As an essential component, roadside units (RSUs) play an indispensable role in realizing Vehicle-to-Everything (V2X) by seamlessly connecting various intelligent devices and vehicles. To facilitate the construction of V2X, much research has been done in designing effective RSU deployment strategies. However, most of these efforts are largely limited by design utility and deployment scalability. To address the limitations of previous works, this paper proposes a general RSU deployment framework,Greta, which can evaluate candidate deployment sites from different perspectives with rich input data, and satisfy different requirements on optimization metrics. To this end, we model the general RSU deployment problem as a customized reinforcement learning (RL) problem that intelligently explores the deployment environment to find a good deployment strategy. Specifically, we design an effective data profiling network to extract features from multi-modality input data. These extracted features are gradually weighted, fused, and encoded as part of the state representation of the RL model. We further design new reward functions considering various deployment metrics and propose an action space pruning scheme to speed up model training. We implement a prototype system ofGretaand extensively evaluate its performance using real-world data. The results showGretaachieves remarkable performance gains compared to recent RSU deployment methods.

Most recently, vehicular fog computing (VFC) has been regarded as a novel and promising architecture to effectively reduce the computation time of various vehicular application tasks in Internet of vehicles (IoV). However, the high mobility of vehicles makes the topology of vehicular networks change fast, and thus it is a big challenge to coordinate vehicles for VFC in such a highly mobile scenario. In this paper, we investigate the joint task assignment and resource allocation optimization problem by taking the mobility effect into consideration in vehicular fog computing. Specifically, we formulate the joint optimization problem from a Min-Max perspective in order to reduce the overall task latency. Then we decompose the nonconvex problem into two sub-problems, i.e., one to one matching and bandwidth resource allocation, respectively. In addition, considering the relatively stable moving patterns of a vehicle in a short period, we further introduce the mobility prediction to design a mobility prediction-based scheme to obtain a better solution. Simulation results verify the efficiency of our proposed mobility prediction-based scheme in reducing the overall task completion latency in VFC.