Task Clustering and Resource Allocation Based on Network Slicing in IoV Scenario

With the development of artificial intelligence, automobile intelligence has become a new direction for the development of automobile and intelligent industry. At the same time, the Internet of vehicles is an important part of intelligent transportation in the future. Vehicles can use the Internet of vehicles technology to communicate with other road users and infrastructure. As the key technology of 5G network, network slicing can be tailored to the emergence of more and more types of internet of vehicles services, so as to provide better services. Based on K-means++ and Elkan k-means algorithms, cluster analysis is carried out on different types of internet of vehicles services according to the similarity of demands and map them into corresponding slices. Secondly, for resource allocation, this paper adopts adaptive inertial weight particle swarm optimization algorithm (AIW-PSO), which aims at maximizing network utility. Simulation results show that the improved algorithm improves the running time and the speed of approaching the optimal value, and the network utility is also improved compared with the traditional resource allocation method.