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In-network surface simplification for sensor fields
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Source Geographic Information Systems archive
Proceedings of the 13th annual ACM international workshop on Geographic information systems table of contents
Bremen, Germany
SESSION: Sensor networks table of contents
Pages: 41 - 50  
Year of Publication: 2005
ISBN:1-59593-146-5
Authors
Brian Harrington  University of North Texas
Yan Huang  University of North Texas
Sponsors
ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 5,   Downloads (12 Months): 31,   Citation Count: 2
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ABSTRACT

Recent research literature on sensor network databases has focused on finding ways to perform in-network aggregation of sensor readings to reduce the message cost. However, with these techniques information about the state at a particular location is lost. In many applications such as visualization, finite element analysis, and cartography, constructing a field from all sensor readings is very important. However, requiring all sensors to report their readings to a centralized station adversely impacts the life span of the sensor network. In this paper we focus on modeling sensor networks as a field deployed in a physical space and exploiting in-network surface simplification techniques to reduce the message cost. In particular, we propose two schemes for performing in-network surface simplification, namely (1) a hierarchical approach and (2) a triangulation based approach. We focus on a quad tree based method and a decimation method for the two approaches respectively. The quad tree based method employs an incremental refinement process during reconstruction using increasingly finer levels of detail sent by selected sensors. It has a guaranteed error bound. The decimation method starts with a triangulation of all sensors and probabilistically selects sensors not to report to prevent error accumulation. To demonstrate the performance, the two simplification techniques are compared with the naive approach of having all sensors report. Experimental results show that both techniques provide substantial message savings compared to the naivealgorithm, usually requiring less than 80% as many messages and less than 50% for some data sets. Furthermore, though the decimation algorithm does not provide a guaranteed error bound, for our experiments less than 4.5% of the interpolated values exceeded the given bound.


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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P. Bonnet, J. E. Gehrke, and P. Seshadri. Querying the physical world. IEEE Personal Communications, 2000.
 
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4
 
5
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S. Goel, A. Passarella, and T. Imielinski. Using buddies to live longer in a boring world, 2004. Rutgers Department of Computer Science Technical Report DCS-TR-558.
 
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P. S. Heckbert and M. Garland. Survey of polygonal surface simplification algorithms. Technical report, 1997.
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14
 
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N. Trigoni, Y. Yao, A. Demers, J. Gehrke, and R. Rajaraman. WaveScheduling: Energy-Efficient Data Dissemination for Sensor Networks. Internet Draft, 2004.
 
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Y. Yao and J. Gehrke. The cougar approach to in-network query processing in sensor networks. In SIGMOD, 2002.
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Y. Yao and J. E. Gehrke. Query Processing in Sensor Networks. In Proc. of the First Biennial Conference on Innovative Data Systems Research (CIDR), 2003.


Collaborative Colleagues:
Brian Harrington: colleagues
Yan Huang: colleagues