Employing User Feedback for Fast, Accurate, Low-Maintenance Geolocationing
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
ACM Computing Surveys (CSUR)
Integrated data management for mobile services in the real world
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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
Positioning and Orientation in Indoor Environments Using Camera Phones
IEEE Computer Graphics and Applications
A long-duration study of user-trained 802.11 localization
MELT'09 Proceedings of the 2nd international conference on Mobile entity localization and tracking in GPS-less environments
Growing an organic indoor location system
Proceedings of the 8th international conference on Mobile systems, applications, and services
Proceedings of the 12th ACM international conference on Ubiquitous computing
Survey of Wireless Indoor Positioning Techniques and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Hybrid Indoor Positioning with Wi-Fi and Bluetooth: Architecture and Performance
MDM '13 Proceedings of the 2013 IEEE 14th International Conference on Mobile Data Management - Volume 01
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Indoor positioning systems based on fingerprinting techniques generally require costly initialization and maintenance by trained surveyors. Organic positioning systems aim to eliminate these deficiencies by managing their own accuracy and obtaining input from users and other sources. Such systems introduce new challenges, e.g., detection and filtering of erroneous user input, estimation of the positioning accuracy, and means of obtaining user input when necessary. We envision a fully organic indoor positioning system, where all available sources of information are exploited in order to provide room-level accuracy with no active intervention of users. For example, such systems can exploit pre-installed cameras to associate a user's location with a Wi-Fi fingerprint from the user's phone; and it can use a calendar to determine whether a user is in the room reported by the positioning system. Numerous possibilities for integration exist that may provide better indoor positioning.