Research On Compression Algorithms For Positioning Data

With the rapid development of positioning and wireless network technology, users can quickly get the current geographical location. The geographical location has the important practical significance. The location information of single user can provide positioning, navigation and location-based services. The location information of many user can be used to detect hot spots and classic lines, analyze and forecast traffic, discriminate the user behavior, etc.Positioning data are usually acquired periodically and uploaded to the server via wireless network in the location data acquisition systems. Along with the time growth, especially to the high precision positioning, a mass of positioning data will be produced. Large scale data will cause many problems. On the one hand, huge communication overheads between the terminal and the server are needed.On the other hand, heavy loads of storage spaces are also needed. It is a huge challenge whether it is terminal or server storage.To this end, an online compression algorithm for positioning data acquisition is proposed, which compresses data by reducing the number of uploaded positioning points. Error threshold can be set according to users’ needs. Feature points are extracted to upload real-timely by considering the changes of direction and speed. The question of signal loss is solved in the process of signal acquisition. If necessary, an approximation trajectory can be obtained by using the proposed recovery algorithm based on the feature points on the server. This algorithm can realize the target that compressing points with collecting them. So, it can reduce the communication cost and storage space.Positioning data with different threshold, different acquisition interval and different travel modes, including walk, non-walk and mixed mode, are used to do a lot of experiments. The experimental results have validated that the proposed algorithm is effective. At the same time, compared synthetically with other classical compression algorithms, the proposed algorithm has the advantages of better real-time performance, faster compression speed, etc. Then, it is more suitable for positioning data acquisition and tracking system with high real-time demand.