Digital twins (DTs) have driven major advancements across various industrial domains over the past two decades. With the rapid advancements in autonomous driving and vehicle-to-everything (V2X) technologies, integrating DTs into vehicular platforms is anticipated to further revolutionize smart mobility systems. In this paper, a new smart mobility DT (SMDT) platform is proposed for the control of connected and automated vehicles (CAVs) over next-generation wireless networks. In particular, the proposed platform enables cloud services to leverage the abilities of DTs to promote the autonomous driving experience. To enhance traffic efficiency and road safety measures, a novel navigation system that exploits available DT information is designed. The SMDT platform and navigation system are implemented with state-of-the-art products, e.g., CAVs and roadside units (RSUs), and emerging technologies, e.g., cloud and cellular V2X (C-V2X). In addition, proof-of-concept (PoC) experiments are conducted to validate system performance. The performance of SMDT is evaluated from two standpoints: (i) the rewards of the proposed navigation system on traffic efficiency and safety and, (ii) the latency and reliability of the SMDT platform. Our experimental results using SUMO-based large-scale traffic simulations show that the proposed SMDT can reduce the average travel time and the blocking probability due to unexpected traffic incidents. Furthermore, the results record a peak overall latency for DT modeling and route planning services to be 155.15 ms and 810.59 ms, respectively, which validates that our proposed design aligns with the 3GPP requirements for emerging V2X use cases and fulfills the targets of the proposed design. Our demonstration video can be found at https://youtu.be/3waQwlaHQkk .

Over the past few years, with the rapid increase in the number of natural disasters, the need to provide smart emergency wireless communication services has become crucial. Unmanned aerial Vehicles (UAVs) have gained much attention as promising candidates due to their unprecedented capabilities and broad flexibility. In this paper, we investigate a UAV-based emergency wireless communication network for a post-disaster area. Our optimization problem aims to optimize the UAV’s flight trajectory to maximize the number of visited ground users during the flight period. Then, a dual cost-aware multi-armed bandit algorithm is adopted to tackle this problem under the limited available energy for both the UAV and ground users. Simulation results show that the proposed algorithm could solve the optimization problem and maximize the achievable throughput under these energy constraints.

The combination of onboard sensors on vehicles with wireless communication has great advantages over the conventional driving systems in terms of safety and reliability. This technique is often called cooperative perception. Cooperative perception is expected to compensate for blind spots in dynamic maps, which are caused by obstacles. Few blind spots in dynamic maps can improve the safety and reliability of driving thanks to the additional information beyond the sensing of the onboard sensors. In this paper, we analyzed the required sensor data rate to be exchanged for the cooperative perception in order to enable a new level of safe and reliable automated driving in overtaking scenario. The required sensor data rate was calculated by the combination of recognition and vehicle movement to adopt realistic assumptions. In the end of the paper, we compared the required sensor data rate with the outage data rate realized by the conventional V2V communication and millimeter-wave communication. The results showed the indispensability of millimeter-wave communications in automated driving systems.

The transformation and innovation brought by vehicle-to-everything (V2X) communications are ongoing. Enhanced road safety, traffic efficiency, and vehicle automation are all promised on condition that the envisioned V2X applications can be realized. "Share what I can see" is one of the visions, known as cooperative perception (CP). CP enables sensor data sharing, while the types of data are diverse according to the levels of vehicle automation. To support sharing raw, pre-processed, and fully processed data, V2X is advancing. Emerging radio access technologies (RATs) like millimeter waves (mmWave) are introduced. V2X networks become more heterogeneous than ever before. To fill in the gaps that existing V2X networks lack interoperability and a network function to manage heterogeneous V2X, this paper proposes Het-SDVN, a software defined networking (SDN)-based vehicular network (SDVN) architecture for heterogeneous V2X. Compared to the counterparts, Het-SDVN features hierarchical control planes (C-planes), which can reduce overhead and latency, provide scalability, and guarantee the availability of CP. This paper also presents the demonstration of Het-SDVN in a real-life proof of concept. Through the response to diverse CP requests, the required V2X performance, the capability of local SDVN, and the importance of interference management are all revealed. Besides, a benchmarking test in flow installation for local and global SDVN controllers is carried out. Finally, this paper shows the visualization results of CP.

In this article, a novel joint energy and Age of Information (AoI) optimization framework for Internet of Things (IoT) devices in a nonstationary environment is presented. In particular, IoT devices that are distributed in the real-world are required to efficiently utilize their computing resources so as to balance the freshness of their data and their energy consumption. To optimize the performance of IoT devices in such a dynamic setting, a novel lifelong reinforcement learning (RL) solution that enables IoT devices to continuously adapt their policies to each newly encountered environment is proposed. Given that IoT devices have limited energy and computing resources, an unmanned aerial vehicle (UAV) is leveraged to visit the IoT devices and update the policy of each device sequentially. As such, the UAV is exploited as a mobile learning agent that can learn a shared knowledge base with a feature base in its training phase, and feature sets of a zero-shot learning method in its testing phase, to generalize between the environments. To optimize the trajectory and flying velocity of the UAV, an actor-critic network is leveraged so as to minimize the UAV energy consumption. Simulation results show that the proposed lifelong RL solution can outperform the state-of-art benchmarks by enhancing the balanced cost of IoT devices by 8.3% when incorporating warm-start policies for unseen environments. In addition, our solution achieves up to 49.38% reduction in terms of energy consumption by the UAV in comparison to the random flying strategy.

Modern wireless networks are notorious for being very dense, uncoordinated, and selfish, especially with greedy user needs. This leads to a critical scarcity problem in spectrum resources. The Dynamic Spectrum Access system (DSA) is considered a promising solution for this scarcity problem. With the aid of Unmanned Aerial Vehicles (UAVs), a post-disaster surveillance system is implemented using Cognitive Radio Network (CRN). UAVs are distributed in the disaster area to capture live images of the damaged area and send them to the disaster management center. CRN enables UAVs to utilize a portion of the spectrum of the Electronic Toll Collection (ETC) gates operating in the same area. In this paper, a joint transmission power selection, data-rate maximization, and interference mitigation problem is addressed. Considering all these conflicting parameters, this problem is investigated as a budget-constrained multi-player multi-armed bandit (MAB) problem. The whole process is done in a decentralized manner, where no information is exchanged between UAVs. To achieve this, two power-budget-aware PBA-MAB) algorithms, namely upper confidence bound (PBA-UCB (MAB) algorithm and Thompson sampling (PBA-TS) algorithm, were proposed to realize the selection of the transmission power value efficiently. The proposed PBA-MAB algorithms show outstanding performance over random power value selection in terms of achievable data rate.