Research On The Characteristic Aanalysis Method Of The Human Mobility Trajectory For The Opportunistic Network

In recent years, with the development of network technology, the feature analysis of human mobility trajectory becomes another important area of opportunistic network research. The data transport of opportunistic network depends on the movement of the nodes. Due to the fact that most of the mobile communication equipment is carried by people, the characteristics of mobility mode of the device node in the network are same to human. Therefore, it is very important to design and evaluate the opportunistic network of analysis the real data sets what collected in reality. However, currently most the mobile nodes of the networks are designed to be controlled, and the nodes move according to the other nodes or the external instructions, which can not be used in practical applications. In addition, there are problems of the large storage space, slow data transmission and analysis and other issues in the massive real trajectory datasets.In order to solve these problems, in this thesis, we study two aspects of the above problems and propose the solutions. Firstly, analyzed the real human mobility trajectory data sets and found the mobility features, the analysis method was presented for the characteristics of the mobility measures of spatial(e.g. the length of the motion path, the radius of the cyclotron), temporal(e.g. access time), connectivity attributes(e.g. communication duration, communication interval).Next, aim at the redundancy problems which large quantities of human movement data is brought, such as low query efficiency, slow the user response time and insufficient storage space, an improved sliding window trajectory compression algorithm is proposed, in which whether the current trajectory point can be compressed is based on the maximum offset distance between the reference point, while in order to reduce the compression time track and improve the compression efficiency. Experimental test results show that comparing with the existing sliding window trajectory compression algorithm, the improved sliding window track data compression algorithm has a significant reduction in compression time, and the same as compression ratio, meanwhile in the commonly used trajectory compression threshold range, the similarity of the two kinds of algorithm is very high.Finally, a comprehensive analysis software platform on the characteristics of human mobility trajectory is implemented and the above two strategies are applied in it. The experimental results show that the platform can easily realize the compression of real data sets of human mobility trajectories, draw trajectory graphics of any users, and quickly analyze the characteristics of the mobility measures of spatial, temporal and connectivity attributes.