In vehicular cooperative perception (CP), numerous resource allocation strategies have been proposed to enhance urban autonomous driving. However, existing studies often overlook the competition between self-perception and cooperative perception, where degrading a ground vehicle's (GV's) self-perception may introduce safety risks and reduce passenger comfort. Moreover, UAV–GV cooperation—which can improve sensing precision, reduce task execution delay, and enhance CP service availability—has received limited attention. It is worth noting that unmanned aerial vehicles (UAVs) are unavailable for cooperative perception during the recharging process. To address these issues, this paper investigates on-demand scheduling strategy in UAV–GV cooperative perception. At the millisecond timescale, resource competition is considered in real-time sensing, communication, and computation (SC2) resource allocation. At the minute timescale, the idle flying period between consecutive tasks is utilized for UAV recharging through attachment to GVs along the route. Specifically, we first develop a model that captures the mutual influence between UAVs and GVs on perception performance under resource constraints. Then, a mixed-timescale solution is proposed: at the small timescale, a multi-agent deep reinforcement learning (MA-DRL) algorithm with gradient-free projection and auxiliary supervision is designed to schedule SC2resources; at the large timescale, a Hungarian-based algorithm is employed to control UAV recharging. Simulation results show that the proposed approach outperforms benchmark schemes by reducing task execution delay and energy consumption, and enhancing CP service availability, while satisfying sensing precision, GV safety, and passenger comfort requirements.

Federated Learning (FL) enables the collaborative training of models by users distributed across various locations, transforming traditional data sharing into model sharing. This paradigm holds the promise of facilitating the development of safe, reliable, and accurate driving models within Internet of Vehicles (IoV), with its performance contingent upon the stability of the training process. However, traditional FL relies on a central server for aggregation, which is susceptible to malicious attacks. Moreover, limited communication resources prevent the inclusion of all users in the training process. To resolve issues related to reliability and resource utilization, this paper proposes a reliable Rotating Server Federated Learning (RSFL) algorithm to enhance the security and efficiency of FL. Specifically, we first consider the vehicular topology and participation in FL during their transition, and introduce a server rotation algorithm that incorporates a weighted sum of multiple factors including model training activity, vehicle credibility, speed stability, and distance to augment system security. Additionally, addressing the limitation of server channel resources that can impede FL efficiency, this paper proposes a method to select high-quality users for channel resource allocation by comprehensively considering participation latency, contribution, energy, and channel state during the FL process. This optimizes resource usage at the FL server side and constructs an efficiency-maximization problem for FL to improve the convergence rate. Simulation results confirm that the proposed RSFL algorithm can significantly enhance the security and system efficiency of FL.

In digital-twin-enabled heterogeneous vehicular networks (DT-HetVNets), vehicles need to synchronize data to their DTs deployed in the cloud for decision-making. However, for a vehicle which is simultaneously covered by a group of heterogeneous network infrastructures, the DT of the vehicle (DT-V) can connect with the DTs of infrastructures (DT-Is) in different infrastructure groups across regions in the virtual networks so that each DT-V may deceive the DT-Is by interacting with multiple DT-I groups and selecting the optimal one to synchronize data. To this end, we propose an on-demand data synchronization scheme for DT-Vs and DT-Is. In the scheme, infrastructures and vehicles are grouped based on their geographical locations and the arrival time of each vehicle through which the DT-Vs and DT-Is can interact with each other to make decisions in groups. Then, the requirements of DT-Vs (i.e., minimize synchronization cost and maximize synchronization satisfaction) and DT-Is (i.e., maximize profits) are considered to design their utility functions and the decision-making process between the DT-Vs in each group and the DT-Is in each group is formulated as a Stackelberg game to obtain their optimal strategies. After that, considering the deceptive behavior of vehicles, a joint optimization algorithm that integrates the Stackelberg game and the selection of each DT-V is designed to obtain the real equilibrium solution for DT-Vs and DT-Is to maximize their utilities. Simulation results show that our scheme can obtain the highest utilities compared with the traditional schemes.

Edge collaboration is expected to effectively relieve the load of base stations and enhance the driving experience of autonomous vehicles (AVs). However, in existing edge collaboration schemes, the frequent information exchange between AVs will consume a significant amount of resources. In addition, the existing schemes ignore the types of services, where services with different types may be combined into a composite service which affects the utility of AVs. To this end, we consider various types of services in autonomous vehicular networks (AVNs) and propose a digital twin (DT)-enabled edge collaboration scheme for composite services. Specifically, we first divide the DTs of service requesters (DT-SRs) into service request groups (SRGs) based on the same basic service requests and propose an architecture to facilitate the edge collaboration between the DTs of the leaders of SRGs (DT-L-SRGs) and the DTs of the service providers (DT-SPs). In this architecture, different service composition forms will result in different resource purchase strategies for DT-L-SRGs and different resource pricing strategies for DT-SPs. Therefore, we model the process of service composition as a coalition game to determine the optimal service composition form for each basic service. In the process of the coalition game, in order to obtain the optimal resource purchase strategy for each DT-L-SRG and the optimal resource pricing strategy for each DT-SP under different coalition structures, the interaction between the DT-L-SRGs and the DT-SPs is formulated as a Stackelberg game. By obtaining the game equilibrium, the optimal strategies of each DT-L-SRG and each DT-SP can be determined to measure the performance of the given coalition structure until a stable and optimal composite service structure is finally formed through multiple rounds of iterations. Compared with traditional schemes, the simulation results demonstrate that our scheme can bring the highest utilities to both the SRs and the SPs.

Connected vehicles (CVs) utilizing cellular vehicle-to-everything (C-V2X) technology are increasingly coexisting on the road with regular vehicles (RVs). As these CVs interact with each other and with roadside infrastructure through vehicle-vehicle and vehicle-infrastructure cooperation, the characteristics of traffic flow are changing in significant ways. It is therefore crucial to understand how different parameters of CVs, roadside sensors, and V2X communications affect the stability of heterogeneous traffic flow. In this research, we investigate the impact of several transportation and infrastructure parameters on the stability of heterogeneous traffic flow. Specifically, we first examine the effects of traffic density, penetration rate of CVs, detection accuracy of roadside sensors, and time delays in V2X communications. We propose a novel C-V2X-based vehicle-vehicle/vehicle-infrastructure cooperation architecture and develop a car-following model based on it. Then, the theoretical stability condition for heterogeneous traffic flow is derived, which reveals the interdependence of transportation and infrastructure parameters. The numerical simulations show that the proposed C-V2X-based vehicle-vehicle/vehicle-infrastructure cooperation architecture achieves traffic flow stability at lower CV penetration rates compared to existing studies that only consider vehicle-to-vehicle communications. This finding highlights the importance of leveraging the full potential of C-V2X technology for improving traffic flow stability in real-world settings.

