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Connected sensor cover: self-organization of sensor networks for efficient query execution
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Source International Symposium on Mobile Ad Hoc Networking & Computing archive
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing table of contents
Annapolis, Maryland, USA
SESSION: Sensor networks table of contents
Pages: 189 - 200  
Year of Publication: 2003
ISBN:1-58113-684-6
Authors
Himanshu Gupta  State University of New York, Stony Brook, NY
Samir R. Das  State University of New York, Stony Brook, NY
Quinyi Gu  State University of New York, Stony Brook, NY
Sponsors
ACM: Association for Computing Machinery
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 49,   Citation Count: 42
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ABSTRACT

Spatial query execution is an essential functionality of a sensor network, where a query gathers sensor data within a specific geographic region. Redundancy within a sensor network can be exploited to reduce the communication cost incurred in execution of such queries. Any reduction in communication cost would result in an efficient use of the battery energy, which is very limited in sensors. One approach to reduce the communication cost of a query is to self-organize the network, in response to a query, into a topology that involves only a small subset of the sensors sufficient to process the query. The query is then executed using only the sensors in the constructed topology.In this article, we design and analyze algorithms for such self-organization of a sensor network to reduce energy consumption. In particular, we develop the notion of a connected sensor cover and design a centralized approximation algorithm that constructs a topology involving a near-optimal connected sensor cover. We prove that the size of the constructed topology is within an log n factor of the optimal size, where n is the network size. We also develop a distributed self-organization version of our algorithm, and propose several optimizations to reduce the communication overhead of the algorithm. Finally, we evaluate the distributed algorithm using simulations and show that our approach results in significant communication cost reduction.


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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CITED BY  42

Collaborative Colleagues:
Himanshu Gupta: colleagues
Samir R. Das: colleagues
Quinyi Gu: colleagues