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Mobile Computing and Communications Review. 01/2011; 15:20-23.
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IEEE Communications Letters. 01/2011; 15:470-472.
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Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services (MobiSys 2011), Bethesda, MD, USA, June 28 - July 01, 2011; 01/2011
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Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services (MobiSys 2011), Bethesda, MD, USA, June 28 - July 01, 2011; 01/2011
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Proceedings of the 17th Annual International Conference on Mobile Computing and Networking, MOBICOM 2011, Las Vegas, Nevada, USA, September 19-23, 2011; 01/2011
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Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services (MobiSys 2011), Bethesda, MD, USA, June 28 - July 01, 2011; 01/2011
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Proceedings of the 7th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2010, April 28-30, 2010, San Jose, CA, USA; 01/2010
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Proceedings of the 16th Annual International Conference on Mobile Computing and Networking, MOBICOM 2010, Chicago, Illinois, USA, September 20-24, 2010; 01/2010
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Proceedings of the 10th ACM Workshop on Hot Topics in Networks. HotNets 2010, Monterey, CA, USA - October 20 - 21, 2010; 01/2010
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Proceedings of the 10th ACM Workshop on Hot Topics in Networks. HotNets 2010, Monterey, CA, USA - October 20 - 21, 2010; 01/2010
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Mobile Computing and Communications Review. 01/2010; 14:4-6.
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Mobile Computing and Communications Review. 01/2010; 14:7-9.
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Mobile Computing and Communications Review. 01/2009; 13:34-37.
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Proceedings of the 15th Annual International Conference on Mobile Computing and Networking, MOBICOM 2009, Beijing, China, September 20-25, 2009; 01/2009
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WCNC 2008, IEEE Wireless Communications & Networking Conference, March 31 2008 - April 3 2008, Las Vegas, Nevada, USA, Conference Proceedings; 01/2008
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Proceedings of the 2007 ACM Conference on Emerging Network Experiment and Technology, CoNEXT 2007, New York, NY, USA, December 10-13, 2007; 01/2007
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ABSTRACT: In this paper, we consider algorithmic issues in employ-ing lossy compression in order to extend the lifetime of wireless sensor networks. We consider two metrics which trade-off with each other: the communication and compu-tation cost on one hand, and the information loss from lossy compression on the other. We consider the particular set-ting where the lossy compression scheme is simple averag-ing. This compression scheme leads to lower computation cost at a node compared to expensive loss-less compression schemes, and in addition saves on the communication cost. However, such a scheme also loses significant information, and must only be employed when node values are highly correlated. We study the problem of deciding the appropriate com-bination of loss-less and lossy compression schemes to im-plement along an aggregation tree. The goal is to opti-mize information loss given a budget on the communication and computation costs. Our algorithmic framework is fairly general and handles various types of cost functions and in-formation loss measures. We perform extensive empirical studies to validate the need for such an algorithmic frame-work when network lifetime is highly constrained.