Skip to main content

Advertisement

Log in

A distributed clustering scheme with self nomination: proposal and application to critical monitoring

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Clustering is a well known methodology to optimize the use of the resources, to lower the congestion and to improve the reliability in self-organized networks as the wireless sensor networks. This paper deals with the proposal of a novel clustering approach based on a low complexity distributed cluster head election based on a two-stage process. In particular, a suitable objective function is introduced in order to take into account the number of 1-hop neighbours (i.e., node degree) and the residual node energy. It is shown in the paper that the proposed protocol achieves remarkable performance improvements with respect to different alternatives, especially in the case of unpredictable scenarios. Moreover, the proposed protocol exhibits self-organize capabilities that are of special interest for critical monitoring applications, in particular when the effect of nodes mobility is significant.

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

Access this article

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Notes

  1. Otherwise the node does not take part in the election.

  2. The factor is introduced in the case of fixed transmitted power levels. If power adaptation is enabled, an intra-cluster communication cost is adopted.

  3. Only in the case of group mobility model, flat routing outperforms hierarchical routing since the mobile nodes are well aggregated.

  4. This represents the typical situation of wide area dense WSNs.

  5. A proper resource management avoiding interference among potentially overlapping clusters and collector is supposed.

  6. The factor is introduced in case of fixed transmitted power levels. If power adaptation is enabled, an intra-cluster communication cost is adopted.

  7. In particular, according to [19] it can been assumed equal to 50 nodes.

  8. Some of the case studies under investigation are described in Sect. 4.

  9. The energy consumption model is introduced in Sect. 4.

  10. The nodes density is normalized to the coverage radius.

  11. This parameter is bounded by the overhead since only a subset of potential CHs can be actually elected.

  12. As a matter of fact, the most suitable candidate is the IEEE 802.15.4 standard.

  13. In the following this number has been assumed equal to 2.

  14. The minimum degree cost has been assumed for HEED [14].

References

  1. Gerla, M., & Tsai, J. T.-C. (1995). Multicluster, mobile, multimedia radio network. Wireless Networks, 1(3), 255–265.

    Article  Google Scholar 

  2. Chen, G., & Stojmenovic, I. (1999). Clustering and routing in mobile wireless networks. Technical report, SITE, University of Ottawa.

  3. Moghaddam, N. M., Zahmati, A. S., & Abolhassani, B. (2007). Lifetime enhancement in WSNs using balanced sensor allocation to cluster heads. In IEEE international conference on signal processing and communications, Nov. 2007. ICSPC 2007 (pp. 101–104).

  4. Sebestyen, G., & Edie, J. (1966). An algorithm for non-parametric pattern recognition. In IEEE transactions on electronic computers, Dec. 1966 (Vol. EC-15. No. 6, pp. 908–915).

  5. Virrankoski, R., & Savvidees, A. (2005). TASC: Topology adaptive spatial clustering for sensor networks. In IEEE international conference on mobile adhoc and sensor systems conference, Nov. 2005 (pp. 10–614).

  6. Chatterjee, M., Das, S. K., & Turgut, D. (2002). WCA: A weighted clustering algorithm for mobile ad hoc networks. In Cluster computing (Vol. 5, No. 2, pp. 192–204). Hingham, MA, USA: Kluwer Academic Publishers.

  7. 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, Jan. 2000 (Vol. 2, pp. 1–10).

  8. Xu, K., Hong, X., & Gerla, M. (2002). An ad hoc network with mobile backbones. In IEEE international conference on communications, ICC 2002 (Vol. 5, pp. 3138–3143).

  9. Abdulsalam, H. M., & Kamel, L. K. (2010). W-LEACH: Weighted low energy adaptive clustering hierarchy aggregation algorithm for data streams in wireless sensor networks. In IEEE international conference on data mining workshops (ICDMW) (pp. 1–8).

  10. Xunbo, L., Na, L., Liang, C., Yan, S., Zhenlin, W., & Zhibin, Z. (2010). W-LEACH: Weighted low energy adaptive clustering hierarchy aggregation algorithm for data streams in wireless sensor networks. In International conference on measuring technology and mechatronics automation (ICMTMA) (Vol. 1, pp. 496–499).

  11. Rahmanian, A., Omranpour, H., Akbari, M., & Raahemifar, K. (2011). A novel genetic algorithm in LEACH-C routing protocol for sensor networks. In 24th Canadian conference on electrical and computer engineering (CCECE) (pp. 001096–001100).

