Characterization of WLAN Location Fingerprinting Systems

Wireless LAN (WLAN) location fingerprinting is an important approach for local positioning system, a complementary and alternative technology to the global positioning system (GPS). Fingerprinting is a method of localization that associates individual positions with distinct features and uses them to infer absolute locations of targets. The widespread presence of WLANs (based on IEEE 802.11 standard) makes it natural to consider WLAN signals to construct fingerprinting based localization systems, especially in indoor environments. Although WLAN location fingerprinting is reasonably well studied and diverse approaches have been proposed, its application in practice is still quite limited owing to concerns on its robustness. This suggests the need for further research to deepen understanding of WLAN location fingerprinting and identify components that could be improved to enhance its overall robustness. In this thesis, we study the impact of different alternatives for WLAN location fingerprints, an issue that is ignored or rarely discussed in prior work. Moreover, we systematically evaluate WLAN fingerprinting systems as a whole considering different localization algorithms as well as different fingerprint types. Our analysis highlights the impact of various features of WLAN fingerprints including visibilities of access points, virtual access points and properties of WiFi received signal strength, which is used to construct fingerprints. The results show that assumptions concerning WLAN enabled environments made by many schemes do not hold in reality. Various methods for each component of fingerprinting systems are included to find optimized combinations. And by analyzing and comparing performance of six different localization algorithms with their optimized configurations, we find that localization algorithms have limited impact. Popular algorithms have similar behaviors with appropriately chosen fingerprint types, and positioning errors are heavily dependent on the construction of fingerprints and the environment. We also find that results aggregation, a common component in positioning systems for error compensations, is not beneficial for fingerprinting.