  WiFi-based localization became one of the main indoor localization techniques
due to the ubiquity of WiFi connectivity. However, indoor environments exhibit
complex wireless propagation characteristics. Typically, these characteristics
are captured by constructing a fingerprint map for the different locations in
the area of interest. This fingerprint requires significant overhead in manual
construction, and thus has been one of the major drawbacks of WiFi-based
localization. In this paper, we present an automated tool for fingerprint
constructions and leverage it to study novel scenarios for device-based and
device-free WiFi-based localization that are difficult to evaluate in a real
environment. In a particular, we examine the effect of changing the access
points (AP) mounting location, AP technology upgrade, crowd effect on
calibration and operation, among others; on the accuracy of the localization
system. We present the analysis for the two classes of WiFi-based localization:
device-based and device-free. Our analysis highlights factors affecting the
localization system accuracy, how to tune it for better localization, and
provides insights for both researchers and practitioners.
