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We consider the problem of localizing a wireless client in an indoor environment based on the signal strength of its transmitted packets as received on stationary sniffers or access points. Several state-of-the-art indoor localization techniques have the drawback that they rely extensively on a labor-intensive'training' phase that does not scale well. Use of unmodeled hardware with heterogeneous power levels further reduces the accuracy of these techniques. We propose a 'learning-based'doi:10.1145/2079296.2079299 dblp:conf/conext/GoswamiOD11 fatcat:r3nbueylunehhjx7mq3uagh24u