Tracking Network-Constrained Moving Objects with Group Updates

  • Jidong Chen
  • Xiaofeng Meng
  • Benzhao Li
  • Caifeng Lai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4016)


Advances in wireless sensors and position technologies such as GPS enable location-based services that rely on the tracking of continuously changing positions of moving objects. The key issue in tracking techniques is how to minimize the number of updates, while providing accurate locations for query results. In this paper, for tracking network-constrained moving objects, we first propose a simulation-based prediction model with more accurate location prediction for objects movements in a traffic road network, which lowers the update frequency and assures the location precision. Then, according to their predicted future functions, objects are grouped and only the central object in each group reports its location to the server. The group update strategy further reduces the total number of objects reporting their locations. A simulation study has been conducted and proved that the group update policy based on the simulation prediction is superior to traditional update policies with fewer updates and higher location precision.


Road Network Cellular Automaton Group Location Central Object Continuous Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jidong Chen
    • 1
  • Xiaofeng Meng
    • 1
  • Benzhao Li
    • 1
  • Caifeng Lai
    • 1
  1. 1.School of InformationRenmin University of ChinaBeijingChina

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