WiFi-based localization became one of the main indoor localization techniquesdue to the ubiquity of WiFi connectivity. However, indoor environments exhibitcomplex wireless propagation characteristics. Typically, these characteristicsare captured by constructing a fingerprint map for the different locations inthe area of interest. This fingerprint requires significant overhead in manualconstruction, and thus has been one of the major drawbacks of WiFi-basedlocalization. In this paper, we present an automated tool for fingerprintconstructions and leverage it to study novel scenarios for device-based anddevice-free WiFi-based localization that are difficult to evaluate in a realenvironment. In a particular, we examine the effect of changing the accesspoints (AP) mounting location, AP technology upgrade, crowd effect oncalibration and operation, among others; on the accuracy of the localizationsystem. We present the analysis for the two classes of WiFi-based localization:device-based and device-free. Our analysis highlights factors affecting thelocalization system accuracy, how to tune it for better localization, andprovides insights for both researchers and practitioners.
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