WLAN Location Determination via Clustering and Probability Distributions
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Context-aware, self-scaling Fuzzy ArtMap for received signal strength based location systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on neural networks for pattern recognition and data mining
The Horus location determination system
Wireless Networks
A novel infrastructure WLAN locating method based on neural network
Proceedings of the 4th Asian Conference on Internet Engineering
SNAP: Fault Tolerant Event Location Estimation in Sensor Networks Using Binary Data
IEEE Transactions on Computers
Recurrent Grid Based Voting Approach for Location Estimation in Wireless Sensor Networks
UIC-ATC '09 Proceedings of the 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing
Survey of Wireless Indoor Positioning Techniques and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Indoor geolocation science and technology
IEEE Communications Magazine
Access Point Height Based Location Accuracy Characterization in LOS and OLOS Scenarios
Wireless Personal Communications: An International Journal
Localization in IEEE 802.11 networks by using the nelder-mead method
Proceedings of the 8th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
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Location Estimation has become important for many applications of indoor wireless networks. Received Signal Strength (RSS) fingerprinting methods have been widely used for location estimation. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. The accuracy and response time of estimation are critical issues in location estimation system for large sites. In this paper, we have proposed a distributed location estimation method, which divide the location estimation system into subsystems. Our method partitions the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between RSS fingerprint and their respective location in a subsystem. We have performed experimentation on two RSS dataset, which are gathered on different testbeds, and compared our results with benchmark RADAR method. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site.