Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
REED: robust, efficient filtering and event detection in sensor networks
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Monitoring dynamic spatial fields using responsive geosensor networks
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Deterministic boundary recognition and topology extraction for large sensor networks
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Locating and bypassing holes in sensor networks
Mobile Networks and Applications
Underground structure monitoring with wireless sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Boundary estimation in sensor networks: theory and methods
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Coverage in wireless ad hoc sensor networks
IEEE Transactions on Computers
Multihop Ad Hoc Networking: The Reality
IEEE Communications Magazine
RETRACTED: Impacts of sensor node distributions on coverage in sensor networks
Journal of Parallel and Distributed Computing
Adaptive edge detection with distributed behaviour-based agents in WSNs
International Journal of Sensor Networks
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We present an algorithm for boundary approximation in locally linked sensor networks that communicate with a remote monitoring station. Delaunay triangulations and Voronoi diagrams are used to generate a sensor communication network and define boundary segments between sensors, respectively. The proposed algorithm reduces remote station communication by approximating boundaries via a decentralised computation executed within the network. Moreover, the algorithm identifies boundaries based on differences between neighbouring sensor readings, not absolute sensor values. An analysis of the bandwidth consumption of the algorithm is presented and compared to two naive approaches. The proposed algorithm reduces the amount of remote communication (compared to the naive approaches) and becomes increasingly useful in networks with more nodes.