Wireless Networks

, Volume 19, Issue 7, pp 1755–1768 | Cite as

Algorithms for finding best locations of cluster heads for minimizing energy consumption in wireless sensor networks

  • Yihui Li
  • Gaoxi Xiao
  • Gurpreet Singh
  • Rashmi Gupta


Clustering is a widely adopted energy-saving technique in wireless sensor networks (WSNs). In this paper, we study algorithms for finding the best locations of cluster heads in WSNs to minimize the overall energy consumption. Specifically, based on the assumption that the global information of all the sensors’ locations or location distribution is available, algorithms are proposed for finding (1) the best location of the cluster head in a single given cluster; (2) the best formation of a given number of clusters where each cluster head has to communicate with base station directly; and (3) the best formation of a given number of clusters where there can be ad-hoc transmission between cluster heads, respectively. For each case, algorithms are designed for free-space and multipath energy consumption models respectively. Theoretical analysis and extensive simulation results show that the proposed algorithms can steadily and quickly achieve satisfactory results. The calculation results of the proposed algorithms provide a useful benchmark for evaluating various local information-based distributed clustering schemes or schemes based on partial or inaccurate global information.


Wireless sensor networks Clustering algorithms Energy efficiency Free-space model Multipath model 


  1. 1.
    Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRefGoogle Scholar
  2. 2.
    Karl, H., & Willig, A. (2005). Protocols and architectures for wireless sensor networks. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  3. 3.
    Chan, H., & Perrig, A. (2004). ACE: An emergent algorithm for highly uniform cluster formation. In Wireless Sensor Networks. Lecture Notes in Computer Science, Vol. 2920, pp. 154–171.Google Scholar
  4. 4.
    Bajaber, F., & Awan, I. (2008). Dynamic/static clustering protocol for wireless sensor network. In Proceedings of the IEEE 2nd European Symposium on Computer Modeling and Simulation (pp. 524–529).Google Scholar
  5. 5.
    Heinzelman, W. B., Chandrakasan, A. P., Balakrishnan, H., & Mit, C. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.CrossRefGoogle Scholar
  6. 6.
    Muruganathan, S. D., Ma, D. C. F., Bhasin, R. I., & Fapojuwo, A. O. (2005). A centralized energy-efficient routing protocol for wireless sensor networks. IEEE Communications Magazine, 43(3), S8–S13.CrossRefGoogle Scholar
  7. 7.
    Tarannum, S., Srividya, S., Asha, D. S., Padmini, R., Nalini, L., Venugopal, K. R., et al. (2008). Dynamic hierarchical communication paradigm for Wireless Sensor Networks: A centralized, energy efficient approach. In 11th IEEE Singapore International Conference on Communication Systems, Singapore (pp. 959–963).Google Scholar
  8. 8.
    Soude, H., & Mehat, J. (2006). Energy efficient clustering algorithm for wireless sensor networks. In International conference on wireless and mobile communications (ICWMC ‘06), 2931 July 2006.Google Scholar
  9. 9.
    Ci, S., Guizani, M., & Sharif, H. (2007). Adaptive clustering in wireless sensor networks by mining sensor energy data. Computer Communications, 30(14–15), 2968–2975.CrossRefGoogle Scholar
  10. 10.
    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.CrossRefGoogle Scholar
  11. 11.
    Huang, Y. F., Luo, W. H., Sum, J., Chang, L. H., Chang, C. W., & Chen, R. C. (2007). Lifetime Performance of an energy efficient clustering algorithm for cluster-based wireless sensor networks. In Frontiers of High Performance Computing and Networking ISPA 2007 Workshops. Lecture Notes in Computer Science, Vol. 4743, pp. 455–464.Google Scholar
  12. 12.
    Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.Google Scholar
  13. 13.
    Qin, M., & Zimmermann, R. (2007). VCA: an energy-efficient voting-based clustering algorithm for sensor networks. Journal of Universal Computer Science, 13(1), 87–109.Google Scholar
  14. 14.
    Manjeshwar, A., & Agrawal, D. P. (2001). TEEN: a routing protocol for enhanced efficiency in wireless sensor networks. In Proceedings of the 1st International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing, San Francisco, CA (pp. 2009-2015).Google Scholar
  15. 15.
    Manjeshwar, A., & Agrawal, D. P. (2002). APTEEN: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In Proceedings International Parallel and Distributed Processing Symposium, IPDPS 2002 (pp. 195–202).Google Scholar
  16. 16.
    Ye, M., Li, C., Chen, G., & Wu, J. (2005). EECS: An energy efficient clustering scheme in wireless sensor networks. In 24th IEEE International Performance, Computing, and Communications Conference, IPCCC 2005 (pp. 535–540).Google Scholar
  17. 17.
    Kumar, D., Aseri, T. C., & Patel, R. B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications, 32(4), 662–667.CrossRefGoogle Scholar
  18. 18.
    Kamimura, J., Wakamiya, N., & Murata, M. (2006). A distributed clustering method for energy-efficient data gathering in sensor networks. International Journal of Wireless and Mobile Computing, 1(2), 113–120.CrossRefGoogle Scholar
  19. 19.
    Agarwal, P. K., & Procopiuc, C. M. (2002). Exact and approximation algorithms for clustering. Algorithmica, 33(2), 201–226.MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Anker, T., Bickson, D., Dolev, D., & Hod, B. (2008). Efficient clustering for improving network performance in wireless sensor networks. In Wireless Sensor Networks. Lecture Notes in Computer Science, Vol. 4913, pp. 221–236.Google Scholar
  21. 21.
    Chen, G., Li, C., Ye, M., & Wu, J. (2009). An unequal cluster-based routing protocol in wireless sensor networks. Wireless Networks, 15(2), 193–207.CrossRefGoogle Scholar
  22. 22.
    Chen, H., Wu, C. S., Chu, Y. S., Cheng, C. C., & Tsai, L. K. (2007). Energy residue aware (ERA) clustering algorithm for leach-based wireless sensor networks. In Systems and Networks Communications, Second International Conference on ICSNC 2007, 2531 August 2007 (pp. 40).Google Scholar
  23. 23.
    Chiasserini, C. F., Chlamtac, I., Monti, P., & Nucci, A. (2004). An energy-efficient method for nodes assignment in cluster-based Ad Hoc networks. Wireless Networks, 10(3), 223–231.CrossRefGoogle Scholar
  24. 24.
    Jin, Y., Wang, L., Kim, Y., & Yang, X. (2008). EEMC: An energy-efficient multi-level clustering algorithm for large-scale wireless sensor networks. Computer Networks, 52(3), 542–562.CrossRefMATHGoogle Scholar
  25. 25.
    Loscri, V., Morabito, G., & Marano, S. (2005). A two-levels hierarchy for low-energy adaptive clustering hierarchy (TL-LEACH). In Proceedings of IEEE VTC (Vol. 3, pp. 1809–1813).Google Scholar
  26. 26.
    Nam, D. H., & Min, H. K. (2007). An efficient ad-hoc routing using a hybrid clustering method in a wireless sensor network. In Proceedings of the Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (p. 60).Google Scholar
  27. 27.
    Peng, J., Li, L., & Xu, S. (2007). A novel energy efficient and reliable clustering algorithm in wireless sensor networks. In IET Conference on Wireless, Mobile and Sensor Networks (CCWMSN07), Shanghai (pp. 596–599).Google Scholar
  28. 28.
    Sivalingam, K. M. (2002). Data gathering algorithms in sensor networks using energy metrics. IEEE Transactions on Parallel and Distributed Systems, 13(9), 924–935.CrossRefGoogle Scholar
  29. 29.
    Zhang, M., Gong, C., & Lu, Y. (2008). An novel dynamic clustering algorithm based on geographical location for wireless sensor networks. In 2008 International Symposium on Information Science and Engineering (ISISE), Piscataway, NJ, USA (pp. 565–568).Google Scholar
