TitleImproving performance, privacy and relevance of location-based services for mobile users
NameShankar, Pravin (author), Iftode, Liviu (chair), Ganapathy, Vinod (internal member), Nath, Badri (internal member), Ebling, Maria (outside member), Rutgers University, Graduate School - New Brunswick,
Location-based services ,
Electronic apparatus and appliances
DescriptionLocation-based services are becoming increasingly popular due to the ubiquity of smartphones and the emergence of vehicular computing applications. Mobile clients have traditionally been consumers of location-based services, but various social applications have recently demonstrated that mobile clients can also be producers of location-based data. Our thesis is that the quality of the location-based services critically depends on the performance of the service, the privacy assurances offered to the clients, and the quality of the data provided by the service. In this dissertation, we propose three contributions addressing key aspects of these challenges: network performance, location privacy for the mobile clients, and relevance of data provided by the service. With respect to the wireless network performance for location-based services, we present Context-Aware Rate Selection (CARS), a rate adaptation algorithm that makes use of knowledge of speed and distance between communicating nodes to choose the optimum transmission rate. Our experimental evaluation in real outdoor vehicular environments shows that CARS adapts to changing link conditions at high vehicular speeds significantly faster than existing rate-adaptation algorithms. With respect to the client location privacy, we present SybilQuery, a fully decentralized and autonomous k-anonymity-based scheme that generates synthetic queries that resemble a real client query. Our experiments on real mobility traces of approximately 500 cabs in the San Francisco Bay area show that SybilQuery can efficiently generate synthetic queries and that these queries are indistinguishable from real queries. Finally, with respect to improving the relevance of location-based data, we present SocialTelescope, a novel location-based service that leverages user interactions in mobile social networks to infer people's preference for places. Our results evaluating the coverage and relevance of our system in comparison to current state-of-the-art approaches show that our approach returns results that are at least as relevant as those returned by current approaches, at a substantially lower cost. The main conclusion of this dissertation is that location-based services can become truly ubiquitous services by providing mobile clients with good network performance, privacy guarantees, as well as relevant results.
NoteIncludes bibliographical references
Noteby Pravin Shankar
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.