The escalating growth of the Internet of Things (IoT) has intensified the demand for dependable and efficient communication networks to accommodate the massive data volumes produced by interconnected devices. Satellite networks have emerged as a promising alternative, particularly in remote and underserved regions where terrestrial communication infrastructures are inadequate. Nevertheless, guaranteeing secure uplink communications in satellite-based IoT networks is a daunting task due to similar satellite channels and limited resources at IoT nodes. In this article, we explore the potential of unmanned aerial vehicle (UAV) to improve the secrecy performance of uplink transmissions in satellite-supported IoT networks. Specifically, we first introduce a framework for UAV-aided secure uplink communications, presuming a secure UAV-to-satellite connection. To mitigate the risks of ground eavesdroppers intercepting uplink transmissions, we develop a max–min secrecy rate optimization problem with uplink power constraints. To address this nonconvex problem, a streamlined two-stage optimization approach is proposed. In the inner stage, we combine uplink power allocation and UAV beamforming and propose a successive convex approximation (SCA)-based joint optimization algorithm to address them. In the outer stage, we propose a synergized bisection and coordinate descent algorithm to optimize UAV positioning. Convergence is attained by alternating iterations between these two stages. Particularly, the secrecy fairness among IoT users is reached by solving the max–min problem. Additionally, we offer a complexity analysis of the proposed algorithm and validate the efficacy of the presented approach through comprehensive simulation results.

In vehicular networks (VNets), vehicular federated learning (VFL) is a new learning paradigm that can protect data privacy of vehicle nodes (VNs) while training models. In VFL, the importance of data (IoD) is a key factor that affects model training accuracy. However, due to the heterogeneity of data in the VFL, it is a challenge to evaluate the quality of data owned by different VNs and design an efficient federated learning scheme to enable the VNs to complete learning tasks collaboratively. In this paper, we consider the IoD and propose a redundancy-aware collaborative federated learning (RCFL) scheme for the VFL. In the scheme, by jointly considering the data quality and the cooperation among VNs, we first design a redundancy-aware federated learning architecture to efficiently provide learning services in VNets. Then, we develop a data importance model that integrates the non-independent and identically distributed (non-IID) degree and the redundancy of data (RoD) to evaluate the data quality and formulate the cooperation of the VNs as a coalition game to improve their data importance, where the equilibrium of the coalition game is obtained by designing a coalition formation algorithm. After that, by considering the diversified characteristics of data and the available resources of different VNs in each coalition, a coalition-based federated learning algorithm is designed to enable the distributed coalitions to complete the learning task cooperatively with the target of improving the learning accuracy. The simulation results show that the proposed scheme outperforms the benchmark schemes in terms of the IoD obtained by the VNs and the training accuracy.

Unmanned aerial vehicle (UAV) relay networks with flexible and controllable characteristics are expected to complement the capacity of the gNB. This paper studies the multiplexing of enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) in a multi-UAV relay network, where the strict latency requirement of URLLC can be achieved by the preemptive multiplexing of eMBB resources. However, this may affect eMBB reliability due to the transmission interruptions. Moreover, given the limited energy resources of UAVs, there is an inherent tradeoff among reliability, delay, spectral efficiency, and energy efficiency. To address these challenges, this paper develops a hierarchical UAV-assisted eMBB/URLLC multiplexing scheduling framework. For the eMBB scheduler, we first utilize multiple UAVs to assist the gNB in relaying eMBB traffic and formulate the eMBB resource allocation problem as an optimization problem. Then, we propose a decomposition-relaxation-optimization algorithm to maximize eMBB data rates while considering the personalized fairness of resource allocation and UAV power consumption. For the URLLC scheduler, we further consider the multiplexing of eMBB/URLLC traffic based on the optimization of eMBB resources. To reduce the performance fluctuations of eMBB, we propose a novel cross-slot strategy to schedule URLLC within two time slots rather than one time slot as in existing works. With this strategy, a deep reinforcement learning-based algorithm is proposed to obtain the optimal strategy for the preemption of URLLC on eMBB. Simulation results show that the proposed algorithms outperform the benchmark schemes in terms of convergence rate, eMBB reliability, personalized resource fairness, UAV consumption, and URLLC satisfaction.

In vehicular edge computing networks, the real-time, on-demand scheduling of scarce network resources for environmental perception, task offloading, computation, and feedback is vital. However, these coupled processes make resource allocation challenging. Moreover, existing real-time channel measurement techniques in complex vehicular topologies present load, accuracy, and customization difficulties. To address these issues, this paper proposes an on-demand environmental perception and resource allocation strategy. Specifically, with the introduction of a channel knowledge base, we first analyze the coupling relationship between environmental perception, communication, and computation. A model is then proposed for task offloading to schedule the granularity of environment perception, communication resources, and computational resources dynamically. Subsequently, the resource allocation problem is formulated as an optimization problem, aiming to minimize system processing delay and maximize resource utilization while ensuring perception accuracy. To address this, a two-phase optimization-assisted deep reinforcement learning (DRL) algorithm is proposed. The initial phase uses convex optimization to approximate a solution. The second phase proposes a DRL-based algorithm to intelligently schedule dynamic network resources, with the first phase’s solution guiding the initial exploration space to enhance DRL training efficiency. Extensive simulation experiments verify the effectiveness of our proposal.

Preemptive scheduling efficiently addresses the coexistence of enhanced Mobile Broad Band (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC). While URLLC puncturing influences eMBB performance, further investigation is necessary to study the trade-offs between stability, delay, and efficiency. However, existing studies overlook the imbalance in eMBB/URLLC load distribution and personalized fluctuations in eMBB performance, leading to sub-optimal results. To tackle this, we propose an unmanned aerial vehicle (UAV) relay-assisted eMBB/URLLC multiplexing framework. Specifically, considering the utilization of UAVs for connecting separated next-generation Node Bs (gNBs) and the individual subject experience of services, we first formulate the multiplexing problem as an optimization problem. The objective is to maximize eMBB throughput and minimize personalized fluctuations in eMBB performance and UAV consumption, subject to URLLC constraints. Then, the challenging problem is decomposed into the eMBB problem and the URLLC problem. For the former, we further decompose it into three sub-problems and solve them using optimization methods. For the latter, we propose a deep reinforcement learning-based algorithm to obtain an optimal strategy for relaying and puncturing URLLC into eMBB intelligently. Simulation results demonstrate that our proposals outperform benchmark schemes regarding eMBB throughput, UAV consumption, eMBB performance fluctuation, URLLC satisfaction, and learning efficiency.

