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Boundary recognition in sensor networks by topological methods
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Source International Conference on Mobile Computing and Networking archive
Proceedings of the 12th annual international conference on Mobile computing and networking table of contents
Los Angeles, CA, USA
SESSION: Sensor networks I table of contents
Pages: 122 - 133  
Year of Publication: 2006
ISBN:1-59593-286-0
Authors
Yue Wang  Stony Brook University
Jie Gao  Stony Brook University
Joseph S.B. Mitchell  Stony Brook University
Sponsors
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 21,   Downloads (12 Months): 152,   Citation Count: 19
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ABSTRACT

Wireless sensor networks are tightly associated with the underlying environment in which the sensors are deployed. The global topology of the network is of great importance to both sensor network applications and the implementation of networking functionalities. In this paper we study the problem of topology discovery, in particular, identifying boundaries in a sensor network. Suppose a large number of sensor nodes are scattered in a geometric region, with nearby nodes communicating with each other directly. Our goal is to find the boundary nodes by using only connectivity information. We do not assume any knowledge of the node locations or inter-distances, nor do we enforce that the communication graph follows the unit disk graph model. We propose a simple, distributed algorithm that correctly detects nodes on the boundaries and connects them into meaningful boundary cycles. We obtain as a byproduct the medial axis of the sensor field, which has applications in creating virtual coordinates for routing. We show by extensive simulation that the algorithm gives good results even for networks with low density. We also prove rigorously the correctness of the algorithm for continuous geometric domains.


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  19

Collaborative Colleagues:
Yue Wang: colleagues
Jie Gao: colleagues
Joseph S.B. Mitchell: colleagues