Topology control based on dynamic graph embedding in Internet of vehicles - Yanfei SUN; Jiazheng YIN; Jin QI; Xiaoxuan HU; Mengting CHEN; Zhenjiang DONG (Tongxin xuebao)
Objectives:With the growth of the automotive market, the road carrying pressure is increasing. However, due to the dynamics, complexity and poor communication environment of Internet of vehicles (IoV), as well as the rapidly changing distance and occlusion between vehicles, frequent chain scission and signal fading among the nodes of the network. It is difficult to control the topology of network. In order to build a more stable and reasonable IoV, fuzzy inference and other methods was used to extract vehicle features, and a graph embedding method for the IoV environment was proposed to make full use of vehicle features to build a network, so as to realize the topology discovery and control of IoV. Methods:The proposed label-range graph embedding (LRGE) method was used to discover and control the topology of IoV. The first thing is to establish the vehiclar network model. The road was reasonably divided into several sub networks according to the road side unit (RSU). The driver assistance system was used to obtain the relevant historical information of the vehicle. Fourier transform and fuzzy inference methods were used to extract the driving features of the drivers, and the low-dimensional feature vector was obtained by processing the vehicle information. Then, proposed range based cold boot method was applied to the vehicles newly added to the network. According to the vehicle features of the target vehicle in join area, the feature vector was updated and modified to make it easier to establish the connections with the vehicle nodes in the new network. Finally, based on the application environment of the IoV, the LRGE method was used to perform random walk among vehicle nodes, and then the established random walk was optimized according to the LRGE model to determine the adjacency matrix. This matrix was dynamically updated according to the historical sequence information. According to the characteristics of the actual IoV, this method fused the feature vector and the driver feature label to realize topology discovery and control. The flat fading composite channel model was used to simulate the actual communication environment and the actual effect was tested on the NGSIM dataset. Results:From the simulation results under NGSIM dataset and flat fading composite channel, it can be seen that LRGE method could basically achieve more reasonable network topology control. Vehicles with more aggressive driving styles prefer to establish connections with forward vehicle clusters, and vehicles with similar driving styles are able to establish stable connections for a longer time. It is contrasted with random network, DeepWalk methods,Node2Vec methods and dynamic growth (DN) algorithm. By testing the established connection number, chain scission probability and average hops between reachable nodes, it is found that the networks established by Node2Vec method and LRGE method are more reasonable, with low chain scission probability, less network redundancy and more practical network topology. In order to reflect the difference of the main target, connectivity and robustness of the network, the connected probability, the importance distribution represented by PageRank and the proportion of cut-vertices were contrasted. The network established by LRGE method has higher connected probability. Its centrality distribution of nodes is flat, and the proportion of cut-vertices is relatively small, so it has advantages in connectivity and robustness. The experimental results were further verified by comparing the relative relations under different communication environment. Conclusions:Although the high dynamics and complexity of IoV make it difficult to build a reasonable and stable network, vehicles and drivers have different features. Therefore, these features can be extracted and used to assist in controlling the topology of network. Driver features are extracted through fuzzy inference and other methods, and the graph embedding method for IoV can make full use of vehicle feature information. Hence, the network topology can be constructed more reasonably and effectively. Moreover, the graph embedding method is simple to calculate, and the performance of established network is better. It can make a rapid response to the dynamic IoV, update the network topology in time, and finally realize the topology control of IoV with good dynamics, connectivity and robustness.