With the development of Internet of Things (IoT) and communication technology, abnormal parking detection based on geomagnetic sensors equipped with solar panels has become increasingly feasible and important. False parking detection is a main challenge affecting the widespread use of the aforementioned technology. Especially when ambient light change triggers changes in the current of solar panel, which may in turn cause local EM field changes and affect the detection of nearby geomagnetic sensor. To that end, we propose an abnormal parking detection scheme to distinguish between real parking and false detections. Specifically, we establish an equivalent model of the magnetic field around the solar panel, and design a method to compute the magnetic field at the periphery of the solar panel. Then, we analyze the difference between the magnetic field caused by the changes in ambient light and the magnetic field produced by vehicles, and extract features that distinguish abnormal parking from false detections. Based on the features, a state machine is designed to distinguish the false detection of light from the real parking based on the magnetic field waveform. Field tests show that the accuracy of the designed abnormal parking detection scheme can reach 97 %.

Task offloading is a potential solution for computation-intensive vehicular applications due to limited on-board computing resources. However, traditional model-driven methods are hindered by long online processing time, while data-driven methods are deficient in interpretability and generalizability. To overcome this challenge, this paper formulates the resource allocation for task offloading in connected autonomous vehicles (CAVs) as a multi-objective optimization problem, and proposes a novel knowledge-driven algorithm that integrates both model-driven and data-driven methods. Specifically, the framework of a model-driven alternating minimization (AM) algorithm, which solves the formulated problem via alternatively optimizing power allocation subproblem and bandwidth and CPU frequency allocation subproblem, is regarded as knowledge. Inspired by such knowledge, our proposed knowledge-driven neural network consists of two long short term memory networks (LSTMs) to alternatively updating these two subproblems. Furthermore, to get away from the local optimum usually occurred in the AM algorithm, our proposed knowledge-driven neural network updates network parameters with the global loss function. Simulation results demonstrate that our method outperforms both the AM algorithm and the LSTM without knowledge.

Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalability and high training costs. Although conventional node-output graph neural networks (GNN) can extract features of edge nodes when the network scales, they fail to handle a new scalability issue whereas the dimension of the decision space may change as the network scales. To address the issue, in this paper, a novel link-output GNN (LOGNN)-based resource management approach is proposed to flexibly optimize the resource allocation in MEC for an arbitrary number of edge nodes with extremely low algorithm inference delay. Moreover, a label-free unsupervised method is applied to train the LOGNN efficiently, where the gradient of edge tasks processing delay with respect to the LOGNN parameters is derived explicitly. In addition, a theoretical analysis of the scalability of the node-output GNN and link-output GNN is performed. Simulation results show that the proposed LOGNN can efficiently optimize the MEC resource allocation problem in a scalable way, with an arbitrary number of servers and users. In addition, the proposed unsupervised training method has better convergence performance and speed than supervised learning and reinforcement learning-based training methods. The code is available at https://github.com/UNIC-Lab/LOGNN.

The rapid development of Internet of Things (IoT) technology can significantly promote the development and deployment of smart roads, enabling efficient and reliable road information sensing and analysis. As an important part of smart roads, timely and accurate detection of road cracks can improve service life of roads and reduce road management and operating costs. In this paper, we propose a vibration-sensor-based crack detection scheme for smart roads. In this scheme, by deploying the vibration sensor on the roadside, the changes in the vibration signals caused by the vehicle passing through the range of the sensor can be collected in real time. Then, considering that the seismic waves caused by vehicle driving are mostly distributed in the low-frequency range, we perform low-pass filtering on the collected vibration signals to retain the low-frequency vibration signals. After that, in order to distinguish the crack state of the road, we extract the vibration signal features of the normal road and the cracked road in the time domain, frequency domain and time-frequency domain, respectively. Based on the extracted features, we use logistic regression (LR), support vector machine (SVM) and random forest classification (RFC) machine learning algorithms to realize road crack detection. Finally, we conduct experiments to evaluate the performance of the proposed road crack detection scheme. The experimental results verify the high accuracy of the proposed scheme, and the accuracy of LR, SVM and RFC are 93.3%, 93.3% and 96.7%, respectively.

With the development of communication technology and virtual reality (VR) technology, virtual Metaverse services are gradually entering people’s lives to provide immersive experience. As one of the important travel tools for people, vehicles have the opportunity to become the carrier of Metaverse, thereby enhancing the driving experience and entertainment experience of vehicle users (VUs). However, due to the high-speed movement of vehicles, how to dynamically adapt to environmental changes to allocate transmission and computing resources so that VUs can better experience VR services in the Metaverse has become a challenge. To this end, in this paper, we propose an environment-aware dynamic resource allocation scheme for VR video services in vehicle Metaverse, aiming to efficiently allocate computing and communication resources to maximize the quality of experience (QoE) of VUs when requesting VR video services. Specifically, we first establish the system model which includes network model, communication model, and VR video model. Then, considering the dynamic changes in the driving environment, we design a QoE model for each VU based on its VR video buffer. After that, we design a deep deterministic policy gradient (DDPG) algorithm to optimally allocate communication and computing resources to maximize the QoE of each VU. The simulation results show that our scheme can bring the highest reward to the VUs compared with the benchmark schemes.

In digital twins enabled space-air-ground integrated networks (DT-SAGINs), the DT of a vehicle (DT-V) needs to constantly migrate between the infrastructures deployed on the path of the vehicle as the vehicle moves to provide stable and continuous driving services for the vehicle. However, each DT-V has differentiated migration requirements and the heterogeneous network infrastructures have various migration performances. Therefore, how to design a scheme that jointly considers the above factors to determine the optimal migration strategy for each DT-V becomes a challenge. In this paper, we propose a game-based migration scheme for the DT-Vs in DT-SAGINs. In this scheme, we first design a two-layer DT migration architecture, where each vehicle has two DTs and each network infrastructure only has one DT. The two DTs of the vehicle are respectively deployed in the cloud layer (Primary DT-V) and the edge layer (Second DT-V). In contrast, the DT of each network infrastructure is deployed in the cloud layer (DT-I). Based on the designed architecture, the interaction of the Primary DT-Vs and the DT-Is deployed in the cloud layer is formulated as a matching game, where an integrated algorithm that couples bilateral matching and dynamic programming is designed to obtain the optimal migration strategy for each Second DT-V deployed in the edge layer to maximize its average utility. The simulation results show that the proposed scheme can lead to a higher utility for each Second DT-V than the conventional schemes.

