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Fuzzy based novel clustering technique by exploiting spatial correlation in wireless sensor network

  • Manjeet SinghEmail author
  • Surender Kumar Soni
Original Research
  • 69 Downloads

Abstract

In wireless sensor networks, the event is detected by multiple closely placed sensor nodes. The spatial relationship can be utilized productively in order to conserve the power banks by halting some sensors to transmit the same information. This paper deals with the segregation of network into the correlated clusters based on correlation value. On the one hand, unlike existing clustering techniques relying on residual energy and distance to select cluster heads, this paper defines more realistic three-dimensional correlation model where cluster heads are elected on the basis of the correlation value, residual energy, and required energy. On the other hand, other than developing theoretical three-dimensional correlation model, a fuzzy-based clustering technique is also proposed to further implement the developed correlation model, where the nodes with similar information are gathered in such a way that data from a solitary node suffices the fidelity constraint to the sink. The effects of node density, sensing range, and the threshold value is studied in detail. Also, the correlation model is clubbed with clustering technique to further take the advantages of exploiting spatial correlation at the network layer. The results have revealed that proposed approach extend network lifetime by 30, 35 and 78% as compared to the FBUC, CHEF, and LEACH respectively. The results of clustering using correlation model show that the number of participating nodes get reduced by 33% when correlation threshold value is decreased from 0.8 to 0.6. Also, it is found that network lifetime gets improved by decreasing the correlation threshold value.

