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Techniques for Efficient Road-Network-Based Tracking of Moving Objects
May 2005 (vol. 17 no. 5)
pp. 698-712
With the continued advances in wireless communications, geo-positioning, and consumer electronics, an infrastructure is emerging that enables location-based services that rely on the tracking of the continuously changing positions of entire populations of service users, termed moving objects. This scenario is characterized by large volumes of updates, for which reason location update technologies become important. A setting is assumed in which a central database stores a representation of each moving object's current position. This position is to be maintained so that it deviates from the user's real position by at most a given threshold. To do so, each moving object stores locally the central representation of its position. Then, an object updates the database whenever the deviation between its actual position (as obtained from a GPS device) and the database position exceeds the threshold. The main issue considered is how to represent the location of a moving object in a database so that tracking can be done with as few updates as possible. The paper proposes to use the road network within which the objects are assumed to move for predicting their future positions. The paper presents algorithms that modify an initial road-network representation, so that it works better as a basis for predicting an object's position; it proposes to use known movement patterns of the object, in the form of routes; and, it proposes to use acceleration profiles together with the routes. Using real GPS-data and a corresponding real road network, the paper offers empirical evaluations and comparisons that include three existing approaches and all the proposed approaches.

[1] I.F. Akyildiz and J.S. M. Ho, “A Mobile User Location Update and Paging Mechanism under Delay Constraints,” ACM-Baltzer J. Wireless Networks, vol. 1, pp. 244-255, 1995.
[2] A. Brilingaitė, C.S. Jensen, and N. Zokaitė, “Enabling Routes as Context in Mobile Services,” Proc. ACM Int'l Workshop Geographic Information Systems, pp. 127-136, 2004.
[3] J.D. Chung, O.H. Paek, J.W. Lee, and K.H. Ryu, “Temporal Pattern Mining of Moving Objects for Location-Based Services,” Proc. Int'l Conf. Database and Expert Systems Applications, pp. 331-340, 2002.
[4] A. Čivilis, C.S. Jensen, J. Nenortaite, and S. Pakalnis, “Efficient Tracking of Moving Objects with Precision Guarantees,” Proc. Int'l Conf. Mobile and Ubiquitous Systems: Networking and Services, pp. 164-173, 2004, extended version available as DB-TR-5, Dept. of Computer Science, Aalborg Univ., Denmark, http://www.cs.aau.dk/DBTR/DBPublications DBTR-5.pdf.
[5] Z. Ding and R.H. Güting, “Managing Moving Objects on Dynamic Transportation Networks,” Proc. Int'l Conf. Scientific and Statistical Database Management, pp. 287-296, 2004.
[6] D. Fox, J. Hightower, L. Liao, D. Schultz, and G. Borriello, “Bayesian Filters for Location Estimation,” IEEE Pervasive Computing, vol. 2, no. 3, pp. 24-33, 2003.
[7] S. Goel and T. Imielinski, “Prediction-Based Monitoring in Sensor Networks: Taking Lessons from MPEG,” ACM Computer Comm. Rev., vol. 31, no. 5, 2001.
[8] H. Gowrisankar and S. Nittel, “Reducing Uncertainty in Location Prediction of Moving Objects in Road Networks,” Proc. Conf. Geographic Information Science, 2002, http://www.spatial.maine. edu/~nittel/publications giscience02_hari.pdf.
[9] C.S. Jensen, H. Lahrmann, S. Pakalnis, and J. Runge, “The INFATI Data,” Aalborg Univ., TimeCenter TR-79, 2004, http://www.cs.aau.dkTimeCenter.
[10] H.A. Karimi and X. Liu, “A Predictive Location Model for Location-Based Services,” Proc. ACM Int'l Symp. Advances in Geographic Information Systems, pp. 126-133, 2003.
[11] K.Y. Lam, O. Ulusoy, T.S.H. Lee, E. Chan, and G. Li, “An Efficient Method for Generating Location Updates for Processing of Location-Dependent Continuous Queries,” Database Systems for Advanced Applications, pp. 218-225, 2001.
[12] G. Li, K. Lam, and T. Kuo, “Location Update Generation in Cellular Mobile Computing Systems,” Proc. Workshop Parallel & Distributed Real-Time Systems, p. 96, 2001.
[13] Z. Naor and H. Levy, “Minimizing the Wireless Cost of Tracking Mobile Users: An Adaptive Threshold Scheme,” Proc. IEEE INFOCOM, pp. 720-727, 1998.
[14] G. Trajcevski, O. Wolfson, F. Zhang, and S. Chamberlain, “The Geometry of Uncertainty in Moving Objects Databases,” Proc. Int'l Conf. Extending Database Technology, pp. 233-250, 2002.
[15] O. Wolfson, “The Opportunities and Challenges of Location Information Management,” Proc. Intersections of Geospatial Information and Information Technology Workshop, 2001.
[16] O. Wolfson, S. Chamberlain, S. Dao, L. Jiang, and G. Mendez, “Cost and Imprecision in Modeling the Position of Moving Objects,” Proc. Int'l Conf. Data Eng., pp. 588-596, 1998.
[17] O. Wolfson, A.P. Sistla, S. Camberlain, and Y. Yesha, “Updating and Querying Databases that Track Mobile Units,” Distributed and Parallel Databases, vol. 7, no. 3, pp. 257-287, 1999.
[18] O. Wolfson and H. Yin, “Accuracy and Resource Concumption in Tracking and Location Prediction,” Proc. Symp. Spatial and Temporal Databases, pp. 325-343, 2003.
[19] B. Xu and O. Wolfson, “Time-Series Prediction with Applications to Traffic and Moving Objects Databases,” Proc. ACM Int'l Workshop Data Eng. for Wireless and Mobile Access, pp. 56-60, 2003.

Index Terms:
Database management, distributed databases, query processing, temporal databases.
Citation:
Alminas Civilis, Christian S. Jensen, Stardas Pakalnis, "Techniques for Efficient Road-Network-Based Tracking of Moving Objects," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 5, pp. 698-712, May 2005, doi:10.1109/TKDE.2005.80
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