With the ever-expanding applications of vehicles and the development of wireless communication technology, the burgeoning unmanned aerial vehicle (UAV) assisted vehicular internet of things (UVIoTs) has emerged, where the ground vehicles can experience more efficient wireless services by employing UAVs as a temporary mobile base station. However, due to the diversity of UAVs, there exist UAVs such as jammers to degenerate the performance of wireless communication between the normal UAVs and vehicles. To solve above the problem, in this paper, we propose a game based secure data transmission scheme in UVIoTs. Specifically, we exploit the offensive and defensive game to model the interactions between the normal UAVs and jammers. Here, the strategy of the normal UAV is to determine whether to transmit data, while that of the jammer is whether to interfere. We then formulate two optimization problems, i.e., maximizing the both utilities of UAVs and jammers. Afterwards, we exploit the backward induction method to analyze the proposed countermeasures and finally solve the optimal solution. Lastly, the simulation results show that the proposed scheme can improve the wireless communication performance under the attacks of jammers compared with conventional schemes.

Collaborative vehicular networks is a key enabler to meet the stringent ultra-reliable and low-latency communications (URLLC) requirements. A user vehicle (UV) dynamically optimizes task offloading by exploiting its collaborations with edge servers and vehicular fog servers (VFSs). However, the optimization of task offloading in highly dynamic collaborative vehicular networks faces several challenges such as URLLC guaranteeing, incomplete information, and dimensionality curse. In this paper, we first characterize URLLC in terms of queuing delay bound violation and high-order statistics of excess backlogs. Then, a Deep Reinforcement lEarning-based URLLC-Aware task offloading algorithM named DREAM is proposed to maximize the throughput of the UVs while satisfying the URLLC constraints in a best-effort way. Compared with existing task offloading algorithms, DREAM achieves superior performance in throughput, queuing delay, and URLLC.

Data sharing in Internet of Vehicles (IoV) makes it possible to provide personalized services for users by service providers in Intelligent Transportation Systems (ITS). As IoV is a multi-user mobile scenario, the reliability and efficiency of data sharing need to be further enhanced. Federated learning allows the server to exchange parameters without obtaining private data from clients so that the privacy is protected. Broad learning system is a novel artificial intelligence technology that can improve training efficiency of data set. Thus, we propose a federated bidirectional connection broad learning scheme (FeBBLS) to solve the data sharing issues. Firstly, we adopt the bidirectional connection broad learning system (BiBLS) model to train data set in vehicular nodes. The server aggregates the collected parameters of BiBLS from vehicular nodes through the federated broad learning system (FedBLS) algorithm. Moreover, we propose a clustering FedBLS algorithm to offload the data sharing into clusters for improving the aggregation capability of the model. Some simulation results show our scheme can improve the efficiency and prediction accuracy of data sharing and protect the privacy of data sharing.

Flying Ad hoc Network (FANET) has drawn significant consideration due to its rapid advancements and extensive use in civil applications. However, the characteristics of FANET including high mobility, limited resources, and distributed nature, have posed a new challenge to develop a secure and efficient routing scheme for FANET. To overcome these challenges, this paper proposes a novel cluster based secure routing scheme, which aims to solve the routing and data security problem of FANET. In this scheme, the optimal cluster head selection is based on residual energy, online time, reputation, blockchain transactions, mobility, and connectivity by using Improved Artificial Bee Colony Optimization (IABC). The proposed IABC utilizes two different search equations for employee bee and onlooker bee to enhance convergence rate and exploitation abilities. Further, a lightweight blockchain consensus algorithm, AI-Proof of Witness Consensus Algorithm (AI-PoWCA) is proposed, which utilizes the optimal cluster head for mining. In AI-PoWCA, the concept of the witness for block verification is also involved to make the proposed scheme resource efficient and highly resilient against 51% attack. Simulation results demonstrate that the proposed scheme outperforms its counterparts and achieves up to 90% packet delivery ratio, lowest end-to-end delay, highest throughput, resilience against security attacks, and superior in block processing time.

