IEEE Wireless Communications · 2002 · 333 citations · 9 references
ComputingComputer ScienceComputer SystemsMobile ComputingMobile SystemsPrivacy EngineeringContext-aware ComputingData ProtectionLocation PrivacyMobile Human-computer InteractionMobile PrivacyMobile SensingContext-aware LocalizationLocation AwarenessLocation SensingPower ConsumptionPower Consumption OptimizationPrivacy-preserving Model TrainingPrivacy-preserving SensingSensing
The study presents and evaluates a location‑sensing algorithm developed at Carnegie Mellon University, comparing its accuracy, training complexity, power consumption, and user suitability to other methods. The authors compare their model to various others built on different architectures and software paradigms, assessing accuracy, training complexity, power consumption, and user suitability. The algorithm achieves eight‑fold lower training complexity, higher accuracy, lower power consumption, and a more secure privacy model compared to prior approaches.
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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