On the semantic annotation of Wi-Fi SSID logs in mobile applications

Research on mining data collected from smartphones has received tremendous interests in the past few years. While significant research efforts have been made on mining various smartphone data, such as GPS trajectories, app usage logs, and accelerator readings, the issue of mining Wi-Fi SSID (Service Set IDentifier) logs was not well explored. The SSID of a Wi-Fi access point is normally a human-readable string, which is typically named by the owner of the Wi-Fi network. Extracting and leveraging information encoded in SSIDs are crucial for a better understanding of the smartphone data. In this study, we investigate the problem of inferring location type of a given SSID, i.e., associating an SSID with a location type, such as a workplace or a shop, where the Wi-Fi access point is installed. This paper reports our findings of leveraging human behavior patterns and spatial correlation among Wi-Fi access points to infer the location type of an SSID. Extensive experiments using real Wi-Fi logs collected from the recruited participants are conducted to evaluate the performance of the proposed schemes, and the experiment results demonstrate the effectiveness of the schemes.