Advertisement

Cluster Computing

, Volume 21, Issue 1, pp 91–103 | Cite as

An energy based cluster head selection unequal clustering algorithm with dual sink (ECH-DUAL) for continuous monitoring applications in wireless sensor networks

  • Mukil AlagirisamyEmail author
  • Chee-Onn Chow
Article
  • 110 Downloads

Abstract

The essential sections of the hot spot problem are network lifetime improvements and uniform residual energy distribution in wireless sensor networks (WSN). Clustering of sensor nodes is a significant process that improves network lifetime and energy efficiency of WSN. Usage of equal cluster sizes in WSN causes more energy to be consumed by the cluster heads when the data is routed to sink thus resulting in hot spot problems. Hence, recent research papers focus on unequal clustering where cluster size increases as the distance to the sink increases. In this paper, cluster heads are selected by modifying energy efficient unequal clustering mechanism (EEUC). This process is done in two ways. Firstly, in EEUC, final cluster heads are selected based on the residual energy of the randomly selected tentative cluster heads. In our algorithm, tentative cluster head is selected based on energy based timer, residual energy, node IDs and trust value. Final cluster head selection approach selects final CHs based on competition range, node degree and head count. Secondly, in applications like continuous monitoring, usage of static sink causes the clusters near the sink to die out faster, as the cluster heads in these clusters form the fixed path for data routing, hence resulting in hot spot problems. In this work, an energy based cluster head selection unequal clustering algorithm (ECH-DUAL) using dual (static and mobile) sink is proposed. The simulation shows that proposed system (ECH-DUAL) improves network lifetime of continuous monitoring wireless sensor networks significantly over EEUC.

Keywords

Cluster head Dual sink Energy based timer Hot spot problem Mobile sink Network lifetime Residual energy Static sink and unequal clustering 

Notes

Acknowledgements

This project was supported by the Fundamental Research Grant Scheme (FRGS) (FP006-2016).

References

  1. 1.
    Nayak, B.K., Mishra, M., Rai, S.C., Pradhan, S.K.: A novel cluster head selection method for energy efficient wireless sensor network. In: 2014 International Conference on Information Technology (ICIT), pp. 53–57. IEEE (2014)Google Scholar
  2. 2.
    Yuan, H., Liu, Y., Yu, J: A new energy-efficient unequal clustering algorithm for wireless sensor networks. In: 2011 IEEE International Conference on Computer Science and Automation Engineering (CSAE), vol. 1, pp. 431–434. IEEE (2011)Google Scholar
  3. 3.
    Zhang, R., Ju, L., Jia, Z., Li, X: Energy efficient routing algorithm for WSNs via unequal clustering. In: 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), pp. 1226–1231. IEEE (2012)Google Scholar
  4. 4.
    Thakkar, A., Kotecha, K.: Cluster head election for energy and delay constraint applications of wireless sensor network. Sens. J. IEEE 14(8), 2658–2664 (2014)CrossRefGoogle Scholar
  5. 5.
    Wang, J., Yang, X., Ma, T., Wu, M., Kim, J.U.: An energy-efficient competitive clustering algorithm for wireless sensor networks using mobile sink. Int. J. Grid Distrib. Comput. 5(4), 79–92 (2012)Google Scholar
  6. 6.
    Wei-Qing, Q: Cluster head selection approach based on energy and distance. In: 2011 International Conference on Computer Science and Network Technology (ICCSNT) (vol. 4, pp. 2516-2519). IEEE (2011)Google Scholar
  7. 7.
    Mathew, G., Gupta, A.K., Pant, M.: Timer and distance based routing protocol for continuous monitoring application in WSN. In: 2012 International Conference on Computing Sciences (ICCS), pp. 332-337. IEEE (2012)Google Scholar
  8. 8.
    Rajaram, S., Babu Karuppiah, A., Vinoth Kumar, K.: Secure routing path using trust values for wireless sensor networks. arXiv preprint arXiv:1407.1972 (2014)
  9. 9.
    Bettstetter, C.: Topology properties of ad hoc networks with random waypoint mobility. In: Proceedings of the ACM MobiHoc 03, poster presentation (2003)Google Scholar
  10. 10.
    Guo, L., Xu, H., Harfoush, K.: The node degree for wireless ad hoc networks in shadow fading environments. In: 2011 6th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE (2011)Google Scholar
  11. 11.
    Hekmat, R., Van Mieghem, P.: Degree distribution and hopcount in wireless ad-hoc networks. In: IEEE ICON’03, pp. 603–609 (2003)Google Scholar
  12. 12.
    Penrose, M.D.: On k-connectivity for a geometric random graph. Wiley Random Struct. Algorithms 15(2), 145–164 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Miorandi, D., Altman, E.: Coverage and connectivity of ad hoc networks in presence of channel randomness. In: IEEE Infocom, pp. 491–502 (2005)Google Scholar
  14. 14.
    Dallas, D.P., Hanlen, L.W.: Optimal transmission range and node degree for multi-hop routing in wireless sensor networks. In: Proceedings of the 4th ACM Workshop on Performance Monitoring and Measurement of Heterogeneous Wireless and Wired Networks. ACM (2009)Google Scholar
  15. 15.
    Doci, A., Springer, W., Xhafa, F.: Maximum node degree mobility metric for wireless ad hoc networks. In: The Proceedings of IEEE UBICOMM’08, Valencia, pp. 463–468 (2008)Google Scholar
  16. 16.
    Gupta, S.K., Jain, N., Sinha, P.: Node degree based clustering for WSN. Int. J. Comput. Appl. 40(16), 49–55 (2012)Google Scholar
  17. 17.
    Liu, X., Zhao, H., Yang, X., Li, X.: SinkTrail: a proactive data reporting protocol for wireless sensor networks. IEEE Trans. Comput. 62(1), 151–162 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Pei, E., Han, H., Sun, Z., Shen, B., Zhang, T.: LEAUCH: low-energy adaptive uneven clustering hierarchy for cognitive radio sensor network. EURASIP J. Wirel. Commun. Netw. 2015(1), 1–8 (2015)CrossRefGoogle Scholar
  19. 19.
    Dongare, S.P., Mangrulkar, R.S: An improved cluster head selection approach for energy efficiency in wireless sensor networks: A review. In: 2015 International Conference on Pervasive Computing (ICPC), pp. 1–6. IEEE (2015)Google Scholar
  20. 20.
    Rajkumar, K., Mohammed, U.: A network lifetime enhancement method for sink relocation and its analysis in wireless sensor networks. Int. J. Inno. Sci. Eng. Res. 2(4), 77–82 (2015)Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Electrical EngineeringUniversity of MalayaKuala LumpurMalaysia

Personalised recommendations