Space-air-ground integrated networks (SAGIN) have been envisioned as the promising and key network architecture for the 6G vehicular networks to provide seamless coverage for the connected vehicles. To access the most appropriate network quickly, this paper proposed a knowledge-driven network access approach, where the communication knowledge is explicitly integrated into neural networks, to deal with multiple tasks in SAGIN-based vehicular networks. Specifically, the formulated long-term network access problem is handled by asynchronous advantage actor-critic algorithm (A3C) in reinforcement learning. During this process, the space-time correlation knowledge is introduced to effectively reduce the action space in channel selection and the reward shaping exploiting the problem-specific communication and mathematical knowledge is adopted to solve the sparse reward problem in reinforcement learning. In addition, by modifying the sub-net learning rate of the A3C algorithm with experimental experience, this paper speeds up the network convergence speed by 1.5%. Numerical results also show that integrating knowledge into traditional deep reinforcement learning algorithm can improve the reward by 4%.

The combination of digital twins (DT) and heterogeneous vehicular networks (HetVNets) can significantly enhance the resource integration capability and performance of the network. In DT-HetVNets, vehicles need to selectively synchronize the data to be updated or cached to their DTs deployed in the cloud for data interaction and decision-making. However, considering that vehicles have diversified data synchronization requirements and network infrastructures have differentiated access capabilities, how to formulate optimal network access strategies and resource pricing strategies for vehicles and network infrastructures becomes a key challenge in the data synchronization process. To this end, we propose a utility-based on-demand data synchronization scheme in DT-HetVNets. In this scheme, we first establish the DT model and communication model in DT-HetVNets. Then, we design the utility functions of the DTs of vehicles and infrastructures by comprehensively considering their requirements. According to the utility functions, we model the decision-making process between the DTs of vehicles and the DTs of infrastructures as a Stackelberg game, where an iterative algorithm is proposed to obtain the Stackelberg equilibrium. The simulation results show that our scheme can bring them the highest utilities compared with the traditional schemes.

The ever-growing Internet of Things (IoT) provides a powerful means for complex and changeable environmental monitoring. Directional sensor networks (DSNs), as a typical architecture of IoT, can efficiently facilitate various digital and intelligent IoT applications. In the DSNs, due to the asymmetry in coverage focus and diversity in detection angle of the directional IoT sensors, how to enhance the coverage performance with the limited sensors becomes a new challenge. To this end, we develop a novel sensor redeployment scheme based on the minimum exposure path (MEP) to optimize the coverage performance of the DSNs. Specifically, we first propose a minimum exposure path searching algorithm based on the particle swarm optimization (MEP-PSO) algorithm with the target of obtaining the MEP in the DSNs. With this algorithm, the traditional MEP problem can be analyzed and simplified by conducting the grid discretization and building the weighted undirected graph. Then, an MEP-based coverage optimization (MEP-CO) algorithm is proposed to determine the optimal deployment locations and the dispatch sensors so that the IoT sensors can be dynamically redeployed to achieve the coverage optimization. After that, we derive the formula for the coverage upper bound (CUB) and develop a CUB algorithm to provide a benchmark for evaluating the effectiveness of different coverage optimization algorithms. Simulation results demonstrate that the proposed coverage optimization scheme can significantly promote the minimum exposure value (MEV) and coverage ratio of the monitoring area compared with the existing algorithms.

With the widespread application of wireless networks, the importance of intelligent analysis of network behaviors is becoming increasingly prominent. In the analysis of networks behaviors, learning and reasoning about the connectivity of unknown networks is a fundamental problem. To obtain the topology information of a noncooperative wireless network that could not be accessed by the monitoring sensors, we propose a topology inference algorithm based on the network two-dimensional spatiotemporal features (TDSTFs). Specifically, the monitoring sensor network monitors the power of the noncooperative network and locates the nodes of the noncooperative network exploiting the neural network (NN)-based method. Then, the communication time and distance between the noncooperative nodes are used as characteristics to infer the topology of the noncooperative network based on$K$-nearest neighbors (KNNs). Simulation results validate that the proposed TDSTF topology inference algorithm outperforms other topology inference algorithms that do not consider both spatial and temporal features and can greatly improve the inference accuracy.

The heterogeneous vehicular networks (HetVNets) can accelerate the deployment of Internet of Vehicles (IoV) and enrich the content distribution methods. However, the diverse requirements of vehicular users (VUs), the limited cache resources of roadside units (RUs), and the frequent interactions between VUs and RUs pose great challenges to efficiently distribute contents. To address these challenges, we propose an on-demand content delivery scheme in digital twin-enabled HetVNets (DT-HetVNets). Specifically, we first design an on-demand content delivery architecture in DT-HetVNets which uses DT communication mode to simplify the frequent interactions between VUs and RUs. With this architecture, by jointly considering the popularity of each content and the relevance between different contents, the personal content requirement of each DT of VU (DT-VU) can be perceived and the VUs within the coverage of the same RU can collaboratively request contents in groups. Then, we formulate the interaction between each group and the DT of the RU (DT-RU) as a double auction game to determine the transaction price of the perceived content, where the request information of the contents which are accepted by the groups can be shared between different DT-RUs based on the path of each group, enabling collaborative content recommendation between the RUs. After that, by jointly considering the contents recommended by different DT-RUs and the content popularity, the content caching model of each DT-RU is formulated as a knapsack problem, where a collaborative content caching algorithm is designed to obtain the optimal caching strategy with the target of making full use of the limited cache resources. Compared with the conventional schemes, the simulation results show that our scheme can not only bring the highest utility to the RUs but also lead to the highest hit ratio and the lowest delay.

