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.

Mobile commerce and location based services (LBS) are some of the fastest growing IT industries in the last five years. Location update of mobile clients is a fundamental capability in mobile commerce and all types of LBS. Higher update frequency leads to higher accuracy, but incurs unacceptably high cost of location management at the location servers. We propose RoadTrack -- a road-network based, query-aware location update framework with two unique features. First, we introduce the concept of precincts to control the granularity of location update resolution for mobile clients that are not of interest to any active location query services. Second, we define query encounter points for mobile objects that are targets of active location query services, and utilize these encounter points to define the adequate location update schedule for each mobile. The RoadTrack framework offers three unique advantages. First, encounter points as a fundamental query awareness mechanism enable us to control and differentiate location update strategies for mobile clients in the vicinity of active location queries, while meeting the needs of location query evaluation. Second, we employ system-defined precincts to manage the desired spatial resolution of location updates for different mobile clients and to control the scope of query awareness to be capitalized by a location update strategy. Third, our road-network based check-free interval optimization further enhances the effectiveness of the Road-Track query-aware location update scheduling algorithm. This optimization provides significant cost reduction for location update management at both mobile clients and location servers. We evaluate the RoadTrack location update approach using a real world road-network based mobility simulator. Our experimental results demonstrate that the RoadTrack query aware location update approach outperforms existing representative location update strategies in terms of both client energy efficiency and server processing load.

Within collaborative computing, computer mediated communications are evolving rapidly thanks to the development of new technologies. The facilitation of awareness and discovery of users in the communications networks is a key requirement for the success of these collaborative systems. Besides the ne

Commercial aerial imagery websites, such as Google Maps, MapQuest, Microsoft Virtual Earth, and Yahoo! Maps, provide high- seamless orthographic imagery for many populated areas, employing sophisticated equipment and proprietary image postprocessing pipelines. There are many areas of the world with poor coverage where locals might benefit from recent, high-resolution orthographic imagery, but which do not fit into the schedules and scaling model of the big sites.

This paper presents PrivacyGrid - a framework for supporting anonymous location-based queries in mobile information delivery systems. The PrivacyGrid framework offers three unique capabilities. First, it provides a location privacy protection preference profile model, called location P3P, which allows mobile users to explicitly define their preferred location privacy requirements in terms of both location hiding measures (e.g., location k-anonymity and location l-diversity) and location service quality measures (e.g., maximum spatial resolution and maximum temporal resolution). Second, it provides fast and effective location cloaking algorithms for location k-anonymity and location l-diversity in a mobile environment. We develop dynamic bottom-up and top-down grid cloaking algorithms with the goal of achieving high anonymization success rate and efficiency in terms of both time complexity and maintenance cost. A hybrid approach that carefully combines the strengths of both bottom-up and top-down cloaking approaches to further reduce the average anonymization time is also developed. Last but not the least, PrivacyGrid incorporates temporal cloaking into the location cloaking process to further increase the success rate of location anonymization. We also discuss PrivacyGrid mechanisms for supporting anonymous location queries. Experimental evaluation shows that the PrivacyGrid approach can provide close to optimal location k-anonymity as defined by per user location P3P without introducing significant performance penalties.