An Optimized In-Network Aggregation Scheme for Data Collection in Periodic Sensor Networks

  • Jacques M. Bahi
  • Abdallah Makhoul
  • Maguy Medlej
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7363)


In-network data aggregation is considered an effective technique for conserving energy communication in wireless sensor networks. It consists in eliminating the inherent redundancy in raw data collected from the sensor nodes. Prior works on data aggregation protocols have focused on the measurement data redundancy. In this paper, our goal in addition of reducing measures redundancy is to identify near duplicate nodes that generate similar data sets. We consider a tree based bi-level periodic data aggregation approach implemented on the source node and on the aggregator levels. We investigate the problem of finding all pairs of nodes generating similar data sets such that similarity between each pair of sets is above a threshold t. We propose a new frequency filtering approach and several optimizations using sets similarity functions to solve this problem. To evaluate the performance of the proposed filtering method, experiments on real sensor data have been conducted. The obtained results show that our approach offers significant data reduction by eliminating in network redundancy and outperforms existing filtering techniques.


Sensor Network Sensor Node Wireless Sensor Network Network Lifetime Data Aggregation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yu, B., Li, J., Li, Y.: Distributed data aggregation scheduling in wireless sensor networks. In: IEEE INFOCOM 2009 (2009)Google Scholar
  2. 2.
    Zheng, Y., Chen, K., Qiu, W.: Building representative-based data aggregation tree in wireless sensor networks. Mathematical Problems in Engineering, 11 pages (2010)Google Scholar
  3. 3.
    Bahi, J., Makhoul, A., Medlej, M.: Data aggregation for periodic sensor networks using sets similarity functions. In: IWCMC 2011, 7th IEEE Int. Wireless Communications and Mobile Computing Conference, pp. 559–564 (July 2011)Google Scholar
  4. 4.
    Sharaf, M.A., Beaver, J., Labrinidis, A., Chrysanthis, P.K.: Tina: A scheme for temporal coherency-aware in-network aggregation. In: 3rd ACM International Workshop on Data Engineering for Wireless and Mobile Access, pp. 69–76 (2003)Google Scholar
  5. 5.
    Xu, Y., Lee, W.-C., Xu, J., Mitchell, G.: Processing window queries in wireless sensor networks. In: 22nd Int. Conf. on Data Engineering, ICDE, p. 70 (2006)Google Scholar
  6. 6.
    Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: Tag: A tiny aggregation service for ad-hoc sensor networks. SIGOPS Oper. Syst. Rev. 36(SI), 131–146 (2002)Google Scholar
  7. 7.
    Cormode, G., Garofalakis, M., Muthukrishnan, S., Rastogi, R.: Holistic aggregates in a networked world: Distributed tracking of approximate quantiles. In: 2005 ACM SIGMOD International Conference on Management of Data, pp. 25–36 (2005)Google Scholar
  8. 8.
    Cormode, G., Garofalakis, M., Muthukrishnan, S., Rastogi, R.: Prolonging the lifetime of wireless sensor networks via unequal clustering. In: Proceedings of the 5th International Workshop on Algorithms for Wireless, Mobile, Ad Hoc and Sensor Networks (2005)Google Scholar
  9. 9.
    Lee, S., Chung, T.: Data Aggregation for Wireless Sensor Networks Using Self-organizing Map. In: Kim, T.G. (ed.) AIS 2004. LNCS (LNAI), vol. 3397, pp. 508–517. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Chen, H., Mineno, H., Mizuno, T.: Adaptive data aggregation scheme in clustered wireless sensor networks. Computer Communications 31(15), 3579–3585 (2009)CrossRefGoogle Scholar
  11. 11.
    Shah, R.C., Rabaey, J.M.: Energy aware routing for low energy ad hoc sensor networks. In: IEEE Wireless Communications and Networking Conf. WCNC, pp. 350–355 (2002)Google Scholar
  12. 12.
    Younis, O., Fahmy, S.: An experimental study of routing and data aggregation in sensor networks. In: IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 8 pages (2005)Google Scholar
  13. 13.
    Prakash, G.L., Thejaswini, M., Manjula, S.H., Venugopal, K.R., Patnaik, L.M.: Tree-on-dag for data aggregation in sensor networks. World Academy of Science, Engineering and Technology 37 (2009)Google Scholar
  14. 14.
    Fan, K.-W., Liu, S., Sinha, P.: Dynamic forwarding over tree-on-dag for scalable data aggregation in sensor networks. IEEE Trans. on Mobile Computing 7(10), 1271–1284 (2008)CrossRefGoogle Scholar
  15. 15.
    Bayardo, R.J., Ma, Y., Srikant, R.: Scaling up all pairs similarity search. In: 16th International Conference on World Wide Web, WWW 2007, pp. 131–140 (2007)Google Scholar
  16. 16.
    Sarawag, S., Kirpal, A.: Efficient exact set-similarity joins. In: 32nd international Conference on Very large Data Bases, VLDB 2006, pp. 918–929 (2006)Google Scholar
  17. 17.
    Chaudhuri, S., Ganti, V., Kaushik, R.: A primitive operator for similarity joins in data cleaning. In: 22nd International Conference on Data Engineering (ICDE 2006), p. 5 (2006)Google Scholar
  18. 18.
    Xiao, C., Wang, W., Lin, X., Yu, J.X.: Efficient similarity joins for near duplicate detection. In: Proceeding of the 17th International Conference on World Wide Web, pp. 131–140. ACM (2008)Google Scholar
  19. 19.
    Xiao, C., Wang, W., Lin, X., Shang, H.: Top-k set similarity joins. In: Proceedings of the 2009 IEEE International Conference on Data Engineering, pp. 916–927 (2009)Google Scholar
  20. 20.
  21. 21.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jacques M. Bahi
    • 1
  • Abdallah Makhoul
    • 1
  • Maguy Medlej
    • 1
  1. 1.FEMTO-ST Laboratory, DISC DepartementUniversity of Franche-ComtéBelfortFrance

Personalised recommendations