Distributed Energy Efficient Data Gathering without Aggregation via Spanning Tree Optimization

  • Lenka Carr-Motyčková
  • David Dryml
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7960)


A distributed algorithm that solves energy efficient data gathering Weighted Spanning Tree Distributed Optimization (WSTDO) is proposed in this paper. It is based on an optimization performed locally on the data gathering spanning tree. WSTDO algorithm is compared to two centralized spanning tree optimization algorithms MITT and MLTTA. The performance of WSTDO achieves between one half and one third of the MITT performance and proves to be better than MLTTA. The performance depends on the density of the network. It works better for sparse networks. WSTDO has lower overhead than MITT and MLTTA for sparse networks. Though the proposed algorithm has a worse performance than MITT it has other features that over-weights this fact. It is able to perform optimization parallely in disjoint sub-trees and also during data gathering which allows a short data sampling period. It is also prone to link and node failures that can be solved locally.


Data gathering Wireless sensor networks Maximum lifetime Convergecast tree Spanning tree optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Burri, N., von Rickenbach, P., Wattenhofer, R.: Dozer: ultra-low power data gathering in sensor networks. In: Proceedings of the 6th International Conference on Information Processing in Sensor Networks, IPSN 2007, pp. 450–459. ACM, New York (2007)Google Scholar
  2. 2.
    Chang, E.J.H.: Echo algorithms: Depth parallel operations on general graphs. IEEE Trans. Software Eng. 8(4), 391–401 (1982)CrossRefGoogle Scholar
  3. 3.
    Hariharan, S., Shroff, N.B.: On optimal energy efficient convergecasting in unreliable sensor networks with applications to target tracking. In: Proceedings of the MobiHoc 2011, pp. 24:1–24:10. ACM, New York (2011)Google Scholar
  4. 4.
    Ingelrest, F., Simplot-Ryl, D.: Localized broadcast incremental power protocol for wireless ad hoc networks. Wirel. Netw. 14(3), 309–319 (2008)CrossRefGoogle Scholar
  5. 5.
    Jacquet, P., Muhlethaler, P., Clausen, T., Laouiti, A., Qayyum, A., Viennot, L.: Optimized link state routing protocol for ad hoc networks. In: Proceedings of the IEEE International Conference IEEE INMIC 2001, pp. 62–68 (2001)Google Scholar
  6. 6.
    Levin, L., Segal, M., Shpungin, H.: Energy efficient data gathering in multi-hop hierarchical wireless ad hoc networks. In: Proceedings of the 7th International Workshop on Foundations of Mobile Computing, FOMC 2011, pp. 62–69. ACM, New York (2011)Google Scholar
  7. 7.
    Liang, J., Wang, J., Cao, J., Chen, J., Lu, M.: An efficient algorithm for constructing maximum lifetime tree for data gathering without aggregation in wireless sensor networks. In: Proceedings of the 29th Conference on Information Communications, INFOCOM 2010, pp. 506–510. IEEE Press, Piscataway (2010)Google Scholar
  8. 8.
    Luo, D., Zhu, X., Wu, X., Chen, G.: Maximizing lifetime for the shortest path aggregation tree in wireless sensor networks. In: INFOCOM, pp. 1566–1574. IEEE (2011)Google Scholar
  9. 9.
    Onodera, K., Miyazaki, T.: An autonomous multicast-tree creation algorithm for wireless sensor networks. In: Proceedings of the Future Generation Communication and Networking, FGCN 2007, vol. 01, pp. 268–273. IEEE Computer Society, Washington, DC (2007)Google Scholar
  10. 10.
    Osterlind, F., Dunkels, A., Eriksson, J., Finne, N., Voigt, T.: Cross-level sensor network simulation with cooja. In: Proceedings of the 2006 31st IEEE Conference on Local Computer Networks, pp. 641–648 (November 2006)Google Scholar
  11. 11.
    Wieselthier, J., Nguyen, G., Ephremides, A.: On the construction of energy-efficient broadcast and multicast trees in wireless networks. In: INFOCOM 2000, vol. 2, pp. 585–594 (2000)Google Scholar
  12. 12.
    Yuan, J., Zhou, H., Chen, H.: Constructing maximum-lifetime data gathering tree without data aggregation for sensor networks. In: Lee, R. (ed.) Computer and Information Science 2011. SCI, vol. 364, pp. 47–57. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    Zeng, W., Arora, A., Shroff, N.: Maximizing energy efficiency for convergecast via joint duty cycle and route optimization. In: Proceedings of the 29th Conference on Information Communications, INFOCOM 2010, pp. 16–20. IEEE Press, Piscataway (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Lenka Carr-Motyčková
    • 1
  • David Dryml
    • 1
  1. 1.Department of Computer Science, Faculty of SciencePalacky UniversityOlomoucCzech Republic

Personalised recommendations