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2012 IEEE 12th International Conference on Data Mining Workshops
Sensor Network Localization for Moving Sensors
Brussels, Belgium Belgium
December 10-December 10
ISBN: 978-1-4673-5164-5
Sensor network localization (SNL) is the problem of determining the locations of the sensors given sparse and usually noisy inter-communication distances among them. In this work we propose an iterative algorithm named PLACEMENT to solve the SNL problem. This iterative algorithm requires an initial estimation of the locations and in each iteration, is guaranteed to reduce the cost function. The proposed algorithm is able to take advantage of the good initial estimation of sensor locations making it suitable for localizing moving sensors, and also suitable for the refinement of the results produced by other algorithms. Our algorithm is very scalable. We have experimented with a variety of sensor networks and have shown that the proposed algorithm outperforms existing algorithms both in terms of speed and accuracy in almost all experiments. Our algorithm can embed 120,000 sensors in less than 20 minutes.
Index Terms:
Noise measurement,Cost function,Distance measurement,Noise,Educational institutions,Wireless sensor networks,Global Positioning System,sensor network localization,Embedding
Citation:
Arvind Agarwal, Hal Daume, Jeff M. Phillips, Suresh Venkatasubramanian, "Sensor Network Localization for Moving Sensors," icdmw, pp.202-209, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012
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