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 large number of connected sensors and devices in Internet of Things (IoT) can generate large amounts of computing data and increase massive energy consumption. Real-time states monitoring and data processing of IoT nodes are of great significance, but the processing power of IoT devices is limited. Using the emerging mobile edge computing (MEC), IoT devices can offload computing tasks to an MEC server associated with small or macro base stations. Moreover, the use of renewable energy harvesting capabilities in base stations or IoT nodes may reduce energy consumption. As wireless channel conditions vary with time and the arrival rates of renewable energy, computing tasks are stochastic, and data offloading and renewable energy aware for IoT devices under a dynamic and unknown environment are major challenges. In this work, we design a data offloading and renewable energy aware model considering an MEC server performing multiple stochastic computing tasks and involving time-varied wireless channels. To optimize data transmission delay, energy consumption, and bandwidth allocation jointly, and to avoid the curse of dimensionality caused by the complexity of the action space, we propose a joint optimization method for data offloading, renewable energy aware, and bandwidth allocation for IoT devices based on deep reinforcement learning (JODRBRL), which can handle the continuous action space. JODRBRL can minimize the total system cost(including data buffer delay cost, energy consumption cost, and bandwidth cost) and obtain an efficient solution by adaptively learning from the dynamic IoT environment. The numerical results demonstrate that JODRBRL can effectively learn the optimal policy, which outperforms Dueling DQN, Double DQN (DDQN), and greedy policy in stochastic environments. INDEX TERMS Data offloading, energy aware, mobile edge computing, deep reinforcement learning.
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