Location estimation in large indoor multi-floor buildings using hybrid networks

This paper presents results for an approach for indoor location estimation that integrates received signal strength (RSS) data from both WiFi and GSM networks. Previous work has focused on relatively small indoor environments. In many potential applications, getting approximate location information, such as in which room the mobile user is, is adequate. A hierarchical clustering method is used to partition the RSS space. To choose the best transmitters in a partition, we assess the amount of RSS variance that is attributable to different base stations (BSs) or access points (APs) by transforming the RSS tuples into principal components (PCs). This allows us to retain most of the useful information of detectable transmitters in fewer dimensions. In our experiments, we collected WiFi and cellular RSS on the 2nd and 3rd-floor electronic engineering (EE) building in Queen Mary campus. The experiment results show that the proposed method can provide a good accuracy of room prediction, especially when we integrate WiFi RSS with GSM RSS together to do the positioning.