The active badge location system
ACM Transactions on Information Systems (TOIS)
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
The smart floor: a mechanism for natural user identification and tracking
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Practical robust localization over large-scale 802.11 wireless networks
Proceedings of the 10th annual international conference on Mobile computing and networking
A wireless LAN-based indoor positioning technology
IBM Journal of Research and Development
Design of indoor positioning systems based on location fingerprinting technique
Design of indoor positioning systems based on location fingerprinting technique
Statistical learning theory for location fingerprinting in wireless LANs
Computer Networks and ISDN Systems
Pervasive and Mobile Computing
Bluetooth: vision, goals, and architecture
ACM SIGMOBILE Mobile Computing and Communications Review
Experiences from real-world deployment of context-aware technologies in a hospital environment
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Perspective/navigation-The Global Positioning System
IEEE Spectrum
Providing universal location services using a wireless E911 location network
IEEE Communications Magazine
A novel acoustic indoor localization system employing CDMA
Digital Signal Processing
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Received signal strength indication fingerprinting (RSSIF) is an indoor localization technique that exploits the prevalence of wireless local area networks (WLANs). Past research into RSSIF systems has seen the development of a number of algorithmic methods that provide effective indoor positioning. A key limitation, however, is that the performance of these methods is heavily dependent on practical implementation parameters and the nature of the test-bed environment. As a result, past research has tend to only compare algorithms of the same paradigm using a specific test-bed, and thus making it difficult to judge and compare their performance objectively. There is, therefore, a critical need for a study that addresses this gap in the literature. To this end, this paper compares a range of RSSIF methods, drawn from both probabilistic and deterministic paradigms, on a common test-bed. We evaluate their localization efficiency and accuracy, and also propose a number of improvements and modifications. In particular, we report on the impact of dense and transient access points (APs) - two problems that stem from the popularity of WLANs. Our results show methods that average the distance to the k nearest neighbors in signal space perform well with reduced dimensions. Moreover, we show the benefits of using the standard deviation of RSSI values to exclude transient APs. Other than that, we outline a shortcoming of the Bayesian algorithm in uncontrolled environments with highly variable APs and RSSI values, and propose an extension that uses a mode filter to restore its accuracy with increasing samples.