The document summarizes a study on using Wi-Fi signals for indoor location fingerprinting. It discusses how fingerprinting involves two phases: a calibration phase where signal strength is recorded at calibration points, and a location estimation phase where current signal strength is compared to the fingerprint map. It evaluates the k-nearest neighbor algorithm using Euclidean, Manhattan, and Chebychev distances to estimate location. Tests of this approach involved collecting Wi-Fi signal data at calibration points in four rooms and a hall to generate a fingerprint map for location estimation. The accuracy of Euclidean and Manhattan distances was found to be better than Chebychev distance for this location fingerprinting method.