The autonomous vehicles (AVs), as intelligent mobile robots, can undertake tasks to facilitate various computation-intensive services in intelligent transportation system (ITS). Due to hardware device failures or environmental identification errors, the AVs controlled by intelligent algorithms may cause accidents during driving. However, the existing studies in the post-accident stage lack the analysis of the impact degree of the accidents and the computing tasks undertaken by the AVs to determine the optimal maintenance strategy. In this article, we consider the accidents in a continuous period of time and design a digital twin (DT)-enabled post-accident maintenance scheme. Specifically, by considering the computing tasks undertaken by the AVs and the impact degree of the accidents, we first design a DT-enabled post-accident maintenance architecture. With the designed architecture, an optimal maintenance method under an incomplete information scenario is then proposed to help each accident AV decide its optimal maintenance strategy. Besides, based on the maintenance strategies of the AVs and the capacities of the maintenance service providers (MSPs), the two-way selection problem between the AVs and the MSPs in the continuous period of time is modeled as a dynamic matching game to obtain the optimal AV-MSP pairs. Simulation results demonstrate that the proposed scheme outperforms the benchmark schemes in terms of the maintenance rate of the accident AVs, the average utility of the MSPs, and the average social welfare.

Due to the wireless broadcasting and broad coverage in satellite-supported Internet of things (IoT) networks, the IoT nodes are susceptible to eavesdropping threats. Considering the distance difference between satellite and nearby destinations is negligible, the main and wiretapping channels between satellite and IoT node are similar, it poses great challenges to reach physical layer security in satellite-assisted IoT networks. In this paper, to guarantee secure transmissions for satellite-assisted IoT downlink communications, the multi-domain resource multiplexing based secure approach is proposed. Particularly, the self-induced co-channel interference between adjacent nodes is leveraged to increase the difference of signal transmission quality over both main and wiretapping channels. By comprehensively optimizing multi-domain resources, i.e., frequency, power, and spatial domains, secure transmissions from satellite to IoT nodes are reached. Specifically, the problem to maximize the sum secrecy rate of IoT nodes is formulated with a constraint of common communication rate of IoT nodes. To solve this non-convex problem, an alternating optimization (AO) algorithm with two inner successive convex approximation (SCA) algorithms are executed to solve the power allocation, spectral multiplexing, and precoding. In addition, simulation results are carried out to evaluate the secrecy rate performance and verify the efficiency of our proposed approach.

A fundamental issue of the vehicular digital twin (DT) is efficiently synchronizing the data between the DT and the vehicular user (VUE). In this paper, we consider the heterogeneous vehicular networks (HetVNets) in which a VUE can connect to the network through different networks. The HetVNets can improve the efficiency of communication by providing seamless connections. However, the uneven distribution of VUEs and the dynamics of HetVNets make the environment more complex. Therefore, we propose the network selection algorithm for data synchronization between VUEs and DTs in the HetVNets, where the behaviour between the VUEs is considered as a competition for wireless resources. A learning-based prediction model residing in the DT is developed where the DT can predict the waiting time of each relay and transmit the predicted results to the VUE for decision-making. We model the network selection problem as a potential game considering both the transmission time and the waiting time obtained from the prediction model and prove the existence of Nash equilibrium (NE). We analyze the performance of the proposed algorithm, and simulation results show that our approach can effectively find the optimal strategy while achieving a fast convergence speed and high-level performance compared to the baselines.

In 5G/B5G networks, the preemptive scheduling provides an efficient solution to the coexistence problem of eMBB/URLLC services. Current works usually assume that the downlink transmission duration of each URLLC service is within one mini-slot, which ignores the different requirements of URLLC users and may lead to the severe data rate loss of eMBB services and low resource utilization efficiency. To deal with above problem, we propose a novel URLLC preemptive strategy, where the arriving URLLC services could cross through multiple mini-slots rather than only one to puncture resources on demand. With the proposed strategy, considering the heterogeneous delay requirements of URLLC services and the preemptive influence on eMBB services, an efficient algorithm based on optimized sparrow search is also proposed. Through allocating time and frequency resources occupied by each URLLC service on de-mand, the number of URLLC services supported by the gNB is maximized while the satisfaction of eMBB services is ensured. The simulation results indicate that the proposed algorithm can achieve better performance compared with the benchmark schemes.

Vehicle platooning is an effective way to improve the efficiency and safety of transportation systems, in which a group of vehicles maintains a moving pattern by minimizing the tracking error of each vehicle. In this paper, a joint optimization of radio resource allocation for kinetic status information transmission and platoon control is considered under resource-constrained conditions to maintain the targeted inter-vehicle spacing. The formulated problem is approximately solved by the decomposition method, where the radio resource allocation and the platoon control are considered alternatively in two stages. In the first stage, a tracking error based scheduling strategy is presented for radio resource allocation. In the second stage, the control inputs of each vehicle are optimized based on the model predictive control (MPC). Simulation results show that the proposed scheme can achieve the objective of platoon control while having a low tracking error compared with other scheduling strategies.

With the advancement of smart cities and the development of heterogeneous vehicular networks (HetVNets), vehicles can collect data and generate valuable information to obtain profits, thus forming a new vehicular self-media paradigm in HetVNets. However, in the HetVNets with potential security risks, the vehicular self-media market lacks the consideration of the values of the data owned by the media data producers (MDPs) and the capabilities of the media data sellers (MDSs) to improve their utilities. To this end, we propose a value-based secure self-media data trading scheme in the HetVNets. Specifically, we first design a vehicular self-media trading mechanism based on smart contracts to provide participants with a safe and reliable transaction environment. Then, we model the interactions between the MDPs and the MDSs as a Stackelberg game by considering the values of various media data and the sales capabilities of different MDPs. After that, we design an iterative method to obtain the optimal game strategies for the MDPs and the MDSs to maximize their utilities. Compared with the traditional schemes, the simulation results show that our scheme can obtain the optimal strategies for the MDPs and the MDSs and bring them the highest utilities.

In the heterogeneous vehicular networks (HetVNets), the base stations (BSs) can exploit the massive amounts of valuable data collected by vehicles to complete federated learning tasks. However, most of the existing studies consider the scenario of one task requester (TR) and ignore the fact that multiple TRs may concurrently generate their model training requests in the HetVNets. In this paper, we consider the scenario of multi-TR and multi-BS and propose a digital twin enabled scheme for multitask federated learning to address the two-way selection problem between the TRs and the BSs. We first analyze the diversified requirements of the TRs in the HetVNets. Then, we develop a novel model that jointly considers the available training data, the declared price, and the training experience to evaluate the differentiated training capabilities of the BSs. After that, based on the requirements of the TRs and the training capabilities of the BSs, the two-way selection problem between the TRs and the BSs is formulated as a matching game in the digital twin networks, where a matching algorithm is designed to obtain their optimal strategies. The simulation results demonstrate that the proposed scheme can obtain the highest model accuracy and bring the highest utility to the TRs compared with the conventional schemes.

