Hassan A. Karimi
ABSTRACT
Location-Based Services (LBSs) utilize information about users' locations through location-aware mobile devices to provide services, such as nearest features of interest, they request. This is a common strategy in LBSs and although it is needed and benefits the users, there are additional benefits when future locations (e.g., locations at later times) are predicted. One major advantage of location prediction is that it provides LBSs with extended resources, mainly time, to improve system reliability which in return increases the users' confidence and the demand for LBSs. However, much of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs whose objective is to compute and present on-road services, because a cell may contain several roads while the computation and delivery of a service may require the exact road on which the user is driving. We propose a new model, called Predictive Location Model (PLM), to predict locations in LBSs with road-level granularities. The premise of PLM is geometrical and topological techniques allowing users to receive timely and desired services.
- D. H. Stojanovic and S. J. Djordjevic-Kajan. Developing location-based services from a GIS perspective. In 5th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service (TELSIKS 2001), vol. 2, pp. 459--462, 2001.Google Scholar
Cross Ref
- N. Davis, K. Cheverst, K, Mitchell, and A. Friday. Caches in the air: Disseminating information in the guide system. In Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications (WMCSA '99), New Orleans, USA, February 1999. Google ScholarDigital Library
- K. Cheverst, N. Davies, K. Mitchell, and A. Friday. Experiences of developing and deploying a context-aware tourist guide. In Proceedings of the sixth annual international conference on Mobile computing and networking, August 2000. Google ScholarDigital Library
- R. Jana, T. Johnson, S. Muthukrishnan, and A. Vitaletti. Location based services in a wireless WAN using cellular digital packet data (CDPD). In Second ACM international workshop on Data engineering for wireless and mobile access, May 2001. Google ScholarDigital Library
- H. D. Chon, D. Agrawal, and A. E. Abbadi. NAPA: Nearest Available Parking Lot Application. In Proceedings of the 18th International Conference on Data Engineering (ICDE'02), 2002. Google ScholarDigital Library
- P. Bahl, A. Balachandran, A. Miu, G. Voelker, W. Russell, and Y. Wang. PAWNs: Satisfying the Need for Ubiquitous Connectivity and Location Services. IEEE Personal Communications Magazine (PCS), Vol. 6, October 2001. Google ScholarDigital Library
- A. T. Campbell, J. Gomez, S. Kim, C. Wan. Comparison of IP Micro-Mobility Protocols. IEEE Wireless Communications Magazine, Vol. 9, No. 1, February 2002. Google ScholarDigital Library
- S. K. Das and S. K. Sen. Adaptive Location Prediction Strategies Based on a Hierarchical Network Model in a Cellular Mobile Environment. The Computer Journal, Vol. 42, No.6, 1999.Google ScholarCross Ref
- B. P. Vijay Kumar and P. Venkataram. Prediction-based location management using multilayer neural networks. Journal of Indian institute of science, pp.7--21, 2002.Google Scholar
- J. Biesterfeld, E. Ennigrou, and K. Jobmann. Location Prediction in Mobile Networks with Neural Networks. In Proc. of the International Workshop on Applications of Neural Networks to Telecommunications '97, S. 207--214, Melbourne, June 1997.Google Scholar
- S. H. Shah, and K. Nahrstedt. Predictive Location-Based QoS Routing in Mobile Ad Hoc Networks. In Proceedings of IEEE International Conference on Communications (ICC 2002), New York, NY, April 2002.Google ScholarCross Ref
- U. Kubach and K. Rothermel. An Adaptive, Location-Aware Hoarding Mechanism. In Proceedings of the Fifth IEEE Symposium on Computers and Communications (ISCC 2000), pp. 615--620, Antibes, France, July 2000. Google ScholarDigital Library
- U. Kubach. A Map-Based, Context-Aware Hoarding Mechanism. Berichtskolloquium des Graduiertenkollegs Parallele und Verteilte Systeme, University of Stuttgart, Germany, July 2000.Google Scholar
- Y. Zhao. Mobile Phone Location Determination and Its Impact on Intelligent Transportation Systems. IEEE Transaction on Intelligent Transportation Systems, Vol. 1, No.1, March 2000. Google ScholarDigital Library
- H. Gowrisankar, and S. Nittel. Reducing Uncertainty in Location Prediction of Moving Objects in Road Networks. In 2nd Int. Conference on Geographic Information Science (GIScience 2002), Boulder, Colorado, September 2002.Google Scholar
- O. Wolfson. The Opportunities and Challenges of Location Information Management. In Intersections of Geospatial Information and Information Technology Workshop, 2001.Google Scholar
- Tranplan Associates. Waterloo Region Travel Survey 1987: An Overview of the Survey Findings. Regional Municipality of Waterloo, Department of Planning and Development, October 1989.Google Scholar
- T. Tugcu, and C. Ersoy. Application of a Realistic Mobility Model to Call Admissions in DS-CDMA Cellular Systems. In Vehicular Technology Conference (VTC'2001), spring, Rhodes, Greece, May 2001.Google ScholarCross Ref
- S. Schönfelder. Some notes on space, location and travel behaviour. In Swiss Transport Research Conference, Monte Verita, Ascona, 2001.Google Scholar
- J. Scourias and T. Kunz. An Activity-based Mobility Model and Location Management Simulation Framework. In Workshop on Modeling and Simulation of Wireless and Mobile Systems (MSWiM'99), Seattle, USA, August 1999. Google ScholarDigital Library
- White Paper: What is 3G? http://www.genericsgroup.com/what/consultancy/whatis3G.pdfGoogle Scholar
Index Terms
- A predictive location model for location-based services
Recommendations
Location anonymity in continuous location-based services
GIS '07: Proceedings of the 15th annual ACM international symposium on Advances in geographic information systemsA major concern for large-scale deployment of location-based services (LBSs) is the potential abuse of their client location data, which may imply sensitive personal information. Location privacy protection is challenging because a location itself may ...
Feeling-based location privacy protection for location-based services
CCS '09: Proceedings of the 16th ACM conference on Computer and communications securityAnonymous location information may be correlated with restricted spaces such as home and office for subject re-identification. This makes it a great challenge to provide location privacy protection for users of location-based services. Existing work ...
A method for predicting future location of mobile user for location-based services system
Convergence of location-aware devices, wireless communications, and geographic information system (GIS) functionalities has been enabling the deployment of a new generation of selective information disseminating services and location-based services (...
Comments