Modeling and designing efficient data aggregation in wireless sensor networks under entropy and energy bounds

  • Laura Galluccio
  • Sergio Palazzo
  • Andrew T. Campbell
Article

Abstract

Sensor networks are characterized by limited energy, processing power, and bandwidth capabilities. These limitations become particularly critical in the case of event-based sensor networks where multiple collocated nodes are likely to notify the sink about the same event, at almost the same time. The propagation of redundant highly correlated data is costly in terms of system performance, and results in energy depletion, network overloading, and congestion. Data aggregation is considered to be an effective technique to reduce energy consumption and prevent congestion in wireless sensor networks. In this paper, we derive a number of important insights concerning the data aggregation process, which have not been discussed in the literature so far. We first estimate the conditions under which aggregation is a costly process in comparison to a non aggregation approach, by considering a realistic scenario where the processing costs related to aggregation of data are not neglected. We also consider that aggregation should preserve the integrity of data, and therefore, the entropy of the correlated data sent by sources can be considered in order to both decrease the amount of redundant data forwarded to the sink and perform an overall lossless process. We also derive the cumulative and the probability distribution functions of the delay in an aggregator node queue, which can be used to relate the delay to the amount of aggregation being considered. The framework we present in this paper serves to investigate the tradeoff between the increase in data aggregation required to reduce energy consumption, and the need to maximize information integrity, while also understanding how aggregation impacts the network propagation delay of a data packet.

Keywords

Wireless sensor networks Data aggregation Entropy Energy consumption Delay 

References

  1. 1.
    C. Shen, C. Srisathapornphat, C. Jaikaeo, Sensor information networking architecture and applications, IEEE Personal Communications, Vol. 8, No. 4, pp. 52–59, 2001.Google Scholar
  2. 2.
    J. M. Kahn, R. H. Katz, K. S. J. Pister, Next Century Challenges: Mobile Networking for "Smart Dust", ACM Mobicom, Seattle, WA, 1999.Google Scholar
  3. 3.
    G. Ahn, S. G. Hong, E. Miluzzo, A. T. Campbell, F. Cuomo, Funneling-MAC: a Localized, Sink-Oriented MAC for Boosting Fidelity in Sensor Networks, ACM Sensys, Boulder, CO, 2006.Google Scholar
  4. 4.
    C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidenmann, F. Siva, Directed diffusion for wireless sensor networking, ACM/IEEE Transactions on Networking, Vol. 11, No. 1, pp. 2–16, 2002.Google Scholar
  5. 5.
    A. Boulis, S. Ganeriwal, M. Srivastava, Aggregation in Sensor Networks: An Energy-Accuracy Tadeoff, IEEE SNPA, Ankorage, AL, 2003.Google Scholar
  6. 6.
    L. Galluccio, A. T. Campbell, S. Palazzo, Concert: Aggregation-Based Congestion Control for Sensor Networks, ACM Sensys, San Diego, CA, 2005.Google Scholar
  7. 7.
    C. Intanagonwiwat, D. Estrin, R. Govindan, J. Heidenmann, Impact of Network Density on Data Aggregation in Wireless Sensor Networks, IEEE ICDCS, Wien, Austria, 2002.Google Scholar
  8. 8.
    A. Scaglione, S. Servetto, On the Interdependence Between Routing and Data Compression, ACM Mobicom, Atlanta, GA, 2002.Google Scholar
  9. 9.
    R. Zheng, R. Barton, Toward Optimal Data Aggregation in Random Wireless Sensor Networks, IEEE INFOCOM, Anchorage, AL, 2007.Google Scholar
  10. 10.
    B. Krishnamachari, D. Estrin, S. Wicker, The Impact of Data Aggregation in Wireless Sensor Networks, DEBS, Wien, Austria, 2002.Google Scholar
  11. 11.
    D. Vass, A. Vidacs, Distributed Data Aggregation with Geographical Routing in Wireless Sensor Networks, IEEE ICPS, Istanbul, Turkey, 2007.Google Scholar
  12. 12.
    X. Su, A combinatorial algorithmic approach to energy efficient information collection in wireless sensor networks, ACM Transactions on Sensor Networks, Vol. 3, No. 1, pp. 40–46, 2007.CrossRefGoogle Scholar
  13. 13.
    H. Gupta, V. Navda, S. R. Das, V. Chowdhary, Efficient Gathering of Correlated Data in Sensor Networks, ACM Mobihoc, Urbana-Champaign, IL, 2005.Google Scholar
  14. 14.
    R. Cristescu, B. Beferull-Lozano, M. Vetterli, R. Wattenhofer, Network correlated data gathering with explicit communication: NP completeness and algorithms, ACM/IEEE Transactions on Networking, Vol. 14, No. 1, pp. 41–54, 2006.CrossRefGoogle Scholar
  15. 15.
    J. Gao, L. Guibas, J. Herschberger, Sparse Data Aggregation in Sensor Networks, IEEE IPSN, Cambridge, MA, 2007.Google Scholar
  16. 16.
    L. Kleinrock, Queueing Systems, Volume II: Computer Applications, Wiley, NY, 1976.Google Scholar
  17. 17.
    W. R. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy-efficient communication protocol forwireless microsensor networks, Proceedings of IEEE HICSS, Hawaii, HW, 2000.Google Scholar
  18. 18.
    W. Feng, H. Alshaer, J. M. H. Elmirghani, Energy Efficiency: Optimal Transmission Range with Topology Management in Rectangular ad-hoc Wireless Networks, Proceedings of IEEE AINA, Bradford, UK, 2009.Google Scholar
  19. 19.
    T. M. Cover, J. A. Thomas, Elements of Information Theory, 2nd ed. Wiley, NY, 1991.MATHGoogle Scholar
  20. 20.
    E. Ertin, Gaussian Process Models for Censored Sensor Readings, IEEE/SP SSP, Madison, WI, 2007.Google Scholar
  21. 21.
    M. C. Vuran, I. Akyildiz, Spatial correlation-based collaborative medium access control in wireless sensor networks, ACM/IEEE Transactions on Networking, Vol. 14, No. 2, pp. 316–329, 2006.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Laura Galluccio
    • 1
  • Sergio Palazzo
    • 1
  • Andrew T. Campbell
    • 2
  1. 1.Dipartimento di Ingegneria Informatica e delle TelecomunicazioniUniversity of CataniaCataniaItaly
  2. 2.Computer Science DepartmentDartmouth CollegeHanoverUSA

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