Re-routing system has become an important technology to improve traffic efficiency. The traditional re-routing schemes do not consider the dynamic characteristics of urban traffic, making the planned routes unable to cope with the changing traffic conditions. Based on real-time traffic information, it is challenging to dynamically re-route connected vehicles to alleviate traffic congestion. Moreover, how to obtain global traffic information while reducing communication costs and improving travel efficiency poses a challenge to the re-routing system. To deal with these challenges, this paper proposes CHRT, a clustering-based hybrid re-routing system for traffic congestion avoidance. CHRT develops a multi-layer hybrid architecture. The central server accesses the global view of traffic, and the distributed part is composed of vehicles divided into clusters to reduce latency and communication overhead. Then, a clustering-based priority mechanism is proposed, which sets priorities for clusters based on real-time traffic information to avoid secondary congestion. Furthermore, to plan the optimal routes for vehicles while alleviating global traffic congestion, this paper presents a multi-metric re-routing algorithm. Through extensive simulations based on the SUMO traffic simulator, CHRT reduces vehicle traveling time, fuel consumption, and CO₂ emissions compared to other systems. In addition, CHRT globally alleviates traffic congestion and improves traffic efficiency.

Internet of Vehicles (IoV) is an evolution of the Internet of Things (IoT) to improve the capabilities of vehicular ad -hoc networks (VANETs) in intelligence transport systems. The network topology in IoV paradigm is highly dynamic. Clustering is one of the promising solutions to maintain the route stability in the dynamic network. However, existing algorithms consume a considerable amount of time in the cluster head (CH) selection process. Thus, this study proposes a mobility aware dynamic clustering -based routing (MADCR) protocol in IoV to maximize the lifespan of networks and reduce the end -to -end delay of vehicles. The MADCR protocol consists of cluster formation and CH selection processes. A cluster is formed on the basis of Euclidean distance. The CH is then chosen using the mayfly optimization algorithm (MOA). The CH subsequently receives vehicle data and forwards such data to the Road Side Unit (RSU). The performance of the MADCR protocol is compared with that ofAnt Colony Optimization (ACO), Comprehensive Learning Particle Swarm Optimization (CLPSO), and Clustering Algorithm for Internet of Vehicles based on Dragonfly Optimizer (CAVDO). The proposed MADCR protocol decreases the end-to-end delay by 5-80 ms and increases the packet delivery ratio by 5%-15%.

Cognitive Internet of Vehicles (CIoV) can improve spectrum utilization by accessing the spectrum licensed to primary user (PU) under the premise of not disturbing the PU's transmissions. However, the traditional static spectrum access makes the CIoV unable to adapt to the various spectrum environments. In this paper, a reinforcement learning based dynamic spectrum access scheme is proposed to improve the transmission performance of the CIoV in the licensed spectrum, and avoid causing harmful interference to the PU. The frame structure of the CIoV is separated into sensing period and access period, whereby the CIoV can optimize the transmission parameters in the access period according to the spectrum decisions in the sensing period. Considering both detection probability and false alarm probability, a Q-learning based spectrum access algorithm is proposed for the CIoV to intelligently select the optimal channel, bandwidth and transmit power under the dynamic spectrum states and various spectrum sensing performance. The simulations have shown that compared with the traditional non-learning spectrum access algorithm, the proposed Q-learning algorithm can effectively improve the spectral efficiency and throughput of the CIoV as well as decrease the interference power to the PU.

Recently, in the researches on vehicular Internet-of-Things (IoT), platooning have received lots of attentions due to its potential to improve the fuel efficiency and driving experience. Platoon is a group of vehicles that act as smart agents, they travel collaboratively by following the leading human-driven vehicle. A vehicle in the platoon utilizes radar and wireless communication to share important information to other vehicles in the same platoon such as speed and acceleration, to realize the safe and efficient driving. The quality of wireless communication is of great importance to manage and maintain the platoons. However, in a scenario that a large number of vehicles exist, communication delay and packet loss caused by channel congestion may endanger the safe inter-vehicle distance. In this paper, we introduce inter-vehicle communication with directional antenna into platooning. By extensive simulations, we evaluate the packet delay and inter-vehicle distance in both normal driving and braking scenarios, and verify the usefulness of directional antenna in platooning for vehicular IoT.

