Cluster Computing

, Volume 22, Supplement 2, pp 4213–4220 | Cite as

An intelligent data aware and energy censoring scheme for wireless sensor networks

  • S. DiwakaranEmail author
  • B. Perumal
  • K. Vimala Devi


Wireless data gathering has accelerated its pace with the advancements in MEMS and Nano technology. Contemporary wireless sensor networks suffer from limited bandwidth, energy constraint and data losses. In this paper, an auction based data gathering approach is proposed for WSN to reduce energy consumption, ultimately to increase the lifetime of the network. In this auction based model, the data are transmitted by a node, only if it differs from the data previously transmitted by other nodes. The order of data transmission becomes directly proportional to the residual energy of the node. Here in this work, an energy aware selective data transmission is introduced to conserve the network energy. Many previous works have considered the data filtering as a better data gathering option. The proposed data gathering considers the energy level of the sensor node to make the transmission decision. This work reduces energy up to 81% and upholds the throughput by preserving life time of the critical paths.


Wireless sensor networks Energy conservation Data censoring 


  1. 1.
    Mao, Sh, et al.: Joint energy allocation for sensing and transmission in rechargeable wireless sensor networks. IEEE Trans. Veh. Technol. 63(6), 2862–2875 (2014)Google Scholar
  2. 2.
    Hong, Y.W., Scaglione, A.: Energy-efficient broadcasting with cooperative transmissions in wireless sensor networks. IEEE Trans. Wirel. Commun. 5(10), 2844–2855 (2006)Google Scholar
  3. 3.
    Elhoseny, M., et al.: Balancing energy consumption in heterogeneous wireless sensor networks using genetic algorithm. IEEE Commun. Lett. 19(12), 2194–2197 (2015)Google Scholar
  4. 4.
    Cheng, V.W., Wang, T.Y.: Performance analysis of distributed decision fusion using a censoring scheme in wireless sensor networks. IEEE Trans. Veh. Technol. 59(6), 2845–2851 (2010)Google Scholar
  5. 5.
    Rago, C., Willett, P.K., Bar-Shalom, Y.: Censoring sensors: A low-communication-rate scheme for distributed detection. IEEE Trans. Aerosp. Electron. Syst. 32(2), 554–568 (1996)Google Scholar
  6. 6.
    Jiang, R., Chen, B.: Fusion of censored decisions in wireless sensor networks. IEEE Trans. Wireless Commun. 4(6), 2668–2673 (2005)Google Scholar
  7. 7.
    Pai, H.-T.: Equal-gain combination for adaptive distributed classification in wireless sensor networks. Int. J. Ad Hoc Ubiquitous Comput. 4(2), 115–121 (2000)Google Scholar
  8. 8.
    Cetin, M., Chen, L., Fisher III, J.W., Ihler, A.T., Moses, R.L., Wainwright, M.J., Willsky, A.S.: Distributed fusion in sensor networks: a graphical models perspective. IEEE Signal Process. Mag. 23(4), 42–55 (2006)Google Scholar
  9. 9.
    Yiu, S., Schober, R.: Nonorthogonal transmission and noncoherent fusion of censored decisions. IEEE Trans. Veh. Technol. 58(1), 263–273 (2009)Google Scholar
  10. 10.
    Cheng, Victor W., Wang, Tsang-Yi: Performance analysis of distributed decision fusion using a censoring scheme in wireless sensor networks. IEEE Trans. Veh. Technol. 59(6), 2845–2851 (2010)Google Scholar
  11. 11.
    Krause, A., Singh, A., Guestrin, C.: Near-optimal sensor placements in gaussian processes: theory, efficient algorithms and empirical studies. J. Machine Learn. Res. 9, 235–284 (2008)Google Scholar
  12. 12.
    Pukelsheim, F.: Optimal Design of Experiments. Society for Industrial Mathematics (2006)Google Scholar
  13. 13.
    Msechu, E.J., Giannakis, G.B.: Distributed measurement censoring for estimation with wireless sensor networks. 2011 IEEE 12th International Workshop on Signal Processing Advances in Wireless Communications. IEEE (2011)Google Scholar
  14. 14.
    Appadwedula, S., Veeravalli, V.V., Jones, D.L.: Decentralized detection with censoring sensors. IEEE Trans. Signal Process. 56, 1362–1373 (2008)Google Scholar
  15. 15.
    Rago, C., Willett, P., Bar-Shalom, Y.: Censoring sensors: a low-communication-rate scheme for distributed detection. IEEE Trans. Aerospace and Electron. Syst. 32, 554–568 (1996)Google Scholar
  16. 16.
    Arunraja, Muruganantham, Malathi, Veluchamy, Sakthivel, Erulappan: Distributed similarity based clustering and compressed forwarding for wireless sensor networks. ISA Trans. 59, 180–192 (2015)Google Scholar
  17. 17.
    Arunraja, Muruganantham, Malathi, Veluchamy: Collective prediction exploiting spatio temporal correlation (CoPeST) for energy efficient wireless sensor networks. KSII Trans. Internet Inf. Syst. (TIIS) 9(7), 2488–2511 (2015)Google Scholar
  18. 18.
    Wang, H., et al.: Network lifetime maximization with cross-layer design in wireless sensor networks. IEEE Trans. Wirel. Commun. 7(10), 3759–3768 (2008)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of ECEKalasalingam UniversitySrivilliputhurIndia
  2. 2.Department of CSEVelammal College of EngineeringChennaiIndia

Personalised recommendations