Efficient Path Selection to Propagate Data Message for Optimizing the Energy Dissipation in WSN

  • Subrata Dutta
  • Nandini Mukherjee
  • Monideepa Roy
  • Sarmistha Neogy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 176)


Original Directed diffusion algorithm chooses the shortest path to transmit data from source node to sink node. Thus, a particular set of nodes are used more frequently leading to energy hole problem. If the message transmission load is distributed considering remaining energy and the remaining path length of a node, then the above problem can be solved. In this paper we suggest a scheme for reducing energy consumption in WSN. The scheme is an extension of the concept introduced in [1].


Wireless Sensor Network Directed Diffusion Uniform Energy Dissipation 


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  1. 1.
    Hwang, A. K., Lee, J. Y., Kim, B. C.: Design of Maximum Remaining Energy Constrained Directed Diffusion Routing for Wireless Sensor Networks. In: Proceedings of the International Conference, UK, pp. 788–795 (May 2006)Google Scholar
  2. 2.
    Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., Silva, F.: Directed Diffusion for Wireless Sensor Networking. IEEE/ACM Transaction on Networking (TON), 2–16 (February 2003)Google Scholar
  3. 3.
    Dutta, S., Mukherjee, N., Neogy, S., Roy, S.: A Comparative Study on Different Wireless Sensor Routing Algorithms. International Journal of Information Processing, 1–9 Google Scholar
  4. 4.
    Dutta, S., Mukherjee, N., Neogy, S., Roy, S.: A Comparison of the Efficiencies of Different Wireless Sensor Network Algorithms with Respect to Time. In: Proceedings of NeCoM, pp. 602–618 (July 2010)Google Scholar
  5. 5.
    Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-Efficient Communication Protocol for Wireless Microsensor Networks. In: Proceedings of Hawaii International Conference on System Science, January 4-7, pp. 1–10 (2000)Google Scholar
  6. 6.
    Khude, N., Kumar, A., Karnik, A.: Time and Energy Complexity of Distributed Computation of a Class of Functions in Wireless Sensor Networks. IEEE Transaction on Mobile Computing, 617–632 (May 2008)Google Scholar
  7. 7.
    Nghiem, T.P., Kim, J.H., Lee, S.H., Cho, T.H.: A Coverage and Energy Aware Cluster-Head Selection Algorithm in Wireless Sensor Networks. In: Huang, D.-S., Jo, K.-H., Lee, H.-H., Kang, H.-J., Bevilacqua, V. (eds.) ICIC 2009. LNCS, vol. 5754, pp. 696–705. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Chitte, S., Dasgupta, S.: Distance Estimation From Received Signal Strength Under Log-Normal Shadowing: Bias and Variance. IEEE Signal Processing Letters 16(3), 216–218 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Subrata Dutta
    • 1
  • Nandini Mukherjee
    • 2
  • Monideepa Roy
    • 2
  • Sarmistha Neogy
    • 2
  1. 1.School of Mobile Computing and CommunicationJadavpur UniversityKolkataIndia
  2. 2.Dept. of Computer Sc. and EnggJadavpur UniversityKolkataIndia

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