AI Empowered Computing Resource Allocation in Vehicular Ad-hoc NETworks

Abstract

The vehicular ad hoc network (VANET) has emerged as a heterogeneous network with no fixed infrastructure. This paper proposes an AI-empowered task offloading and computing resource allocation model which can manage the computing resources in VANET dynamically. The model is divided into two layers. First, the task offloading layer, where the Random Forest (RF) algorithm is used to determine offloading the vehicle's computing tasks whether to the Cloud Computing (CC) server or Mobile Edge Computing (MEC) server or to be processed locally (in-vehicle computing). Second, the resource allocation layer, where the Deep Deterministic Policy Gradient (DDPG) algorithm is used to determine the computing platform again when the task is determined to be offloaded to either MEC servers or the cloud servers. To evaluate the performance of the RF classifier, we applied the model to a real-world driving trajectory dataset, and then compared the results with a different set of Machine Learning (ML) algorithms namely, K-nearest neighbour (KNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM). The results show the RF model outperformed other models in classification accuracy score of 99.83% for task offloading decision, where the KNN, MLP and SVM achieved 9S%, 94.Sl% and 90.94%, respectively. Moreover, the DDPG based resource allocation scheme converges within 150 episodes and reduced the latency cost by 85%.