  12. Handy, M. J., 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).

  13. Lu, J.-L., Valois, F., Barthel, D., & Dohler, M. (2007). FISCO: A fully integrated scheme of self-configuration and self-organization for WSN. In IEEE wireless communications and networking conference, WCNC 2007 (Vol. 5, pp. 3370–3375).

  14. 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 

  15. Mohamad, K. D. R., Muhamad, W. N. W., & Kadir, R. A. (2010). Evaluation of stable cluster head election (SCHE) routing protocol for wireless sensor networks. In Proceedings of the international multiconference of engineers and computer scientists (pp. 895–899).

  16. Liu, X. (2012). A survey on clustering routing protocols in wireless sensor networks. Sensors, 12(8), 11113–11153.

    Article  Google Scholar 

  17. Pang, K. L., & Qin, Y. (2006). The comparison study of flat routing and hierarchical routing in ad hoc wireless networks. In Proceedings of the 14th IEEE international conference on networks (pp. 1–6).

  18. Zhang, M., & Chong, P. H. J. (2009). Performance comparison of flat and cluster-based hierarchical ad hoc routing with entity and group mobility. In Proceedings of wireless communications and networking conference (pp. 1–6).

  19. Chiti, F., Fantacci, R., & Lappoli, S. (2010). Contention delay minimization in wireless body sensor networks: A game theoretic perspective. In IEEE Global telecommunications conference (GLOBECOM 2010) (pp. 1–6).

  20. Ira, N., Chaki, R., & Chaki, N. (2010). WACA: A new weighted adaptive clustering algorithm for MANET. In Recent trends in networks and communications (Vol. 90, pp. 270–283). Berlin, Heidelberg: Springer.

  21. Wang, Y.-X., & Bao, F. S. (2007). An entropy-based weighted clustering algorithm and its optimization for ad hoc networks. In 2012 IEEE 8th international conference on wireless and mobile computing, networking and communications (WiMob) (pp. 56–56).

  22. Ryder, G. S., & Ross, K. (2005). A probability collectives approach to weighted clustering algorithms for ad hoc networks. In Proceedings of the third IASTED international conference on communications and computer networks (pp. 94–99).

  23. IEEE Std 802.15.4e-2012 (Amendment to IEEE Std 802.15.4-2011) (2012): IEEE standard for local and metropolitan area networks-part 15.4: Low-rate wireless personal area networks (LR-WPANs) Amendment 1: MAC sublayer, pp. 1–225.

  24. Bougard, B., Catthoor, F., Daly, D. C., Chandrakasan, A., & Dehaene, W. (2005). Energy Efficiency of the IEEE 802.15.4 standard in dense wireless microsensor networks: Modeling and improvement perspectives. In Proceedings of the conference on design, automation and test in Europe (pp. 196–201).

  25. Ramachandran, I., Das, A. K., & Roy, S. (2007). Analysis of the contention access period of IEEE 802.15.4 MAC. In ACM Transactions on Sensor Networks, New York, NY, USA (Vol. 3, No. 1, pp. 196–201).

  26. He, J., Tang, Z., Chen, H.-H., & Zhang, Q. (2009). An accurate and scalable analytical model for IEEE 802.15.4 slotted CSMA/CA networks. IEEE Transactions on Wireless Communications, 8(1), 440–448.

    Article  Google Scholar 

  27. Pollin, S., Ergen, M., Ergen, S., Bougard, B., Der Perre, L., Moerman, I., et al. (2008). Performance analysis of slotted carrier sense IEEE 802.15.4 medium access layer. IEEE Transactions on Wireless Communications, 7(6), 3359–3371.

    Article  Google Scholar 

  28. Faridi, A., Palattella, M. R., Lozano, A., Dohler, M., Boggia, G., Grieco, L. A., et al. (2010). Comprehensive evaluation of the IEEE 802.15.4 MAC layer performance With retransmissions. IEEE Transactions on Vehicular Technology, 59(8), 3917–3932.

    Article  Google Scholar 

  29. Bianchi, G. (2000). Performance analysis of the IEEE 802.11 distributed coordination function. IEEE Journal on Selected Areas in Communications, 18(3), 535–547.

    Article  Google Scholar 

  30. Baranidharan, B., & Shanthi, B. (2010). A survey on energy efficient protocols for wireless sensor networks. International Journal of Computer Applications, 11(10), 35–40.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Francesco Chiti.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chiti, F., Fantacci, R., Mastandrea, R. et al. A distributed clustering scheme with self nomination: proposal and application to critical monitoring. Wireless Netw 21, 329–345 (2015). https://doi.org/10.1007/s11276-014-0785-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-014-0785-z

Keywords

Navigation