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

Abstract

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 (AP) 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 about APs which are not visible at a given location and an optimal clustering method in the location searching phase. Our real-world 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.

Built by the metalsmith-blue Metalsmith pipeline.
Created 2/11/2016
Updated 2/11/2016
Commit af8efe3 // History // View
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