Proactive Context-Aware Sensor Networks

  • Sungjin Ahn
  • Daeyoung Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3868)


We propose a novel context detection mechanism in Wireless Sensor Networks, called PROCON. In PROCON, context decisions are made in a distributed way, by cooperation of nodes connected through a context overlay on the network. As a result, the sensor network can deliver context level information, not low level sensing data, directly to proper actuators. Moreover, PROCON achieves highly efficient energy consumption compared to the existing centralized context detection mechanism. The analysis and simulation results show that the proposed mechanism outperforms the existing centralized mechanism in average energy consumption, capability of mitigating congestion to a base station, context service lifetime, and reliability.


Sensor Network Sensor Node Wireless Sensor Network Timing Relation Context Decision 
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.
    Madden, S., et al.: TinyDB: An acquisitional query processing system for sensor networks. In: Transactions on Database Systems, TODS (2005)Google Scholar
  2. 2.
    Madden, S., et al.: TAG: A Tiny AGgregation Service for Ad-Hoc Sensor Networks. In: Proceedings of the 5th Symposium on Operating Systems Design and Implementation, OSDI (2002)Google Scholar
  3. 3.
    Nath, S., et al.: Synopsis Diffusion for Robust Aggregation in Sensor Networks. In: ACM Sensys (2004)Google Scholar
  4. 4.
    Bonnet, P., et al.: Towards Sensor Database Systems. In: MDM (2001)Google Scholar
  5. 5.
    Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed diffusion: A scalable and robust communication paradigm for sensor networks. In: ACM MOBICOM (2000)Google Scholar
  6. 6.
    Bonfils, B.J., Bonnet, P.: Adaptive and Decentralized Operator Placement for In-Network Query Processing. In: Zhao, F., Guibas, L.J. (eds.) IPSN 2003. LNCS, vol. 2634, pp. 47–62. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Krishnamachari, B., Estrin, D., Wicker, S.: Modelling data-centric routing in wireless sensor networks. In: IEEE INFOCOM (2002)Google Scholar
  8. 8.
    Ee, C.T., Bajcsy, R.: Congestion Control and Fairness for Many-to-One Routing in Sensor Networks. In: ACM SenSys (2004)Google Scholar
  9. 9.
    Hightower, J., Bordello, G.: Location systems for ubiquitous computing. In: IEEE Comp. (2001)Google Scholar
  10. 10.
    Chintalapudi, K., et al.: Ad-hoc localization using ranging and sectoring. In: IEEE INFOCOM (2004)Google Scholar
  11. 11.
    Hou, J.S., Hsiao, H.C., King, C.T., Lu, C.N.: Context Discovery in Sensor Networks. In: IEEE ITRE (2005)Google Scholar
  12. 12.
    Michahelles, F., Samulowitz, M., Schiele, B.: Detecting Context in Distributed Sensor Networks by Using Smart Context-Aware Packets. In: Schmeck, H., Ungerer, T., Wolf, L. (eds.) ARCS 2002. LNCS, vol. 2299, p. 34. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Yu, Y., et al.: Geographical and Energy Aware Routing: a recursive data dissemination protocol for wireless sensor networks. UCLA CS Tech. Report (2001)Google Scholar
  14. 14.
    Perkins, C., Belding-Royer, E., Das, S.: Ad hoc On-Demand Distance Vector Routing. IETF RFC 3561 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sungjin Ahn
    • 1
  • Daeyoung Kim
    • 1
  1. 1.Real-time and Embedded Systems LaboratoryInformation and Communications University (ICU)DaejeonKorea

Personalised recommendations