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Information-directed routing in ad hoc sensor networks
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Source International Workshop on Wireless Sensor Networks and Applications archive
Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications table of contents
San Diego, CA, USA
SESSION: Queries and aggregation table of contents
Pages: 88 - 97  
Year of Publication: 2003
ISBN:1-58113-764-8
Authors
Juan Liu  Palo Alto Research Center, Palo Alto, CA
Feng Zhao  Palo Alto Research Center, Palo Alto, CA
Dragan Petrovic  U.C. Berkeley, Berkeley, CA
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

In a sensor network, data routing is tightly coupled to the needs of a sensing task, and hence the application semantics. This paper introduces the novel idea of information-directed routing, in which routing is formulated as a joint optimization of data transport and information aggregation. The routing objective is to minimize communication cost while maximizing information gain, differing from routing considerations for more general ad hoc networks. The paper uses the concrete problem of locating and tracking possibly moving signal sources as an example of information generation processes, and considers two common information extraction patterns in a sensor network: routing a user query from an arbitrary entry node to the vicinity of signal sources and back, or to a prespecified exit node, maximizing information accumulated along the path. We derive information constraints from realistic signal models, and present several routing algorithms that find near-optimal solutions for the joint optimization problem. Simulation results have demonstrated that information-directed routing is a significant improvement over a previously reported greedy algorithm, as measured by sensing quality such as localization and tracking accuracy and communication quality such as success rate in routing around sensor holes.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Juan Liu: colleagues
Feng Zhao: colleagues
Dragan Petrovic: colleagues