Abstract
This paper introduces IFUC, which is an Improved Fuzzy Unequal Clustering scheme for large scale wireless sensor networks (WSNs).It aims to balance the energy consumption and prolong the network lifetime. Our approach focuses on energy efficient clustering scheme and inter-cluster routing protocol. On the one hand, considering each node’s local information such as energy level, distance to base station and local density, we use fuzzy logic system to determine each node’s chance of becoming cluster head and estimate the cluster head competence radius. On the other hand, we use Ant Colony Optimization (ACO) method to construct the energy-aware routing between cluster heads and base station. It reduces and balances the energy consumption of cluster heads and solves the hot spots problem that occurs in multi-hop WSN routing protocol to a large extent. The validation experiment results have indicated that the proposed clustering scheme performs much better than many other methods such as LEACH, CHEF and EEUC.
Similar content being viewed by others
References
Akyildiz I, Su W, Sankarasubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Comm Mag 40(8):102–114
Wang F, Liu J (2011) Networked wireless sensor data collection: issues, challenges, and approaches. IEEE Commun Surv Tutor 13(4):673–687
Belding-Royer E (2002) Hierachical routing in ad hoc mobile networks. Wirel Commun Mob Comput 2(5):515–532
Deosarkar BP, Yadav NS, Yadav RP (2008) Cluster head selection in clustering algorithm for wireless sensor networks: A survey, International Conference on Computing, Communication and Networking, 2008. ICCCN, pp;1–8
Younis O, Krunz M, Ramasubramanian S (2006) Node clustering in wireless sensor networks: recent developments and deployment challenges. IEEE Netw 20(3):20–25
Abbasi AA, Younis M (2007) A survey on clustering algorithms for wireless sensor networks. Comput Commun 30(14–15):2826–2841
Muruganathan SD, Ma DCF, Bhasin RI, Fapojuwo AO (2005) A centralized energy-efficient routing protocol for wireless sensor networks. IEEE Comm Mag 43(3):S8–13
Heizelman W, Chandrakasan A, Balakrishnan H (2002) An application-specific protocol architecture for wireless MicroSensor networks. IEEE Trans Wirel Commun 1(4):660–670
Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for Ah Hoc Sensor networks. IEEE Trans Mob Comput 3(4):660–669
Ye M, Li CF, Chen GH, Wu J(2005) EECS: An Energy Efficient Clustering Scheme in Wireless Sensor Networks, 24th IEEE International Performance, Computing, and Communication Conference, 2005. IPCCC , pp. 535–540
Li CF, Ye M, Chen GH, Wu J(2005) An energy-efficient unequal clustering mechanism for Wireless sensor network. IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 2005. pp. 596–640
Ghosh S, Razouqi Q, Schumacher H, Celmins A (1998) A survey of recent advances in fuzzy logic in telecommunications networks and new challenges. IEEE Trans Fuzzy Syst 6(3):443–447
Zadeh LA (2008) Is there a need for fuzzy logic? Annual Meeting of the North American on Fuzzy Information Processing Society, May 19-22, 2008, NAFIPS, pp. 1–3
Kulkarni RV, Forster A, Venayagamoorthy GK (2011) Computational intelligence in wireless sensor networks: a survey. IEEE Commun Surv Tutor 13(1):68–96
Gupta I, Riordan D, Sampalli S (2005) Cluster-head election using fuzzy logic for wireless sensor networks. In Proceedings of the 3rd Annual Communication Networks and Services Research Conference, pp. 255–260
Kim J, Park S, Han Y et al (2008) CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks. Proceedings of the ICACT, Feb. 17–20, 2008. 654–659
Iyengar SS, Wu HC, Balakrishnan N et al (2007) Biologically inspired cooperative routing for wireless sensor networks. IEEE Syst J 1(1):29–37
Sim KM, Sun WH (2003) Ant colony optimization for routing and load-balancing: Survey and new directions. IEEE Trans Syst Man Cybern 33(5):560–572
Zadeh LA (1996) Fuzzy logic = computing with words. IEEE Trans Fuzzy Syst 4(2):103–111
Minhas MR, Gopalakrishnan S, LeungV (2008) Fuzzy Algorithm for Maxmum Lifetime Routing in Wireless Sensor Networks. In Proceedings of the IEEE Global Telecommunications Conference (Globecom), pp. 1–7
Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learing approach to the travelling salesman. IEEE Trans Evol Comput 1(1):53–66
Kim Y-M, Lee E-J, Park H-S (2011) Ant colony optimization based energy saving routing for energy-efficient networks. IEEE Comm Lett 15(7):779–781
Acknowledgments
This work was supported by National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No.2009ZX03006-006, No. 2009ZX03006-009)and National Natural Science Foundation of China(Grant No. 60902046, No. 60972079),and this research was partly supported by the The Ministry of Knowledge Economy, Korea, under the ITRC support program supervised by the NIPA (NIPA-2011-C1090-1111-0007).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mao, S., Zhao, C., Zhou, Z. et al. An Improved Fuzzy Unequal Clustering Algorithm for Wireless Sensor Network. Mobile Netw Appl 18, 206–214 (2013). https://doi.org/10.1007/s11036-012-0356-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-012-0356-4