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Combs, needles, haystacks: balancing push and pull for discovery in large-scale sensor networks
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Source Conference On Embedded Networked Sensor Systems archive
Proceedings of the 2nd international conference on Embedded networked sensor systems table of contents
Baltimore, MD, USA
SESSION: Routing and MAC table of contents
Pages: 122 - 133  
Year of Publication: 2004
ISBN:1-58113-879-2
Authors
Xin Liu  University of California
Qingfeng Huang  Palo Alto Research Center (PARC) Inc.
Ying Zhang  Palo Alto Research Center (PARC) Inc.
Sponsors
SIGARCH: ACM Special Interest Group on Computer Architecture
SIGBED: ACM Special Interest Group on Embedded Systems
ACM: Association for Computing Machinery
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
SIGCOMM: ACM Special Interest Group on Data Communication
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
SIGOPS: ACM Special Interest Group on Operating Systems
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 9,   Downloads (12 Months): 53,   Citation Count: 17
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ABSTRACT

In this paper we investigate efficient strategies for supporting on-demand information dissemination and gathering in large-scale vwireless sensor networks. In particular, we propose a "comb-needle" discovery support model resembling an ancient method: use a comb to help find a needle in sands or a haystack. The model combines push and pull for information dissemination and gathering. The push component features data duplication in a linear neighborhood of each node. The pull component features a dynamic formation of an on-demand routing structure resembling a comb. The comb-needle model enables us to investigate the cost of a spectrum of push and pull combinations for supporting discovery and query in large scale sensor networks. Our result shows that the optimal routing structure depends on the frequency of query occurrence and the spatial-temporal frequency of related events in the network. The benefit of balancing push and pull for discovery in large scale geometric networks are demonstrated. We also raise the issue of query coverage in unreliable networks and investigate how redundancy can improve the coverage via both theoretical analysis and simulation. Last, we study adaptive strategies for the case where the frequencies of query and events are unknown a priori and time-varying.


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  18

Collaborative Colleagues:
Xin Liu: colleagues
Qingfeng Huang: colleagues
Ying Zhang: colleagues