Advertisement

Wireless Personal Communications

, Volume 90, Issue 2, pp 423–434 | Cite as

An Effective Clustering Approach with Data Aggregation Using Multiple Mobile Sinks for Heterogeneous WSN

  • A. Muthu KrishnanEmail author
  • P. Ganesh Kumar
Article

Abstract

Wireless Sensor Networks (WSNs) mostly uses static sink to collect data from the sensor nodes randomly deployed in the sensor region. In the static sink based approach, the data packets are flooded across the network to reach the mobile base station in multi-hop communication. Due to this, the static sink is inefficient in energy utilization. Recently, mobile sink are used for data gathering, has less energy utilization which in turn increases the network lifetime. Thus, the sink mobility has difficulties in finding the routing path for the data packets. This paper proposes an effective clustering approach with data aggregation using multiple mobile sinks for heterogeneous WSN. The proposed algorithm achieves network lifetime increases with limited energy utilization.

Keywords

Wireless Sensor Network (WSN) Data aggregation Clustering 

References

  1. 1.
    Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38, 393–422.CrossRefGoogle Scholar
  2. 2.
    Akkaya, K., & Younis, M. (2005). A survey on routing protocols in wireless sensor networks. Ad Hoc Networks, 3, 325–349.CrossRefGoogle Scholar
  3. 3.
    Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless sensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences, Washington, DC, USA (pp 1–10).Google Scholar
  4. 4.
    Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1, 660–670.CrossRefGoogle Scholar
  5. 5.
    Hammoudeh, M., & Newman, R. (2015). Adaptive routing in wireless sensor networks: QoS optimisation for enhanced application performance. Information Fusion, 22, 3–15.CrossRefGoogle Scholar
  6. 6.
    Wang, Z. M., Melachrinoudis, E., & Basagni, S. (2005). Voronoi diagram-based linear programming modeling of wireless sensor networks with a mobile sink. In Proceedings of the IIE annual conference and exposition.Google Scholar
  7. 7.
    Azad, P., & Sharma, V. (2015). Pareto-optimal clustering scheme using data aggregation for wireless sensor networks. International Journal of Electronics, 102(7), 1165–1176.CrossRefGoogle Scholar
  8. 8.
    Sert, S. A., Bagci, H., & Yazici, A. (2015). MOFCA: Multi-objective fuzzy clustering algorithm for wireless sensor networks. Applied Soft Computing, 30, 151–165.CrossRefGoogle Scholar
  9. 9.
    Mainwaring, A., Polastre, J., Szewczyk, R., Culler, D., & Anderson, J. (2002). Wireless sensor networks for habitat monitoring. In Proceedings of ACM Int’l workshop wireless sensor networks and applications (WSNA).Google Scholar
  10. 10.
    Kaiser, W. J., Pottie, G. J., Srivastava, M., Sukhatme, G. S., Villasenor, J., & Estrin, D. (2003). Networked infomechanical systems (NIMS) for ambient intelligence, Technical report 31, Center for Embedded Networked Sensing, University of California, Los Angeles.Google Scholar
  11. 11.
    Khan, M. I., Gansterer, W. N., & Haring, G. (2012). Static vs. mobile sink: The influence of basic parameters on energy efficiency in wireless sensor networks. Computer Communications, 36, 965–978.CrossRefGoogle Scholar
  12. 12.
    Yu, S., Zhang, B., Li, C., & Mouftah, H. T. (2014). Routing protocols for wireless sensor networks with mobile sinks: A survey. IEEE Communications Magazine, 52(7), 150–157.Google Scholar
  13. 13.
    Kansal, A., Somasundara, A., Jea, D. D., Srivastava, M. B. & Estrin, D. (2004). Intelligent fluid infrastructure for embedded networks. In Proceedings of of the 2nd ACM/USENIX MobiSys.Google Scholar
  14. 14.
    Jea, D., Somasundara, A., & Srivastava M. B. (2005). Multiple controlled mobile elements (data mules) for data collection in sensor networks. In Proceedings of the 1st IEEE/ACM DCOSS.Google Scholar
  15. 15.
    Intanagonwiwat, C., Govindan, R., & Estrin, D. (2000). Directed diffusion: A scalable and robust communication paradigm for sensor networks. In MOBICOM (pp. 56–67).Google Scholar
  16. 16.
    Wichmann, A., & Korkmaz, T. (2015). Smooth path construction and adjustment for multiple mobile sinks in wireless sensor networks. Computer Communications. doi: 10.1016/j.comcom.2015.06.001.
  17. 17.
    Shi, L., Zhang, B. X., Mouftah, H. T., & Ma, J. (2013). DDRP: An efficient data-driven routing protocol for wireless sensor networks with mobile sinks. International Journal of Communication Systems, 26, 1341–1355.Google Scholar
  18. 18.
    Liu, W., Lu, K. J., Wang, J. P., Xing, G. L., & Huang, L. S. (2012). Performance analysis of wireless sensor networks with mobile sinks. IEEE Transactions on Vehicular Technology, 61, 2777–2788.CrossRefGoogle Scholar
  19. 19.
    Lin, C. J. (2006). HCDD: Hierarchical cluster-based data dissemination in wireless sensor networks with mobile sink. In Proceedings of international conference on wireless communications and mobile computing, Vancouver, Canada (pp. 1189–1194).Google Scholar
  20. 20.
    Shah, R. C., Roy, S., Jain, S., & Brunette, W. (2003). Data MULEs: Modeling a three-tier architecture for sparse sensor networks. In Proceedings of IEEE workshop sensor network protocols and applications (SNPA).Google Scholar
  21. 21.
    Konstantopoulos, C., Pantziou, G., Gavalas, D., Mpitziopoulos, A., & Mamalis, B. (2012). A rendezvous-based approach enabling energy-efficient sensory data collection with mobile sinks. Parallel and Distributed Systems, IEEE Transactions on, 23(5), 809–817.CrossRefGoogle Scholar
  22. 22.
    Small, T., & Haas, Z. (2003). The shared wireless infostation model—A new ad hoc networking paradigm (or where there is a whale, there is a way). In Proceedings of ACM MobiHoc.Google Scholar
  23. 23.
    Heidemann, J., Silva, F., Intanagonwiwat, C., Govindan, R., Estrin, D., & Ganesan, D. (2001). Building efficient wireless sensor networks with low-level naming. In SOSP. Oct 2001.Google Scholar
  24. 24.
    Intanagonwiwat, C., Estrin, D., Govindan, R., & Heidemann, J. (2001). Impact of network density on data aggregation in wireless sensor networks. Submitted for Publication, ICDCS-22.Google Scholar
  25. 25.
    Intanagonwiwat, C. Govindan, R., & Estrin, D. (2000). Directed diffusion: A scalable and robust communication paradigm for sensor networks. In MobiCOM, Boston, MA.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of ECEAnna University Regional Centre MaduraiMaduraiIndia
  2. 2.K.L.N College of EngineeringPottapalayam, SivagangaiIndia

Personalised recommendations