  30. 30.
    Yin, Y., Shi, J., Li, Y., & Zhang, P. (2006). Cluster head selection using analytical hierarchy process for wireless sensor networks. In 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications Piscataway, NJ, USA (pp. 1–5).Google Scholar
  31. 31.
    Salehpour, A. A., Afzali-Kusha, A., & Mohammadi, S. (2008). Efficient clustering of wireless sensor networks based on memetic algorithm. In IIT 2008 International Conference on Innovations in Information Technology, Piscataway, NJ, USA (pp. 450–454).Google Scholar
  32. 32.
    Indranil, G., Riordan, D., & Srinivas, S. (2005). Cluster-head election using fuzzy logic for wireless sensor networks. In Proceedings of the 3rd Annual Communication Networks and Services Research Conference, Los Alamitos, CA, USA (pp. 255–260).Google Scholar
  33. 33.
    Gu, Y., Wu, Q., & Rao, N. S. V. (2010). Optimizing cluster heads for energy efficiency in large-scale heterogeneous wireless sensor networks. International Journal of Distributed Sensor Networks. doi:10.1155/2010/961591.
  34. 34.
    Baker, D. J., & Ephremides, A. (1981). The architectural organization of a mobile radio network via a distributed algorithm. IEEE Transactions on Communications, CM-29, 11, 1694–1701.CrossRefGoogle Scholar
  35. 35.
    Garcia, F., Solano, J., & Stojmenovic, I. (2003). Connectivity based k-hop clustering in wireless networks. Telecommunication Systems, 22(1), 205–220.Google Scholar
  36. 36.
    Banerjee, S., & Khuller, S. (2001). A clustering scheme for hierarchical control in multi-hop wireless networks. In Proceedings of 20th Joint Conference of the IEEE Computer and Communications Societies (INFOCOM’ 01) Anchorage, AK (Vol. 2, pp. 1028–1037).Google Scholar
  37. 37.
    Bandyopadhyay, S., & Coyle, E. J. (2003). An energy efficient hierarchical clustering algorithm for wireless sensor networks. In Proceedings of Twenty-Second Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM’ 03 (Vol. 3, pp. 1713–1723).Google Scholar
  38. 38.
    Younis, M., Youssef, M., & Arisha, K. (2003). Energy-aware management for cluster-based sensor networks. Computer Networks, 43(5), 649–668.CrossRefGoogle Scholar
  39. 39.
    Xu, K., & Gerla, M. (2002). A heterogeneous routing protocol based on a new stable clustering scheme. In Proceedings of 2002 Military Communications Conference, Piscataway, NJ, USA (Vol. 2, pp. 838–843).Google Scholar
  40. 40.
    Hou, T. C., & Tsai, T. J. (2001). An access-based clustering protocol for multihop wireless ad hoc networks. IEEE Journal on Selected Areas in Communications, 19(7), 1201–1210.CrossRefGoogle Scholar
  41. 41.
    Ye, W., Heidemann, J., & Estrin, D. (2002). An energy-efficient MAC protocol for wireless sensor networks. In Proceedings of IEEE INFOCOM (Vol. 3, pp. 1567–1576).Google Scholar
  42. 42.
    Van Dam, T., & Langendoen, K. (2003). An adaptive energy-efficient MAC protocol for wireless sensor networks. In Proceedings of the 1st international conference on Embedded networked sensor systems, Los Angeles, CA (pp. 171–180).Google Scholar
  43. 43.
    Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30(14–15), 2826–2841.CrossRefGoogle Scholar
  44. 44.
    Rappaport, T. S. (2002). Wireless communications: Principles and practice. New Jersey: Prentice Hall PTR.Google Scholar
  45. 45.
    Vattani, A. (2011). k-means requests exponentially many iterations even in the plane. Discrete and Computational Geometry, 45(4), 596–616.MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Yihui Li
    • 1
  • Gaoxi Xiao
    • 1
  • Gurpreet Singh
    • 2
  • Rashmi Gupta
    • 3
  1. 1.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.NetApp Inc., SunnyvaleCAUSA
  3. 3.Brocade Communications SystemsSan JoseUSA

Personalised recommendations