009). An unequal cluster-based routing protocol in wireless sensor networks. Wireless Networks, 15(2), 193–207.CrossRefGoogle Scholar
  20. 20.
    Tanwar, S., Kumar, N., & Niu, J. (2014). EEMHR: Energy-efficient multilevel heterogeneous routing protocol for wireless sensor networks. International Journal of Communication Systems, 27(9), 1289–1318.CrossRefGoogle Scholar
  21. 21.
    Faisal, S., Javaid, N., Javaid, A., Khan, M. A., Bouk, S. H., & Khan, Z. A. (2013). Z-SEP: Zonal-stable election protocol for wireless sensor networks. Journal of Basic and Applied Scientific Research (JBASR), 3(5), 132–139.Google Scholar
  22. 22.
    Kashaf, A., Javaid, N., Khan Z. A., & Khan, I. A. (2012). TSEP: Threshold-sensitive stable election protocol for WSNs. In 10th international conference on frontiers of information technology (FIT) (pp. 164–168). Islamabad, Pakistan.Google Scholar
  23. 23.
    Benkirane, S., Benihssane, A., Hasnaoui, M. L., & Laghdir, M. (2012). Distance-based stable election protocol (DB-SEP) for heterogeneous wireless sensor network. International Journal of Computer Applications, 58(16), 9–15.CrossRefGoogle Scholar
  24. 24.
    Kumar, D., Aseri, T. C., & Patel, R. B. (2011). Multi-hop communication routing (MCR) protocol for heterogeneous wireless sensor networks. International Journal of Information Technology, Communications and Convergence, 1(2), 130–145.CrossRefGoogle Scholar
  25. 25.
    Elbhiri, B., Saadane, R., Fkihi, S. E., & Aboutajdine, D. (2010). Developed distributed energy-efficient clustering (DDEEC) for heterogeneous wireless sensor networks. In 5th international symposium on I/V communications and mobile network (ISVC). Rabat.Google Scholar
  26. 26.
    Saini, P., & Sharma, A. K. (2010). E-DEEC-enhanced distributed energy efficient clustering scheme for heterogeneous WSN. In 1st international conference on parallel, distributed and grid computing (PDGC—2010) (pp. 205–210). Waknaghat, Solan, H.P., India.Google Scholar
  27. 27.
    Javaid, N., Qureshi, T. N., Khan, A. H., Iqbal, A., Akhtar, E., & Ishfaq, M. (2013). EDDEEC: Enhanced developed distributed energy-efficient clustering for heterogeneous wireless sensor networks. In 4th international conference on ambient systems, networks and technologies (ANT 2013). Halifax, Nova Scotia, Canada.Google Scholar
  28. 28.
    Qureshi, T. N., Javaid, N., Khan, A. H., Iqbal, A., Akhtar, E., & Ishfaq, M. (2013). BEENISH: Balanced energy efficient network integrated super heterogeneous protocol for wireless sensor networks. In 4th international conference on ambient systems, networks and technologies (ANT-2013). Halifax, Nova Scotia, Canada.Google Scholar
  29. 29.
    Javaid, N., Mohammad, S. N., Latif, K., Qasim, U., Khan, Z. A., & Khan, M. A. (2013). HEER: Hybrid energy efficient reactive protocol for wireless sensor networks. In 2013 Saudi international electronics, communications and photonics conference (SIECPC). Riyadh, Saudi Arabia.Google Scholar
  30. 30.
    Aslam, M., Shah, T., Javaid, N., Rahim, A., Rahman, Z., & Khan, Z. A. (2012). CEEC: Centralized energy efficient clustering a new routing protocol for WSNs. In 9th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks (SECON) (pp. 103–105).Seoul, Korea.Google Scholar
  31. 31.
    Heinzelman, W. (2000). Application-specific protocol architecture for wireless networks, Ph.D. Thesis, Massachusetts Institute of Technology.Google Scholar
  32. 32.
    Bandyopadhyay, S., & Coyle, E. J. (2004). Minimizing communication costs in hierarchically-clustered networks of wireless sensors. Computer Networks, 44(1), 1–16.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Instrument Science and EngineeringSoutheast UniversityNanjingChina

Personalised recommendations