Distance-Sensitive Information Brokerage in Sensor Networks

  • Stefan Funke
  • Leonidas J. Guibas
  • An Nguyen
  • Yusu Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4026)


In a sensor network information from multiple nodes must usually be aggregated in order to accomplish a certain task. A natural way to view this information gathering is in terms of interactions between nodes that are producers of information, e.g., those that have collected data, detected events, etc., and nodes that are consumers of information, i.e., nodes that seek data or events of certain types. Our overall goal in this paper is to construct efficient schemes allowing consumer and producer nodes to discover each other so that the desired information can be delivered quickly to those who seek it. Here, efficiency means both limiting the redundancy of where producer information is stored, as well as bounding the consumer query times. We introduce the notion of distance-sensitive information brokerage and provide schemes for efficiently bringing together information producers and consumers at a cost proportional to the separation between them — even though neither the consumers nor the producers know about each other beforehand.

Our brokerage scheme is generic and can be implemented on top of several hierarchical routing schemes that have been proposed in the past, provided that they are augmented with certain key sideway links. For such augmented hierarchical routing schemes we provide a rigorous theoretical performance analysis, which further allows us to prove worst case query times and storage requirements for our information brokerage scheme. Experimental results demonstrate that the practical performance of the proposed approaches far exceeds their theoretical (worst-case) bounds. The presented algorithms rely purely on the topology of the communication graph of the sensor network and do not require any geographic location information.


Sensor Network Sensor Node Hash Function Data Item Information Server 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stefan Funke
    • 1
  • Leonidas J. Guibas
    • 1
  • An Nguyen
    • 1
  • Yusu Wang
    • 2
  1. 1.Department of Computer ScienceStanford UniversityStanfordUSA
  2. 2.Department of Computer Science and EngineeringThe Ohio State UniversityColumbusUSA

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