Skip to main content

Advertisement

SpringerLink
Log in

CGC: centralized genetic-based clustering protocol for wireless sensor networks using onion approach

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

Wireless sensor networks consist of a large number of nodes which are distributed sporadically in a geographic area. The energy of all nodes on the network is limited. For this reason, providing a method of communication between nodes and network administrator to manage energy consumption is crucial. For this purpose, one of the proposed methods with high performance, is clustering methods. The big challenge in clustering methods is dividing network into several clusters that each cluster is managed by a cluster head (CH). In this paper, a centralized genetic-based clustering (CGC) protocol using onion approach is proposed. The CGC protocol selects the appropriate nodes as CHs according to three criteria that ultimately increases the network life time. This paper investigates the genetic algorithm (GA) as a dynamic technique to find optimum CHs. Furthermore, an innovative fitness function according to the specified parameters is presented. Each chromosome which minimizes fitness function, is selected by base station (BS) and its nodes are introduced to the whole network as proper CHs. After the selection of CHs and cluster formation, for upper level routing between CHs, we define a novel concept which is called Onion Approach. We divide the network into several onion layers in order to reduce the communication overhead among CH nodes. Simulation results show that the implementation of the proposed method by GA and using onion approach, presents better efficiency compared with other previous methods. Conducted simulation results show that the CGC protocol has done significant improvement in terms of running time of the algorithm, the number of nodes alive, first node death, last node death, the number of packets received by the BS, and energy consumption of the network.

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Fuad, B., & Irfan, A. (2014). An efficient cluster-based communication protocol for wireless sensor networks. Telecommunication Systems, 55(3), 387–401.

    Article  Google Scholar 

  2. Getsy, S. S., & Sridharan, D. (2014). Routing in mobile wireless sensor network: A survey. Telecommunication Systems, 57(1), 51–79.

    Article  Google Scholar 

  3. Hatamian, M., Barati, H., & Movaghar, A. (2015). A new greedy geographical routing in wireless sensor networks. Journal of Advances in Computer Research, 6(1), 9–18.

    Google Scholar 

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

    Article  Google Scholar 

  5. 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(2014), 127–140.

    Article  Google Scholar 

  6. Li, X., & Guan, X. (2013). Energy-aware routing in wireless sensor networks using local betweenness centrality. International Journal of Distributed Sensor Networks, 2013, 1–9.

    Google Scholar 

  7. Wei, H., Lee, C., Huang, F., Hsu, T., & Shih, W. (December 2012). EEGRA: Energy efficient geographic routing algorithms for wireless sensor network. In IEEE 12th International symposium on pervasive systems, algorithms and networks (ISPAN) (pp. 104–113).

  8. Lonare, S., & Wahane, G. (July 2013). A survey on energy efficient routing protocols in wireless sensor networks. In Computing, communications and networking technologies conference (ICCCNT) (pp. 1–5).

  9. Akkaya, K., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Elsevier Adhoc Network Journal, 3(3), 325–349.

    Article  Google Scholar 

  10. Bsoul, M., Al-Khasawneh, A., Abdallah, A. E., Abdallah, E. E., & Obeidat, I. (2013). An energy-efficient threshold-based clustering protocol for wireless sensor networks. Wireless Personal Communications, 70(1), 99–112.

    Article  Google Scholar 

  11. Kumar, D. (2014). Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks. IET Wireless Sensor Systems, 4(1), 9–16.

    Google Scholar 

  12. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (January 2000). Energy-efficient communication protocol for wireless microsensor network. In Hawaii international conference on system sciences (pp. 1–10).

  13. 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.

    Article  Google Scholar 

  14. Chen, J., Li, Z., & Kuo, Y. (2013). A centralized balance clustering routing protocol for wireless sensor network. Wireless Personal Communications, 72(1), 623–634.

    Article  Google Scholar 

  15. Young-Long, C., Neng-Chung, W., Yi-Nung, S., & Jia-Sheng, L. (2014). Improving low-energy adaptive clustering hierarchy architectures with sleep mode for wireless sensor networks. Wireless Personal Communications, 75(1), 349–368.

