A Hybrid Aggregation Technique for Continuous-Monitoring in Wireless Sensor Networks

  • R. Rajkamal
  • P. Vanaja Ranjan
Part of the Communications in Computer and Information Science book series (CCIS, volume 142)


Network lifetime is the one of the most important issues in WSN and is fundamental to any design and development effort. In continuous monitoring application, the Wireless sensor Network (WSN) is generated data continuously and transmit to base station with a predefined frequency. The dominant energy consumption in the WSN occurs in radio transceiver that is responsible for transmission and reception of data. In this work, a hybrid (combined data and information) aggregation scheme is proposed to achieve energy efficiency by exploring the impact of heterogeneity of network for continuous Power Quality (PQ) monitoring application.


Continuous Monitoring Wireless Sensor Network Radio Transceiver Network lifetime Power Quality monitoring 


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  1. 1.
    Intanagonwitat, C., Estrin, D., Govindan, R., Heidemann, J.: Impact of Network Density on Data Aggregation in wireless sensor Networks. In: ICDCS 2002 (2002)Google Scholar
  2. 2.
    Fakildiz, I., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A Survey on Sensor Networks. IEEE Communications Magazine 40(8), 102–114 (2002)CrossRefGoogle Scholar
  3. 3.
    Min, R., et al.: Energy-centric enabling technologies for wireless sensor networks. In: Proceedings of IEEE Wireless Communications, pp. 28–39 (2002)Google Scholar
  4. 4.
    Sohrabi, K., et al.: Protocols for self-organization of a wireless sensor network. In: Proceedings of IEEE Personal Communications, vol. 7, pp. 16–27 (2000)Google Scholar
  5. 5.
    Estrin, D., et al.: Next century challenges: scalable coordination in sensor networks. In: Proceedings of MOBICOMM, pp. 263–270 (1999)Google Scholar
  6. 6.
    Raghunathan, V., et al.: Energy-Aware Wireless Sensor Networks. Proceedings of IEEE Signal Processing 19, 40–50 (2002)CrossRefGoogle Scholar
  7. 7.
    Rajagopalan, R., Varhney, P.k.: Data Aggregation Techniques in Sensor Networks: A Survey. IEEE Communications Survey and Tutorials 8(4), 48–62 (2006)CrossRefGoogle Scholar
  8. 8.
    Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., Silva, F.: Directed Diffusion for Wireless Sensor Networking. ACM/IEEE Transactions on Networking 11(1), 2–16 (2003)CrossRefGoogle Scholar
  9. 9.
    Heinzelman, W.R.: Application-Specific Protocol Architectures for Wireless Networks, Ph.D. thesis, Massachusetts Institute of Technology (2000)Google Scholar
  10. 10.
    Younis, O., Fahmy, S.: HEED: a Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor networks. IEEE Trans. Mobile Computing 3(4), 366–379 (2004)CrossRefGoogle Scholar
  11. 11.
    Chatterjea, S., Havinga, P.: A Dynamic Data Aggregation Scheme For Wireless Sensor Networks. In: Proc. Program for Research on Integrated Systems and Circuits, Veldhoven, The Netherlands (2003)Google Scholar
  12. 12.
    Lindsey, S., Raghavendra, C., Sivalingam, K.M.: Data Gathering Algorithms in Sensor Networks Using Energy metrics. IEEE Trans. Parallel and Distributed Systems 13(9), 924–935 (2002)CrossRefGoogle Scholar
  13. 13.
    Ding, M., Cheng, X., Xue, G.: Aggregation Tree Construction in Sensor Networks. In: IEEE 58th Vehicle. Tech. Conf., vol. 4(4), pp. 2168–2172 (2003)Google Scholar
  14. 14.
    Tan, H.O., Korpeoglu, I.: Power Efficient Data Gathering and Aggregation in Wireless Sensor Networks. SIGMOD Record 32(4), 66–71 (2003)CrossRefGoogle Scholar
  15. 15.
    Lindsey, S., Raghavendra, C.: PEGASIS: Power Efficient Gathering in Sensor Information Systems. In: IEEE Aerospace Conference, vol. 3, pp. 3-1125–3-1130 (2002)Google Scholar
  16. 16.
    Azim, M.A., Moad, S., Bouabdallah, N.: SAG: Smart Aggregation Technique for Continuous-Monitoring in Wireless Sensor Networks. In: IEEE Conference on Communications, pp. 1–6 (2010)Google Scholar
  17. 17.
    IEEE. Wireless medium access control (MAC) and physical layer (PHY) specifications for low rate wireless personal area networks (LR-WPANs). The Institute of Electrical and Electronics Engineers, New York, NY, USA (2003) Google Scholar
  18. 18.
    Rajkamal, R., Vanaja Ranjan, P.: Soft Computing Feature Extraction Algorithm with Minimum Entropy. International Journal of Distributed Energy Resources 6(3), 253–261 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • R. Rajkamal
    • 1
  • P. Vanaja Ranjan
    • 1
  1. 1.Department of Electrical and Electronics Engineering, College of Engineering GuindyAnna UniversityChennaiIndia

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