Adaptive Location Update Mechanism for Network-Constrained Moving Objects in Changeful Traffic Conditions

Location update strategy is one of the most important factors that affect the performance of moving objects databases. However, current motion vector based location tracking methods are designed for regular movements and are thus not suitable for transportation networks with changeful traffic conditions. To solve this problem, we propose a new location update mechanism, Adaptive Network-constrained moving object Location Update Mechanism (ANLUM), in this paper. In ANLUM, the moving object can switch between different location tracking policies according to difference traffic conditions, so that the overall performance can be improved. To evaluate the performance of the proposed method, an experimental system is implemented and the results show that ANLUM can effectively reduce the communication costs with location tracking accuracy guaranteed in traffic jammed transportation networks.