The space-air-ground integrated vehicular networks (SAGIVNs) can efficiently accelerate the deployment of the Internet of Vehicles (IoV) and enrich the content distribution methods in the networks. In this paper, we propose a content delivery scheme in SAGIVNs that integrates sensing, communication, and caching. Specifically, we first perceive the content requests of the vehicles through which the vehicles covered by the same roadside unit (RU) can be facilitated to request the contents collaboratively. Then, based on the location and path of each vehicle, the perceived request information can be transmitted between different RUs, enabling efficient collaborative content recommendation between the RUs. After that, by jointly considering the contents recommended by different RUs, the popularity of each content, and the limited cache resources, the content caching model of each RU is formulated as a knapsack problem, where a dynamic programming method is designed to obtain the optimal caching strategy. Compared with the conventional schemes, the simulation results show that the proposed scheme can lead to the highest hit ratio and the lowest transmission delay.

The sixth-generation (6G) wireless communication system is expected to integrate communication, intelligence, sensing, positioning, control, and calculation to adapt to time critical, ultrareliable, and energy-saving data delivery, as well as accurate positioning of personnel and equipment, serving the Internet of Things (IoT). On the one hand, reconfigurable intelligent surface (RIS) can intelligently manipulate radio waves and is considered to be one of the candidate technologies for the 6G wireless communication. Hence, there are more and more surveys on RIS-assisted communications. On the other hand, the potential of RIS in positioning has attracted growing attention, and articles on RIS-assisted positioning have been blown out. Therefore, it is time to review this literature to understand the potential of RIS positioning, research status, and point out the direction for future research. This article first explains the working principle and channel model of RIS and summarizes some characteristics of RIS suitable for positioning. Then, we give a concise review and classification of existing RIS positioning research. Finally, we put forward our views on the future research challenges and attractive directions for RIS-aided wireless positioning technology.

The customization of edge computing services is one of the key research fields in sixth-generation (6G) heterogeneous vehicular networks (HetVNETs). With various personalized requirements of vehicles on computation-intensive applications, how to explore the heterogeneous computing resources in the 6G HetVNETs to guarantee vehicles with the customized Quality of Experience (QoE), therefore, becomes a challenge. In this article, we develop a novel secure scheme to provide personalized edge computing services for moving vehicles (MVs) in 6G HetVNETs. In the scheme, a smart-contract-based secure edge computing architecture is designed by jointly considering the attack models and the characteristics of the 6G network infrastructures (e.g., satellites, drones, base stations, and roadside units), where each network infrastructure manages a number of parking vehicles to complete computing services collaboratively. With this architecture, based on the available computing resources owned by different network infrastructures, the collaborative computing resource allocation algorithm is designed to help each network infrastructure decide a customized service strategy (CSS) to satisfy the QoE of MVs. After deciding the CSSs, a model based on the second price-sealed auction is formulated to describe the competition among the network infrastructures, where the Nash equilibrium of the game is obtained to guide their optimal bidding strategies to obtain the chance for completing the services. The security analysis and the simulation results show that the proposed scheme can defend against the attacks and lead to a lower cost for completing the services than the conventional schemes.

The vehicular crowdsensing, which benefits from edge computing devices (ECDs) distributedly selecting autonomous vehicles (AVs) to complete the sensing tasks and collecting the sensing results, represents a practical and promising solution to facilitate the autonomous vehicular networks (AVNs). With frequent data transaction and rewards distribution in the crowdsensing process, how to design an integrated scheme which guarantees the privacy of AVs and enables the ECDs to earn rewards securely while minimizing the task execution cost (TEC) therefore becomes a challenge. To this end, in this article, we develop a blockchain-based collaborative crowdsensing (BCC) scheme to support secure and efficient vehicular crowdsensing in AVNs. In the BCC, by considering the potential attacks in the crowdsensing process, we first develop a secure crowdsensing environment by designing a blockchain-based transaction architecture to deal with privacy and security issues. With the designed architecture, we then propose a coalition game with a transferable reward to motivate AVs to cooperatively execute the crowdsensing tasks by jointly considering the requirements of the tasks and the available sensing resources of AVs. After that, based on the merge and split rules, a coalition formation algorithm is designed to help each ECD select a group of AVs to form the optimal crowdsensing coalition (OCC) with the target of minimizing the TEC. Finally, we evaluate the TEC of the task and the rewards of the ECDs by comparing the proposed scheme with other schemes. The results show that our scheme can lead to a lower TEC for completing crowdsensing tasks and bring higher rewards to ECDs than the conventional schemes.

Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges between AVs for determining the members in each collaborative group will consume a lot of time and resources. In addition, since AVs have different computing capabilities and costs, the collaboration types of the AVs in each group and the distribution of the AVs in different collaborative groups directly affect the performance of the cooperative driving. Therefore, how to develop an efficient collaborative autonomous driving scheme to minimize the cost for completing the driving process becomes a new challenge. To this end, we regard collaboration as a service and propose a digital twins (DT)-based scheme to facilitate the collaborative and distributed autonomous driving. Specifically, we first design the DT for each AV and develop a DT-enabled architecture to help AVs make the collaborative driving decisions in the virtual networks. With this architecture, an auction game-based collaborative driving mechanism (AG-CDM) is then designed to decide the head DT and the tail DT of each group. After that, by considering the computation cost and the transmission cost of each group, a coalition game-based distributed driving mechanism (CG-DDM) is developed to decide the optimal group distribution for minimizing the driving cost of each DT. Simulation results show that the proposed scheme can converge to a Nash stable collaborative and distributed structure and can minimize the autonomous driving cost of each AV.

With the popularity of image sensors in various mobile devices, image blurring caused by hand shaking or out of focus becomes ubiquitous, which deteriorates image quality and poses challenges for vision tasks, including object detection, image classification and image segmentation. Designing an efficient blur detection algorithm which can automatically detect and locate blurred regions becomes necessary. In this letter, we design an end-to-end convolution neural network called heterogeneous attention nested U-shaped network (HANUN) for blur detection. We introduce pyramid pooling into encoders to enhance the feature extraction at different scales and reduce the gradual information loss. Inspired by the nested network design, small U-shaped networks are embedded into our decoders to increase the network depth and promote feature fusion with different receptive field scales. In addition, we incorporate a channel attention mechanism in the proposed network to highlight the informative features for detecting the blurry regions. Experimental results show that HANUN outperforms other state-of-the-art algorithms for blur detection tasks on public datasets and real-world images.

