Vehicle to everything (V2X) is a new generation of information and communication technologies that connect vehicles to everything. It not only creates a more comfortable and safer transportation environment, but also has much significance for improving traffic efficiency, and reducing pollution and accident rates. At present, the technology is still in the exploratory stage, and the problems of traffic safety and information security brought about by V2X applications have not yet been fully evaluated. Prior to marketization, we must ensure the reliability and maturity of the technology, which must be rigorously tested and verified. Therefore, testing is an important part of V2X technology. This article focuses on the V2X application requirements and its challenges, the need of testing. Then we also investigate and summarize the testing methods for V2X in the communication process and describe them in detail from the architectural perspective. In addition, we have proposed an end-to-end testing system combining virtual and real environments which can undertake the test task of the full protocol stack.

A closed-loop parallel testing system implements more challenging tests to accelerate evaluation and development of autonomous vehicles.

Vehicular edge computing (VEC) is emerging as a promising architecture of vehicular networks (VNs) by deploying the cloud computing resources at the edge of the VNs. However, efficient resource management and task offloading in the VEC network is challenging. In this work, we first present a hierarchical framework that coordinates the heterogeneity among tasks and servers to improve the resource utilization for servers and service satisfaction for vehicles. Moreover, we formulate a joint resource allocation and task offloading problem (JRATOP), aiming to jointly optimize the intra-VEC server resource allocation and inter-VEC server load-balanced offloading by stimulating the horizontal and vertical collaboration among vehicles, VEC servers, and cloud server. Since the formulated JRATOP is NP-hard, we propose a cooperative resource allocation and task offloading algorithm named BARGAIN-MATCH, which consists of a bargaining-based incentive approach for intra-server resource allocation and a matching method-based horizontal-vertical collaboration approach for inter-server task offloading. Besides, BARGAIN-MATCH is proved to be stable, weak Pareto optimal, and polynomial complex. Simulation results demonstrate that the proposed approach achieves superior system utility and efficiency compared to the other methods, especially when the system workload is heavy.

The vehicular network needs efficient and reliable data communication technology to maintain low latency. It is very challenging to minimize the energy consumption and data communication delay while the vehicle is moving and wireless channels and bandwidth are time-varying. With the help of the emerging mobile edge computing (MEC) server, vehicles and roadside units (RSUs) can offload computing tasks to MEC associated with base station (BS). However, the environment for offloading tasks to MEC, e.g., wireless channel states and available bandwidth, is unstable. Therefore, ensuring the efficiency of computing offloading under such an unstable environment is a challenge. In this work, we design a task computation offloading model in a heterogeneous vehicular network; this model takes into account multiple stochastic tasks, the variety of wireless channels and bandwidth. To obtain the tradeoff between the cost of energy consumption and the cost of data transmission delay and avoid curse of dimensionality caused by the complexity of the large action space, we propose an adaptive computation offloading method based on deep reinforcement learning (ACORL) that can address the continuous action space. ACORL adds the Ornstein-Uhlenbeck (OU) noise vector to the action space with different factors for each action to validate the exploration. Multi transmission equipment can execute local processing and computing offloading to MEC. Nevertheless, ACORL considers the variety of wireless channels and available bandwidth between adjacent time slots. The numerical results illustrate that the ACORL algorithm can effectively learn the optimal policy, which outperforms the Dueling DQN and greedy policy in the stochastic environment.

This article proposes a new solution for end-to-end autonomous mining operations: Internet of Things (IoT)-based parallel mining, consisting of the concept definition, the solution given, and the concrete realization. The proposed parallel mining is inspired by the artificial societies (A) for modeling, computational experiments (C) for analysis, and parallel execution (P) for control (ACP) approach. The basic framework of parallel mining is given and its advantages are expounded. Then, the solution of parallel mining is proposed, which is mainly composed of four parts: 1) the management and control center for autonomous mining; 2) the autonomous transportation platform of truck; 3) the semiautonomous mining/shovel platform; and 4) the remote takeover platform. Key technologies of IoT-based parallel mining are discussed in detail, namely, network communication, virtual parallel mining construction, mining environment perception over-the-horizon for the moving area and obstacle detection, collaborative decision making, planning, and control for unmanned mining equipment, and parallel taking-over and remote control. Finally, the performance of IoT-based parallel mining, including fusion perception, collaborative decision making, planning, and control, is evaluated. The realization of parallel mining can fundamentally improve the safety of personnel and equipment, reduce the cost of mining operation, and increase the production rate.

In the Internet of Things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined.