LOCATION SENSING AND PRIVACY IN A

This article presents and evaluates the performance of a location sensing algorithm developed and demonstrated at Carnegie Mellon University. We compare our model with various others based on different architectures and software paradigms. We show comparative results in accuracy, the complexity of training, total power consumption, and suitability to users. Our method reduces training complexity by a factor of eight over previous algorithms, and yields noticeably better accuracy. The algorithm uses less power than previous models, and offers a more secure privacy model.

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