Robust, Cost-Effective and Scalable Localization in Large Indoor Areas

Indoor location information plays a fundamental role in supporting various interesting location-aware indoor applications. Widely deployed WiFi networks make it feasible to perform indoor localization by first establishing a received signal strength (RSS) map covering the whole area based on a signal propagation model, then determining a location from an online RSS measurement given the RSS map. However, challenges remain in practical deployments, due to inaccurately estimated RSS values in the RSS map and insufficient number of access points (APs) in large indoor areas. To address these challenges, we develop a robust, cost-effective and scalable localization system (REAL). Our approach takes the error from the indoor radio signal propagation model into consideration. It also exploits information of unobserved APs at a given location and an optimal clustering method in the location searching phase. Our realworld experimental results demonstrate that REAL achieves considerable localization accuracy at a very low training cost even for a large indoor area. In addition, the results show that our scheme can also be effectively applied to Bluetooth networks with sparse signal coverage.