Skip to main content

Advertisement

SpringerLink
Log in

A Cognitive Multi-hop Clustering Approach for Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Wireless sensor networks (WSNs) exert a pull on the modern research community towards many design challenges, especially, constraints on their lifetimes. Solutions proposed to save energy in WSNs posses their own merits and limitations. The trends evolved from the perspective of improving performance and scalability of conventional clustering approaches. They emerge by adopting cognitive techniques to handle uncertainty and instability present in the application atmosphere. This paper proposes a clustering approach for WSNs, namely, energy aware fuzzy clustering algorithm (EAFCA) which achieves lifetime enhancement in CH election, data aggregation and inter-cluster traffic phases of a multi-hop WSN environment. This algorithm contributes the process of cluster head (CH) election in a cluster in an energy-efficient manner by considering the residual energy, mean distance to 1-hop neighbors and 2-hop coverage of the competing nodes. The elected CH aggregates the data from all the sensor nodes of its cluster and forwards the same to the base station. Performance evaluation of the proposed EAFCA is done with popular clustering algorithms and the experimental results show improvement in terms of lifetime of WSNs under first node dies and half of the nodes alive scenarios.

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

Similar content being viewed by others

References

  1. Abbasi, A., & Younis, M. A. (2007). Survey on clustering algorithms for wireless sensor networks. Computer Communication, 30(28), 26–41.

    Google Scholar 

  2. Song, M., & Cheng-lin, Z. (2011). Unequal clustering algorithm for WSN based on fuzzy logic and improved ACO. The Journal of China Universities of Posts and Telecommunications, 18(9), 89–97.

    Google Scholar 

  3. Soro, S., & Heinzelman, W. (2005). Prolonging the lifetime of wireless sensor networks via unequal clustering. In IEEE parallel and distributed processing symposium (IPDPS) (pp. 236–240). Denver, Colorado, USA.

  4. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Hawaii International Conference on System Sciences (HICSS) (pp. 10–19). Wailea Maui, Hawaii.

  5. Cui, X. (2007). Research and improvement of LEACH protocol in wireless sensor networks. In International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE) (pp. 251–254). Hangzhou, China.

  6. Sang, H. K., & Thinh, N. (2012). Distance based thresholds or cluster head selection in wireless sensor networks. IEEE Communication Letters, 16(9), 1396–1399.

    Article  Google Scholar 

  7. Ran, G., Zhang, H., & Gong, S. (2010). Improving on LEACH protocol of wireless sensor networks using fuzzy logic. Journal of Information & Computational Science, 7, 767–775.

    Google Scholar 

  8. Jindal, P., & Gupta, V. (2013). Study of energy efficient routing protocols of wireless sensor networks and their further researches: A survey. Journal of Computer Science Communication Engineering, 2(2), 57–62.

    Google Scholar 

  9. Handy, M., Haase, M., & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In Proceedings of IEEE 4th International Workshop on Mobile and Wireless Communications Network (MWCN) (pp. 368–372). Citeseer

  10. Mhatre, V., & Rosenberg, C. (2004). Homogeneous vs heterogeneous clustered sensor networks: A comparative study. In IEEE international conference on communications (ICC) (Vol. 6, pp. 3646–3651). Paris, France.

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

    Article  Google Scholar 

  12. Manjeshwar, A., & Agrawal, D. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In IEEE parallel and distributed processing symposium (IPDPS), SanFrancisco, USA, 2009–2015.

  13. Manjeshwar, A., & Agrawal, D. (2002). APTEEN: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In IEEE parallel and distributed processing symposium (IPDPS) (pp. 195–202). Florida, USA.

  14. Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power efficient gathering in sensor information systems. In IEEE aerospace conference (pp. 1125–1130).

  15. Gupta, D., & Sampalli, S. (2005). Cluster-head election using fuzzy logic for wireless sensor networks. In IEEE conference on communication networks and services research (CNSR) (pp. 255–260). Halifax, Novia Scotia, Canada.

  16. Kim, J.-M., Park, S.-H., Han, Y.-J., & Chung, T.-M. (2008). CHEF: Cluster head election mechanism using fuzzy logic in wireless sensor networks. International Conference on Advanced Communication Technology (ICACT), 1, 654–659.

    Google Scholar 

  17. Bagci, H., & Yazici, A. (2010). An energy aware fuzzy unequal clustering algorithm for wireless sensor networks. In IEEE conference on fuzzy systems (FUZZ) (pp. 1–8). Barcelona, Spain.

  18. Shah, R. C., & Rabaey, J. M. (2002). Energy aware routing for low energy ad hoc sensor networks. IEEE Wireless Communications and Networking Conference, 1, 350–355.

    Google Scholar 

  19. Taheri, H., Neamatollahi, P., Younis, O., Naghibzadeh, S., & Yaghmaee, M. (2012). An energy aware distributed clustering protocol in wireless sensor networks using fuzzy logic. Ad Hoc Networks, 10, 1469–1481.

    Article  Google Scholar 

  20. Younis, O., & Fahmy, S. (2004). HEED: A hybrid energy-efficient distributed clustering approach for ad hoc sensor networks. In Proceedings of IEEE INFOCOM (pp. 1–36). Hong Kong

  21. Sert, S. A., Bagci, H., & Yazici, A. (2015). MOFCA: Multi-objective fuzzy clustering algorithm for wireless sensor networks. Applied Soft Computing, 30, 151–165.

    Article  Google Scholar 

  22. Mainanwal, V., Gupta, M., & Upadhayay, S. K. (2015). A survey on wireless body area network: Security technology and its design methodology issue. In 2015 international conference on innovations in information, embedded and communication systems (ICIIECS) (pp. 1–5). IEEE.

  23. Patel, Y. S., Vyas, S., & Dwivedi, A. K. (2015). A expert system based novel framework to detect and solve the problems in home appliances by using wireless sensors. In 2015 international conference on futuristic trends on computational analysis and knowledge management (ABLAZE) (pp. 459–464). IEEE.

  24. Venkataraman, R., Moeller, S., Krishnamachari, B., & RamaRao, T. (2015). Trust-based backpressure routing in wireless sensor networks. International Journal of Sensor Networks, 17(1), 27–39.

    Article  Google Scholar 

  25. Vidács, A., & Vida, R. (2015). Wireless sensor network based technologies for critical infrastructure systems. In Intelligent monitoring, control, and security of critical infrastructure systems (pp. 301–316). Springer, Berlin.

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Coimbatore Institute of Technology, Coimbatore, 641014, India

    I. S. Akila

  2. Department of CSE, PSG College of Technology, Coimbatore, 641004, India

    R. Venkatesan

Authors
  1. I. S. Akila
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Venkatesan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to I. S. Akila.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akila, I.S., Venkatesan, R. A Cognitive Multi-hop Clustering Approach for Wireless Sensor Networks. Wireless Pers Commun 90, 729–747 (2016). https://doi.org/10.1007/s11277-016-3200-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3200-5

Keywords

Search

Navigation