The vehicle localization, which aims to identify a vehicle and then position the vehicle with a high precision, can be used to facilitate various applications and services in vehicular networks. Unfortunately, conventional localization systems, e.g., global positioning system (GPS), hardly meet the accuracy requirements especially in certain specific scenarios, such as tunnels. At the same time, Ultrahigh frequency (UHF) radio frequency identification (RFID) has become an efficient booster for internet of things (IoT) due to the desirable advantages, such as low cost, battery-free, and unique identification. In this paper, based on the UHF-RFID, we propose a novel real-time vehicle localization scheme in GPS-Less Environments. Considering the practical implementation of multiple RFID reader antennas on a vehicle is constrained, we adopt single antenna multi-frequency ranging scheme, in which the integer ambiguity problem is solved by the maximum-likelihood estimation (MLE)-based robust Chinese remainder theorem (CRT). With the reconstructed distances between the tags and the reader, the coordinates of the vehicle then can be calculated with the Levenberg-Marquardt (LM) algorithm. Furthermore, the computational complexities of the algorithms and the time consumption of the proposed scheme are analyzed. The experimental results demonstrate that the proposed scheme can track vehicle’s location with error lower than 27 cm at the probability of 90%.

Internet of Things (IoT) is playing an increasingly important role in Intelligent Transportation Systems (ITS) for real-time sensing and communication. In ITS, vehicle types, volume and speeds provide important information for road traffic management. However, the present methods for on-road traffic monitoring are lacking in providing cost-effective means to meet the demands. In this paper, we propose MagMonitor, a novel method for on-road traffic surveillance through a single small and easy-to-install magnetic sensor. The developed magnetic sensor system is wireless-connected, cost-effective, and environmental-friendly. First, a magnetic model of a moving vehicle is presented. The model employs multiple magnetic dipoles for modelling moving vehicle and varies depending on the on-road vehicle types. Through modelling of local magnetic field perturbations caused by moving vehicles, we extract the characteristics of magnetic waveforms for vehicle identification and speed estimation. The proposed model and estimation technique are validated with real field experimental data. Furthermore, we analyze and compare the performance of the proposed estimation technique with other speed estimation algorithms, which shows the superior accuracy of the proposed technique.

The autonomous vehicles (AVs) in smart city, as intelligent mobile robots, are expected to provide diversified services to facilitate the life of citizens. However, the attributes of the services requested by users are different and the statuses of the AVs managed by different central servers are dynamically changed. To execute the services with the minimum cost based on the requirements of users and the statuses of AVs therefore becomes a challenge. In this article, we establish an intelligent multi-attribute service response framework in smart city based on the request of users and the response of AVs. In the first phase of the framework, each central server decides the minimum service execution cost (SEC) to respond to the user’s service by considering the available resources of its AVs, where the minimization problems are formulated for the services with one attribute and the services with multiple attributes, respectively. To address the problems, the optimal AV selection (OAVS) algorithm for the services with one attribute and the OAVS-M algorithm for the services with multiple attributes are designed. In the second phase, based on the SEC of each central server, an auction game is developed to model the competition among the central servers to help the user select the optimal one to execute the service with the lowest service transaction price (STP). By achieving the Nash equilibrium of the game, the optimal strategy of each central server to win the chance for executing the service is obtained. The simulation results show that the designed framework can reduce the STP compared with the conventional schemes.

The rapid growth of connected and autonomous vehicles (CAVs) shows an urgent demand for driving and transportation-related data, which gives rise to vehicular crowdsensing systems (VCSs). Nevertheless, the existing centralized VCS framework mainly faces the system reliability problem while the decentralized one cannot satisfy the management flexibility. In addition, when the privacy preservation scheme that prevents information leakage encounters the user selection scheme that desires detailed information of participants, how to balance this seemingly irreconcilable contradiction is inevitable for VCS. To remedy that, we take the first research attempt and explore the balance point between the system management, privacy preservation, and quality of experience (QoE) of participants. By fully exploiting the characters of participating entities, a blockchain-enabled conditional decentralized VCS is proposed in this paper. Firstly, we propose a privacy-preserving scheme where the zk-SNARK proof combines with the mixed-task smart contract to guarantee the interaction process will not reveal any private information of participants. Secondly, we propose an efficient reputation management mechanism that renders certain the participants can get a satisfactory QoE even under the condition that the private information of users is secured. And also, the malicious operations in the system will be effectively supervised. Theoretical analysis and extensive simulations demonstrate the security and efficiency properties of privacy preservation and indicate the effectiveness of reputation management.

In this paper, we investigate secure transmissions in integrated satellite-terrestrial communications and the green interference based symbiotic security scheme is proposed. Particularly, the co-channel interference induced by the spectrum sharing between satellite and terrestrial networks and the inter-beam interference due to frequency reuse among satellite multi-beam serve as the green interference to assist the symbiotic secure transmission, where the secure transmissions of both satellite and terrestrial links are guaranteed simultaneously. Specifically, to realize the symbiotic security, we formulate a problem to maximize the sum secrecy rate of satellite users by cooperatively beamforming optimizing and a constraint of secrecy rate of each terrestrial user is guaranteed. Since the formulated problem is non-convex and intractable, the Taylor expansion and semi-definite relaxation (SDR) are adopted to further reformulate this problem, and the successive convex approximation (SCA) algorithm is designed to solve it. Finally, the tightness of the relaxation is proved. In addition, numerical results verify the efficiency of our proposed approach.

Efficient path planning is the key enabling technology for the realization of intelligent transportation systems (ITS). However, due to poor real-time performance and lack of effective incentive methods, it is difficult for traditional path planning schemes to significantly improve the efficiency of traffic management. In addition, existing solutions that use driving distance and driving time as indicators cannot meet the personalized requirements of vehicle users. To this end, by considering the personalized requirements of vehicle users, we propose a digital-twin (DT) enabled path planning scheme to facilitate traffic management. To be specific, based on the collection of traffic data, we first establish a DT architecture for traffic scheduling to reduce the delay of path planning. Then, according to the traffic density of different road sections, we regard road sections as resources and set different rewards for different road sections to encourage vehicles to obey the scheduling instructions. In addition, by jointly considering the driving time and rewards, we further design personalized utility models to map the requirements of different vehicle users. After that, based on the personalized requirement of the vehicle user, we use a$Q$-learning algorithm to obtain the optimal path with the target of maximizing the user's utility. The simulation results show that the proposed scheme can bring higher utility to the vehicle users than the conventional schemes.