With the rapid development of the Internet of vehicles (IoV), vehicle to everything (V2X) has strict requirements for ultra-reliable and low latency communications (URLLC), and massive multi-input multi-output (MIMO) channel state information (CSI) feedback can effectively support URLLC communication in 5G vehicle to infrastructure (V2I) scenarios. Existing research applies deep learning (DL) to CSI feedback, but most of its algorithms are based on low-speed outdoor or indoor environments and assume that the feedback link is perfect. However, the actual channel still has the influence of additive noise and nonlinear effects, especially in the high-speed V2I scene, the channel characteristics are more complex and time-varying. In response to the above problems, this paper proposes a CSI intelligent feedback network model for V2I scenarios, named residual mix-net (RM-Net). The network learns the channel characteristics in the V2I scenario at the vehicle user (User Equipment, UE), compresses the CSI and sends it to the channel; the roadside base station (Base Station, BS) receives the data and learns the compressed data characteristics, and then restore the original CSI. The system simulation results show that the RM-Net training speed is fast, requires fewer training samples, and its performance is significantly better than the existing DL-based CSI feedback algorithm. It can learn channel characteristics in high-speed mobile V2I scenarios and overcome the influence of additive noise. At the same time, the network still has good performance under high compression ratio and low signal-to-noise ratio (SNR).

Vehicular communications have recently attracted great interest due to their potential to improve the intelligence of the transportation system. When maintaining the high reliability and low latency in the vehicle-to-vehicle (V2V) links as well as large capacity in the vehicle-to-infrastructure (V2I) links, it is essential to flexibility allocate the radio resource to satisfy the different requirements in the V2V communication. This paper proposes a new radio resources allocation system for V2V communications based on the proximal strategy optimization method. In this radio resources allocation framework, a vehicle or V2V link that is designed as an agent. And through interacting with the environment, it can learn the optimal policy based on the strategy gradient and make the decision to select the optimal sub-band and the transmitted power level. Because the proposed method can output continuous actions and multi-dimensional actions, it greatly reduces the implementation complexity of large-scale communication scenarios. The simulation results indicate that the allocation method proposed in this paper can meet the latency constraints and the requested capacity of V2V links under the premise of minimizing the interference to vehicle-to-infrastructure communications.

The Internet of Moving Things (IoMT) takes a step further with respect to traditional static IoT deployments. In this line, the integration of new eco-friendly mobility devices such as scooters or bicycles within the Cooperative-Intelligent Transportation Systems (C-ITS) and smart city ecosystems is crucial to provide novel services. To this end, a range of communication technologies is available, such as cellular, vehicular WiFi or Low-Power Wide-Area Network (LPWAN); however, none of them can fully cover energy consumption and Quality of Service (QoS) requirements. Thus, we propose a Decision Support System (DSS), based on supervised Machine Learning (ML) classification, for selecting the most adequate transmission interface to send a certain message in a multi-Radio Access Technology (RAT) set up. Different ML algorithms have been explored taking into account computing and energy constraints of IoMT end-devices and traffic type. Besides, a real implementation of a decision tree-based DSS for micro-controller units is presented and evaluated. The attained results demonstrate the validity of the proposal, saving energy in communication tasks as well as satisfying QoS requirements of certain urgent messages. The footprint of the real implementation on an Arduino Uno is 444 bytes and it can be executed in around 50 μs.

In recent years, autonomous driving technology has made good progress, but the non-cooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving challenges. V2I (Vehicle-to-Infrastructure) communication is a potential solution to enable cooperative intelligence of vehicles and roads. In this paper, the RGB-PVRCNN, an environment perception framework, is proposed to improve the environmental awareness of autonomous vehicles at intersections by leveraging V2I communication technology. This framework integrates vision feature based on PVRCNN. The normal distributions transform(NDT) point cloud registration algorithm is deployed both on onboard and roadside to obtain the position of the autonomous vehicles and to build the local map objects detected by roadside multi-sensor system are sent back to autonomous vehicles to enhance the perception ability of autonomous vehicles for benefiting path planning and traffic efficiency at the intersection. The field-testing results show that our method can effectively extend the environmental perception ability and range of autonomous vehicles at the intersection and outperform the PointPillar algorithm and the VoxelRCNN algorithm in detection accuracy.