User-trained, zero-configuration, self-adaptive opportunistic WiFi localization for room-level accuracyUser-trained, zero-configuration, self-adaptive opportunistic WiFi localization for room-level accuracy
Faculty of Sciences. Mathematics and Computer Science
Faculty of Applied Engineering Sciences
Engineering sciences. Technology
AMBIENT 2012 : The Second International Conference on Ambient Computing, Applications, Services and Technologies
In this paper the possibility of room-level localization through Wi-Fi by using user collaboration and zero-configuration is investigated. User collaboration and zero-configuration means that it avoids the time-consuming training phase known by other systems such as fingerprinting and entering the floor plan. Fingerprints are created as soon as the users start to collaborate by providing their location and corresponding Wi-Fi data. A floor plan is not necessary as fingerprints are simply assigned to rooms without using coordinates. It is called opportunistic localization in the way that it relies on the already available infrastructure, thus no additional hardware needs to be installed. Using these methods, simulations show that a localization success rate of about 90\% can be reached and that the system is able to cope with collaboration errors and a changed environment.