SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Communications of the ACM - Wireless networking security
A Weight-based Map Matching Method in Moving Objects Databases
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
On map-matching vehicle tracking data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Addressing the Need for Map-Matching Speed: Localizing Globalb Curve-Matching Algorithms
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
Practical Lessons from Place Lab
IEEE Pervasive Computing
Improving Wireless Positioning with Look-ahead Map-Matching
MOBIQUITOUS '07 Proceedings of the 2007 Fourth Annual International Conference on Mobile and Ubiquitous Systems: Networking&Services (MobiQuitous)
Large-scale localization from wireless signal strength
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Nonmaterialized motion information in transport networks
ICDT'05 Proceedings of the 10th international conference on Database Theory
Event processing and real-time monitoring over streaming traffic data
W2GIS'12 Proceedings of the 11th international conference on Web and Wireless Geographical Information Systems
Gaze map matching: mapping eye tracking data to geographic vector features
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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Wireless Positioning Systems (WPS) are popular alternative localization methods, especially in dense urban areas where GPS has known limitations. Map-matching (MM) has been used as an approach to improve the accuracy of the estimated locations of WiFi Access Points (APs), and thus the accuracy of a wireless positioning system. Large-scale wireless positioning differs from satellite based positioning in at least two aspects: First, wireless positioning systems typically derive the location estimates based on war-driving access point (AP) data. Second, the locations of the AP beacons are not generally known at the same precision as that of the satellite locations. This results in lower accuracy and a lower confidence factor in the use of wireless positioning. This paper presents a fast selective look-ahead map-matching technique, called SLAMM. Existing MM algorithms developed for real-time location tracking of a moving vehicle are ill-suited for matching large collections of war-driving data due to the time complexity. Another unique feature of SLAMM is the map-matching of critical location samples in an AP trace to the road network before matching non-critical samples. Our experiments over a real dataset of 70 million AP samples show that SLAMM is accurate and significantly faster than the traditional MM approaches.