Considering Lifetime of Sensors for Clusterhead Selection in WSN Using Fuzzy Logic

  • Qi Wang
  • Leonard Barolli
  • Elis Kulla
  • Gjergji Mino
  • Makoto Ikeda
  • Jiro Iwashige
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 253)


In Wireless Sensor Networks (WSN), cluster formation and cluster head selection are critical issues. They can drastically affect the network’s performance in different environments with different characteristics. In order to deal with this problem, we have proposed a fuzzy-based system for cluster-head selection and controlling sensor speed in Wireless Sensor Networks (WSNs). The proposed system is constructed by two Fuzzy Logic Controllers (FLC). We use four input linguistic parameters for evaluating lifetime of a sensor in FLC1. Then, we use the output of FLC1 and two other linguistic parameters as input parameters of FLC2 to control the probability of headcluster selection. By considering the moving speed of the sensor we are able to predict whether the node will leave or stay in the cluster. In this paper, we evaluate FLC1 and FLC2 by simulations and show that they have a good behavior.


Sensor speed WSN Feedback Fuzzy-based Cluster-head 


  1. 1.
    Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–442CrossRefGoogle Scholar
  2. 2.
    Akyildiz IF, Kasimoglu IH (2004) Wireless sensor and actor networks: research challenges. Ad Hoc Netw 2(4):351–367CrossRefGoogle Scholar
  3. 3.
    Giordano S, Rosenberg C (2006) Topics in ad hoc and sensor networks. IEEE Commun Mag 44(4):97CrossRefGoogle Scholar
  4. 4.
    Al-Karaki JN, Kamal AE (2004) Routing techniques in wireless sensor networks: a survey. IEEE Wirel Commun 11(6):6–28CrossRefGoogle Scholar
  5. 5.
    Chatterjee M, Das SK, Turgut D (2002) Wca: a weighted clustering algorithm for mobile ad hoc networks. J Cluster Comput 5(2):193–204CrossRefGoogle Scholar
  6. 6.
    Banerjee S, Khuller S (2001) A clustering scheme for hierarchical control in multi-hop wireless networks. In: Proceedings of IEEE INFOCOM-2001, pp 1028–1037Google Scholar
  7. 7.
    Chen WP, How JC, Sha L (2004) Dynamic clustering for acoustic target tracking in wireless sensor networks. IEEE Trans Mob Comput 3(3):258–271Google Scholar
  8. 8.
    Basagni S (1999) Distributed clustering for ad hoc networks. In: International symposium of parallel architectures, algorithms and networks (I-SPAN’99), pp 310–315Google Scholar
  9. 9.
    Amis AD, Prakash R, Vuong THP, Huynh DT (2000) Max–min d-cluster formation in wireless ad hoc networks. In: Proceedings of IEEE INFOCOM-2000, pp 32–41Google Scholar
  10. 10.
    Chan H, Perrig A (2004) Ace: an emergent algorithm for highly uniform cluster formation. In: Proceedings of European workshop on wireless sensor networks (EWSN-2004) pp 154–171Google Scholar
  11. 11.
    Heinzelman WB, Chandrakasan AP, Balakrishnan H (2004) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wireless Commun 1(4):660–670CrossRefGoogle Scholar
  12. 12.
    Heinzelman WR, 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 (HICSS) pp 3005–3014Google Scholar
  13. 13.
    Lindsey S, Raghavendra C, Sivalingam KM (2002) Data gathering algorithms in sensor networks using energy metrics. IEEE Trans Parallel Distrib Syst 13(9):924–935Google Scholar
  14. 14.
    Chan PML, Sheriff RE, Hu Y, Conforto P, Tocci C (2001) Mobility management incorporating fuzzy logic for a heterogeneous ip environment. IEEE Commun Mag 39(12):42–51Google Scholar
  15. 15.
    Barolli L, Koyama A, Suganuma T, Shiratori N (2003) Gaman: a ga based qos routing method for mobile adhocnetworks. J Interconnect Netw (JOIN) 4(3):251–270CrossRefGoogle Scholar
  16. 16.
    Wang Q, Ando H, Kulla E, Barolli L, Durresi A (2012) A fuzzy-based cluster-head selection system for WSNs considering different parameters. In: Proceedings of the 26th international conference on advanced information networking and applications workshops (WAINA’12), pp 962–967Google Scholar
  17. 17.
    Wang Q, Barolli L, Kulla E, Durresi A, Biberaj A, Takizawa M (2012) A fuzzy-based simulation system for controlling sensor speed in wireless sensor networks. In: Proceedings of 15th international conference on network-based information systems (NBiS’12), pp 208–213Google Scholar
  18. 18.
    Liang Q (2003) A design methodology for wireless personal area networks with power efficiency. In: Proceedings of the wireless communications and networking (WCNC), vol 3, pp 1475–1480Google Scholar
  19. 19.
    Anno J, Barolli L, Xhafa F, Durresi A (2007) A cluster head selection method for wireless sensor networks based on fuzzy logic. In: Proceedings of IEEE TENCON-2007, CD–ROM, 4 pGoogle Scholar
  20. 20.
    Anno J, Barolli L, Durresi A, Xhafa F, Koyama A (2008) A cluster head decision system for sensor networks using fuzzy logic and number of neighbor nodes. In: Proceedings of IEEE Ubi-media 2008, pp 50–56Google Scholar
  21. 21.
    Anno J, Barolli L, Xhafa F, Durresi A, Koyama A (2008) Performance evaluation of two-fuzzy based cluster head selection systems for wireless sensor networks. J Mobile Inf Syst (MIS) 4(4):297–312Google Scholar
  22. 22.
    Mendel JM (1995) Fuzzy logic systems for engineering: a tutorial. Proc IEEE 83(3):345–377CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Qi Wang
    • 1
  • Leonard Barolli
    • 2
  • Elis Kulla
    • 1
  • Gjergji Mino
    • 3
  • Makoto Ikeda
    • 2
  • Jiro Iwashige
    • 2
  1. 1.Graduate School of EngineeringFukuoka Institute of Technology (FIT)FukuokaJapan
  2. 2.Department of Information and Communication EngineeringFukuoka Institute of Technology (FIT)FukuokaJapan
  3. 3.Computer Technology SolutionSalemUSA

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