Graphical abstract

Keywords

Spatial correlation Wireless sensor network Correlated clusters Fuzzy clustering 

Notes

References

  1. Akyildiz IF, Vuran MC (2010) Wireless sensor networks, vol 4. Wiley, Hoboken.  https://doi.org/10.1002/9780470515181 CrossRefzbMATHGoogle Scholar
  2. Bagci H, Yazici A (2013) An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Appl Soft Comput 13(4):1741–1749.  https://doi.org/10.1016/j.asoc.2012.12.029 CrossRefGoogle Scholar
  3. Gupta I, Riordan D, Sampalli S (2005) Cluster-head election using fuzzy logic for wireless sensor networks. In: 3rd annual conference on communication networks and services research, May 16–18, 2005, Halifax, NS, Canada, pp 255–260.  https://doi.org/10.1109/CNSR.2005.27
  4. Heinzelman W, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: 33rd annual Hawaii international conference on system sciences, Jan 4–7, 2000, Maui, Hawaii, USA, pp 1–10.  https://doi.org/10.1109/HICSS.2000.926982
  5. Heinzelman W, Chandrakasan A, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670.  https://doi.org/10.1109/twc.2002.804190 CrossRefGoogle Scholar
  6. Izadi D, Abawajy J, Ghanavati S (2013) A new energy efficient cluster-head and backup selection scheme in WSN. In: IEEE 14th international conference on Information reuse and integration (IRI), Aug 14–16, 2013, San Francisco, CA, USA, pp 408–415.  https://doi.org/10.1109/IRI.2013.6642500
  7. Izadi D, Abawajy J, Ghanavati S (2015) An alternative clustering scheme in WSN. IEEE Sens J 15:4148–4155.  https://doi.org/10.1109/JSEN.2015.2411598 CrossRefGoogle Scholar
  8. Jin R, Wei N, Shi X, Gao T, Zou J (2011) Clustering routing protocol based on fuzzy inference for WSNs. In: 7th international conference on wireless communications, networking and mobile computing (WiCOM), Sept 23–25, 2011, Wuhan, China, pp 1–4.  https://doi.org/10.1109/wicom.2011.6040346
  9. Kim JM, Park SH, Han YJ, Chung TM (2008) CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks. In: 10th international conference on advanced communication technology (ICACT), Feb 17–20, 2008, Gangwon-Do, South Korea, pp 654–659.  https://doi.org/10.1109/ICACT.2008.4493846
  10. Lee JS, Cheng WL (2012) A fuzzy-logic-based clustering approach for wireless sensor networks using energy predication. IEEE Sens J 12:2891–2897.  https://doi.org/10.1109/JSEN.2012.2204737 CrossRefGoogle Scholar
  11. Logambigai R, Kannan A (2016) Fuzzy logic based unequal clustering for wireless sensor networks. Wirel Netw 22(3):945–957.  https://doi.org/10.1007/s11276-015-1013-1 CrossRefGoogle Scholar
  12. Mhemed R, Aslam N, Phillips W, Comeau F (2012) An energy efficient fuzzy logic cluster formation protocol in wireless sensor networks. Procedia Comput Sci 10:255–262.  https://doi.org/10.1016/j.procs.2012.06.035 CrossRefGoogle Scholar
  13. Nayak P, Devulapalli A (2016) A fuzzy logic-based clustering algorithm for wsn to extend the network lifetime. IEEE Sens J 16:137–144.  https://doi.org/10.1109/JSEN.2015.2472970 CrossRefGoogle Scholar
  14. Pires A, Silva C, Cerqueira E, Monteiro D, Viegas R (2011) CHEATS: a cluster-head election algorithm for WSN using a Takagi-Sugeno fuzzy system. In: IEEE Latin-American conference on communications (LATINCOM), Oct 24–26, 2011, Belem do Para, Brazil, pp 1–6.  https://doi.org/10.1109/LatinCOM.2011.6107427
  15. Selvi M, Logambigai R, Ganapathy S, Ramesh LS, Nehemiah HK, Arputharaj K (2016) Fuzzy temporal approach for energy efficient routing in WSN. In: Proceedings of the international conference on informatics and analytics, August 25–26, 2016, Pondicherry, India, pp 1–5.  https://doi.org/10.1145/2980258.2982109
  16. Selvi M, Logambigai R, Ganapathy S, Nehemiah HK, Arputharaj K (2017a) An intelligent agent and FSO based efficient routing algorithm for wireless sensor network. In: Second international conference on recent trends and challenges in computational models (ICRTCCM), Feb 3–4, 2017, Tindivanam, India, pp 100–105.  https://doi.org/10.1109/ICRTCCM.2017.43
  17. Selvi M, Velvizhy P, Ganapathy S, Nehemiah HK, Kannan A (2017b) A rule based delay constrained energy efficient routing technique for wireless sensor networks. Cluster Comput.  https://doi.org/10.1007/s10586-017-1191-y Google Scholar
  18. Shakya RK, Singh YN, Verma NK (2013) Generic correlation model for wireless sensor network applications. IET Wirel Sens Syst 3:266–276.  https://doi.org/10.1049/iet-wss.2012.0094 CrossRefGoogle Scholar
  19. Singh M, Soni SK (2017) A comprehensive review of fuzzy-based clustering techniques in wireless sensor networks. Sens Rev 37(3):289–304.  https://doi.org/10.1108/SR-11-2016-0254 CrossRefGoogle Scholar
  20. Singh M, Soni S, Kumar V (2016) Clustering using fuzzy logic in wireless sensor networks. In: 3rd international conference on computing for sustainable global development (INDIACom), March 16–18, 2016, New Delhi, India, pp 1669–1674Google Scholar
  21. Sivanandam SN, Sumathi S, Deepa SN (2007) Introduction to fuzzy logic using MATLAB. Springer, Berlin.  https://doi.org/10.1007/978-3-540-35781-0 CrossRefzbMATHGoogle Scholar
  22. Tam NT, Hai DT (2016) Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization. Wirel Netw.  https://doi.org/10.1007/s11276-016-1412-y Google Scholar
  23. Vuran M, Akyildiz I (2006) Spatial correlation-based collaborative medium access control in wireless sensor networks. IEEE/ACM Trans Netw 14(2):316–329.  https://doi.org/10.1109/TNET.2006.872544 CrossRefGoogle Scholar
  24. Vuran M, Akan Ö, Akyildiz I (2004) Spatio-temporal correlation: theory and applications for wireless sensor networks. Comput Netw 45(3):245–259.  https://doi.org/10.1016/j.comnet.2004.03.007 CrossRefzbMATHGoogle Scholar
  25. Xu J, Liu W, Lang F, Zhang Y, Wang C (2010) Distance measurement model based on RSSI in WSN. Wirel Sens Netw 2(8):606–611.  https://doi.org/10.4236/wsn.2010.28072 CrossRefGoogle Scholar
  26. Yuan HY, Yang SQ, Yi YQ (2011) An Energy-efficient unequal clustering method for wireless sensor networks. In: International conference on computer and management (CAMAN), May 19–21, 2011, Wuhan, China, pp 1–4.  https://doi.org/10.1109/CAMAN.2011.5778810
  27. Zheng G, Tang S (2011) Spatial correlation-based MAC protocol for event-driven wireless sensor networks. J Netw 6(1):121–128.  https://doi.org/10.4304/jnw.6.1.121-128 Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringNational Institute of TechnologyHamirpurIndia

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