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	<title>Suresh Venkatasubramanian &#187; CCF 1115677</title>
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		<title>Sensor Network Localization for Moving Sensors</title>
		<link>http://www.cs.utah.edu/~suresh/web/2012/10/15/sensor-network-localization-for-moving-sensors/</link>
		<comments>http://www.cs.utah.edu/~suresh/web/2012/10/15/sensor-network-localization-for-moving-sensors/#comments</comments>
		<pubDate>Mon, 15 Oct 2012 17:44:24 +0000</pubDate>
		<dc:creator>suresh</dc:creator>
				<category><![CDATA[Papers]]></category>
		<category><![CDATA[CCF 0953066]]></category>
		<category><![CDATA[CCF 1115677]]></category>

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		<description><![CDATA[[author]Arvind Agarwal, Hal Daume III, Jeff M. Phillips, Suresh Venkatasubramanian[/author] The Second IEEE ICDM Workshop on Data Mining in Networks Abstract: 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 [...]]]></description>
				<content:encoded><![CDATA[<p>[author]Arvind Agarwal, Hal Daume III, Jeff M. Phillips, Suresh Venkatasubramanian[/author]<br />
<em><a href="http://damnet.reading.ac.uk/">The Second IEEE ICDM Workshop on Data Mining in Networks</a></em></p>
<p><span id="more-287"></span></p>
<p>Abstract:</p>
<blockquote><p>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.<br />
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<br />
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.</p>
</blockquote>
<p>Links: <a href="http://www.cs.utah.edu/~suresh/papers/damnet/damnet.pdf">PDF</a></p>
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