    Article  Google Scholar 

  16. Hussain, S., Matin, A. W., & Islam, O. (2007). Genetic algorithm for hierarchical wireless sensor networks. Journal of Networks, 1(5), 87–97.

    Google Scholar 

  17. Peiravi, A., Rajabi Mashhadi, H., & Javadi, S. H. (2013). An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm. International Journal of Communication Systems, 26(1), 114–126.

    Article  Google Scholar 

  18. Kuila, P., Gupta, S. K., & Jana, P. K. (2013). A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm and Evolutionary Computation, 12(2013), 48–56.

    Article  Google Scholar 

  19. Low, C. P., Fang, C., Ng, J. M., & Ang, Y. H. (2008). Efficient load-balanced clustering algorithms for wireless sensor networks. Computer Communications, 31(4), 750–759.

  20. Zhang, H., Zhang, S., & Bu, W. (2014). A clustering routing protocol for energy balance of wireless sensor network based on simulated annealing and genetic algorithm. International Journal of Hybrid Information Technology, 7(2), 71–82.

    Article  Google Scholar 

  21. Yang, X. (2014). Nature-inspired optimization algorithms. Waltham: Elsevier Science Publishing Co Inc.

    Google Scholar 

  22. Guo, P., Wang, X., & Han, Y. ( October 2010). The enhanced genetic algorithms for the optimization design. In 3rd International conference on biomedical engineering and informatics (BMEI) (pp. 2990–2994).

  23. Tabassum, M., & Mathew, K. (2014). A genetic algorithm analysis towards optimization solutions. International Journal of Digital Information and Wireless Communications, 4(1), 124–142.

    Article  Google Scholar 

  24. Sahoo, B., Mohapatra, S., & Jena, S. K. (July 2008). A genetic algorithm based dynamic load balancing scheme for heterogeneous distributed systems. In Proceedings of the International Conference on parallel and distributed processing techniques and applications (PDPTA) (pp. 499-505).

  25. Zhu, Y., Wu, W., Pan, J., & Tang, Y. (2010). An energy-efficient data gathering algorithm to prolong lifetime of wireless sensor networks. Computer Communications, 33(5), 639–647.

    Article  Google Scholar 

  26. Lin, C., Huang, C., & Fang, R. (2008). A power-efficient data gathering scheme on grid sensor networks. In Proceedings of the 8th WSEAS international conference on multimedia systems and signal processing, (pp. 142–147).

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

    Article  Google Scholar 

  28. Hatamian, M., Ahmadpoor, S. S., Berenjian, S., Razeghi, B. & Barati, H. (2015) A. A novel evolutionary clustering protocol for wireless sensor networks. In Proceedings of the 6th IEEE computer society international conference on computing, communications and networking technologies (ICCCNT) USA, (Accepted).

  29. Botta, M., & Simek, M. (2013). Adaptive distance estimation based on RSSI in 802.15.4 network. Radioengineering Journal, 22(4), 1162–1168.

    Google Scholar 

  30. Knuth, D. E. (1976). Big Omicron and big Omega and big Theta. ACM SIGACT News, 8(2), 18–24.

    Article  Google Scholar 

  31. Kuila, P., Gupta, S. K., & Jana, P. K. (2013). A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm and Evolutionary Computation, 12(2013), 48–56.

    Article  Google Scholar 

  32. Li, F., & Wang, J. (2011). A best clustering scheme based on simulated annealing algorithm in wireless sensor networks. Chinese Journal of Sensors and Actuators, 24(6), 900–904.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran

    Majid Hatamian & Hamid Barati

  2. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Ali Movaghar

  3. Department of Computer Science, Rutgers University, New Brunswick, NJ, USA

    Alireza Naghizadeh

Authors
  1. Majid Hatamian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamid Barati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Movaghar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alireza Naghizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Majid Hatamian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hatamian, M., Barati, H., Movaghar, A. et al. CGC: centralized genetic-based clustering protocol for wireless sensor networks using onion approach. Telecommun Syst 62, 657–674 (2016). https://doi.org/10.1007/s11235-015-0102-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-015-0102-x

Keywords

Search

Navigation