Robotics-based location sensing using wireless ethernet
Proceedings of the 8th annual international conference on Mobile computing and networking
WLAN Location Determination via Clustering and Probability Distributions
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Recognition of Human Activity through Hierarchical Stochastic Learning
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Cooperative Location-Sensing for Wireless Networks
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Enhancing the efficiency of active RFID-based indoor location systems
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
Clustering-based location in wireless networks
Expert Systems with Applications: An International Journal
A dynamic system approach for radio location fingerprinting in wireless local area networks
IEEE Transactions on Communications
Proceedings of the eleventh ACM international symposium on Mobile ad hoc networking and computing
Empirical evaluation of signal-strength fingerprint positioning in wireless LANs
Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems
Particle filtering: the need for speed
EURASIP Journal on Advances in Signal Processing
Review: From wireless sensor networks towards cyber physical systems
Pervasive and Mobile Computing
Robust tracking algorithm for wireless sensor networks based on improved particle filter
Wireless Communications & Mobile Computing
AWESOM: automatic discrete partitioning of indoor spaces for wifi fingerprinting
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
ARIEL: automatic wi-fi based room fingerprinting for indoor localization
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Kernel-based particle filtering for indoor tracking in WLANs
Journal of Network and Computer Applications
A Review of Tags Anti-collision and Localization Protocols in RFID Networks
Journal of Medical Systems
Multi-agent location system in wireless networks
Expert Systems with Applications: An International Journal
An Adaptive AP Selection Scheme Based on RSS for Enhancing Positioning Accuracy
Wireless Personal Communications: An International Journal
LocateMe: Magnetic-fields-based indoor localization using smartphones
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Hybrid indoor and outdoor location services for new generation mobile terminals
Personal and Ubiquitous Computing
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WLAN location estimation based on 802.11 signal strength is becoming increasingly prevalent in today's pervasive computing applications. Among the well-established location determination approaches, probabilistic techniques show good performance and, thus, become increasingly popular. For these techniques to achieve a high level of accuracy, however, a large number of training samples are usually required for calibration, which incurs a great amount of offline manual effort. In this paper, we aim to solve the problem by reducing both the sampling time and the number of locations sampled in constructing a radio map. We propose a novel learning algorithm that builds location-estimation systems based on a small fraction of the calibration data that traditional techniques require and a collection of user traces that can be cheaply obtained. When the number of sampled locations is reduced, an interpolation method is developed to effectively patch a radio map. Extensive experiments show that our proposed methods are effective in reducing the calibration effort. In particular, unlabeled user traces can be used to compensate for the effects of reducing the calibration effort and can even improve the system performance. Consequently, manual effort can be reduced substantially while a high level of accuracy is still achieved.