Research On Collaborative Data Dissemination And Caching In Edge Assisted Vehicular Networks

With the development of mobile communication technology,the safety and non-safety services of vehicle users are rapidly increasing.Compared with cloud computing,edge computing can provide vehicle users with more timely and reliable services.Edge storage and bandwidth resources can be used to cache data for various mobile services,such as multimedia,augmented reality and news.Due to limited storage and bandwidth resources,the edge's ability to provide timely services to vehicles is limited.In addition,some intelligent driving services,such as moving object detection and motion trajectory prediction,are distributed and cached at the edge or other vehicles.Vehicles requesting data can be obtained from multiple source nodes,and a suitable source node needs to be selected for the requester for data transmission.Under the influence of practical factors such as increased vehicle density on the road,high mobility of the vehicle,data timeliness,and limited edge resources,the edge should try to meet the service requests of vehicle users.This thesis focuses on the data caching and dissemination of service data cache in different scenarios in the vehicular networks.The main work is as follows:(1)The scenario where the vehicle requested data is cached in the base station.In order to reduce the amount of data offloaded from the base station,a collaborative data dissemination strategy through vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)communication is possible.However,existing methods give priority to V2 I transmission,and then explore V2 V transmission without communication conflict with V2 I.This method cannot fully utilize V2 V broadcasting to reduce the base station's traffic overhead.Therefore,this paper proposes a novel collaborative data dissemination problem in the edge assisted vehicular networks.Decide when to inject which data into which vehicle,and decide whether the vehicle obtains the required data directly from base station or nearby neighbor vehicles.Under the deadline of the data request,the traffic overhead from the base station is minimized.For different road conditions,we propose three heuristic algorithms to solve this problem.Among them is the OFfline hybrid Data Dissemination algorithm(OFDD),which preferentially finds the most beneficial V2 V broadcast and then selects the feasible V2 I transmission.On the basis of OFDD,we have developed online algorithms based on snapshots and predictions respectively.They measure V2 V and V2 I transmissions,so as to select appropriate communication to meet vehicle requests.Through a large number of simulation experiments,it is proved that the algorithm proposed in this paper is superior to the latest method in data acquisition rate and base station traffic overhead.(2)In the scenario where data requested by the vehicle is distributed and cached at the edge or other vehicles,the vehicle requesting the data(destination node)may be cached at the same time in other vehicles or edge nodes(source nodes).We need to select the appropriate source node for the destination node for data transmission to satisfy the request of the vehicle as much as possible.Therefore,this paper proposes a routing and dynamic caching problem based on edge computing from multiple sources to multiple destinations.Considering the timeliness of the data and the request deadline constraints,we must select a suitable source node for the destination node,and determine the route and cache strategy from the source node to the destination node to maximize the success rate of the request.In order to solve this problem,we convert the routing problem into a minimum Steiner tree problem.We also developed a multiagent reinforcement learning algorithm to decide which source node the target node chooses and the node's caching strategy.Simulation results show that the algorithm proposed in this paper has a significant improvement in request success rate compared to the benchmark algorithm.