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Neural Networks: A Comprehensive Foundation
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A Probabilistic Room Location Service for Wireless Networked Environments
UbiComp '01 Proceedings of the 3rd international conference on Ubiquitous Computing
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
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Reducing the Calibration Effort for Location Estimation Using Unlabeled Samples
PERCOM '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications
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The Horus location determination system
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Redpin - adaptive, zero-configuration indoor localization through user collaboration
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
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AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
A taxonomy for radio location fingerprinting
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
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MELT'09 Proceedings of the 2nd international conference on Mobile entity localization and tracking in GPS-less environments
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ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
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UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
PowerLine positioning: a practical sub-room-level indoor location system for domestic use
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Challenges for social sensing using WiFi signals
Proceedings of the 1st ACM workshop on Mobile systems for computational social science
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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WiFi fingerprinting is currently one of the most popular techniques for indoor localization as it provides reasonable positioning accuracy while at the same time being able to exploit existing wireless infrastructure. To facilitate calibration efforts and to overcome fluctuations in location measurements, many indoor WiFi positioning systems utilize a discrete partitioning, e.g., a grid or a topological map, of the space where the positioning is being deployed. A major limitation of this approach, however, is that instead of considering spatial similarities in the signal environment, the partitioning is typically based on an uniform division of the space or topological constraints (e.g., rooms and walls). This can significantly decrease positioning accuracy when the signal environment is not sufficiently stable across all partitions. Moreover, current solutions provide no support for identifying partitions that are not compatible with the current wireless deployment. To overcome these limitations, we propose AWESOM (Activations Weighted by the Euclidean-distance using Self-Organizing Maps), a novel measure for automatically creating a discrete partitioning of the space where the WiFi positioning is being deployed. In addition to enabling automatic construction of a discrete partitioning, AWESOM provides a measure for evaluating the goodness of a given partitioning for a particular access point deployment. AWESOM also enables identifying partitions where additional access points should be deployed. We demonstrate the usefulness of AWESOM using data collected from two large scale deployments of a proprietary wireless positioning system in a hypermarket environment.