Edge-MapReduce-Based Intelligent Information-Centric IoV: Cognitive Route Planning

With the rapid development of automatic vehicles (AVs), vehicles have become important intelligent objects in Smart City. Vehicles bring huge amounts of data for Intelligent Transportation System (ITS), and at the same time, they also put forward new application requirements. However, it is difficult to obtain and analyze massive data and provide accurate application services for AVs. In today's society of traffic explosion, how to plan the route of vehicles has become a hot issue. In order to solve this problem, we introduced content-data-friendly information-center networking (ICN) architecture into the Internet of Vehicles (IoV), and achieved efficient route planning for AVs through the Big Data acquisition and analysis architecture in ICN. We use the analytical capabilities of the network to achieve active cognitive access to traffic data. At the same time, we use game theory to achieve the incentive mechanism for task distribution and information sharing. Finally, the simulation results show that the method is effective.