Research Of Mobility Model Based On Membership Clouds In Vehicular Sensor Network

As a typical application of the Internet of Things technology, vehicular sensor network has become a priority to improve the quality of road traffic. Because of the great traffic flow in city roads, it is possible to construct a stable vehicular sensor network with a certain density.In vehicular sensor network, the high speed of the nodes causes frequent change in the network topology, therefore, the network characteristics are limited. Factors affecting the behavior of mobile nodes have uncertainties such as fuzziness and randomness, the traditional mobility models of vehicles have been unable to accurately characterize the movement of nodes. Thus, it has great research value to develop vehicular sensor network models directed against the uncertainty of node movement.In this paper, the existing mobility models in vehicular sensor networks were simulated and analyzed, the differences and deficiencies of nodes in each model were summarized.Combined with the uncertainty analysis of node movement in VSN, the five kinds of uncertainty indexes which include relative speed, relative position, time dependence, spatial dependence and road dependence were proposed. The cloud digital features of each index were calculated, and the transformation between the qualitative analysis and quantitative description was completed by using the cloud model to solve the problem of fuzziness and randomness in the process of node movement.A kind of improved Intelligent Driver Model was proposed, which could cover the uncertainty to the greatest extent in the process of node movement, more accurately characterize the rule and strategy of node movement, and expand the description ability of the Intelligent Driver Model. According to the measured data, the calculated results of the improved model were compared with the measured acceleration and velocity values, which verified the validity of the model.Combined with the Vanet MobiSim and NS2 simulation platform, the influence of the improved model and Intelligent Driver Model on the routing protocol was compared. Theaverage end to end delay and packet data delivery ratio were used to characterize the influence of the two mobility models on the routing protocol.Verification and analysis showed that compared with the Intelligent Driver Model, the improved model could objectively reflect the urban road vehicle movement, improve the communication link stability in complex traffic environment, and more realistically characterize the vehicle movement.