Incident scene text detection, as the most crucial step of an incident scene text recognition system, has received increasing research attention. In this paper, a two-layer mobile federated learning model (TMFL) is proposed to protect data privacy and improve training efficiency. Particularly, a fast scene text detector is proposed to detect the multi-directional and multi-scale text by using an asymmetric convolution based feature pyramid network (AC-FPN). Compared with the traditional feature pyramid, asymmetric convolutions can effectively extract rotation-invariant features to improve the model's robustness to directed text. Moreover, in order to achieve a balance between the detection accuracy and efficiency, we modify the lightweight backbone of mobilenetv3, and integrate it with the asymmetric convolution based feature pyramid. In addition, we evaluate the performance of our detector on three benchmark datasets, where the results show that both the accuracy and the speed can be improved. Our detector can achieve an F-measure of 87.8 on the ICDAR2013, 80.5 on the MSRA-TD500 and 84.1 on the ICDAR2015 dataset, running at 32.5 FPS.

In smart cities, on-street parking space prediction is the key yet difficult point in smart parking system. However, conventional prediction methods generally neglect spatial and temporal dependencies and cannot predict long-term parking events accurately. To this end, we propose a parking space prediction scheme based on the spatial-temporal graph convolution networks (STGCN). We first consider the instantaneous status of the parking to calculate the on-street parking occupancy rate (POR). Then, based on the POR, we exploit a time convolution module and a graph convolution module to extract spatial and temporal dependencies of the parking spaces, respectively. Next, we design the parameters of the STGCN to predict the POR of all the parking spaces based on the spatial and temporal dependencies. Finally, based on the real-world data sets, we compare the proposed scheme with the benchmark models. The experimental results show that the proposed scheme has the best performance in predicting the POR.

Recently, the abnormal intrusion detection has become an urgent problem in high-speed railway system. One way to solve this problem is the optical fiber distributed acoustic sensing (DAS) system that can monitor the intrusion events and provide early warning. However, most long-distance DAS systems are unable to distinguish signal types to improve the detection performance. Moreover, the traditional fiber optic sensing system is susceptible to interference from environmental factors, resulting in false detections and alarms. To this end, with the adoption of DAS system, we propose a railway intrusion detection system based on Faster R-CNN. In our system, we first design the DAS system to collect the optical fiber acoustic signals. Then, the collected signals are normalized in temporal and spatial dimensions and converted into Spatio-temporal images. After that, we design the Faster R-CNN algorithm to extract the Spatio-temporal features to detect and classify five types of abnormal intrusion events. The experimental results demonstrate that the average detection precision of our system for all abnormal intrusion events is above 89%. In addition, compared with the conventional methods, our system achieves the highest detection precision. Meanwhile, the system can distinguish the non-threatening background noise, which is of great help to reduce the system false positive rate.

The integration of the massive multiple-input multiple-output (MIMO) and millimeter-wave (mmWave) communication can increase the throughput of 5G networks. As an attractive technique in the MIMO systems, hybrid beamforming (HBF) can improve the 5G capacity by employing spatial domain resources. However, with the increase of the number of antennas, the traditional beamforming algorithms fail to efficiently keep a balance between the hardware complexity and beamforming gains. In this paper, with the aid of bidirectional location information, a bidirectional positioning assisted HBF (BPA-HBF) scheme is proposed. Specifically, we first propose a new scheme to decouple the optimal problem of traditional HBF as two phases. In the analog beamforming (ABF) phase, the dominated path among the multi-path components is determined by the transmitter and receiver. In addition, the codebook-based beamforming weight vectors are bidirectionally and synchronously determined according to the angle parameters of the dominated path. In the second phase, based on the ABF matrices, the digital beamformers are designed to maximize the energy efficiency. Simulation results indicate that the proposed BPA-HBF scheme can lead to a lower convergence time and complexity than the conventional schemes. In addition, the results show that the algorithm convergence time can be significantly reduced by increasing the positioning precision.

Transforming our roads into smart roads is an indispensable step towards future self-driving systems, and therefore has drawn increasing attention from both academia and industry. To this end, this paper develops a novel cost-effective IoT-based target detection system utilizing the multi-sensor data fusion technology with a particular focus on pedestrian detection, as an important component of smart road system. Particularly, the developed intelligent pedestrian detection module (${i}$PDM) consists of three major sensors, i.e., Doppler microwave radar sensor, passive infrared (PIR), and geomagnetic sensor. A multi-sensor data fusion algorithm is developed to fuse the sensor data and achieves reliable target detection. After that, ${i}$PDM sends the relevant warning signal wirelessly to nearby base station and vehicles. Experiments are conducted on real traffic environment to evaluate the performance of ${i}$PDM. The results validate the high reliability of ${i}$PDM with an average 91.7% detection accuracy. Moreover, to our best knowledge, ${i}$PDM is the first IoT-based implementation for pedestrian detection of smart roads. It is necessary to highlight that ${i}$PDM is a low-cost, low-power, wide-coverage pedestrian detection system where the cost of a single ${i}$PDM is only US $ 30, which makes it suitable to large-scale deployment.

Smart roads can achieve a comprehensive, real-time and accurate perception of road environment, which is of great significance for intelligent transportation systems (ITS). However, due to massive data needed to be computed, cloud computing usually imposes pressure on backhaul and produces high delay. In this context, mobile edge computing (MEC) provides a promising solution. Meanwhile, current researches of the task offloading based on MEC lack global considerations and ignore IoT devices along the roadside, so optimization on three-side is very necessary and worth researching. To this end, we consider a scenario of smart roads including vehicular terminals (VTs), IoT devices and MEC servers. And we formulate an optimization problem aiming at minimizing a weighted sum of the costs of energy consumption and time delay for users side and cost for MEC servers. On this basis, we propose a three-side dynamic joint task offloading and resource allocation (TDJORA) scheme. Moreover, considering that the optimization problem is a multi-objective optimization problem, we utilize a combination of the particle swarm optimization (PSO) algorithm and Pareto optimality to obtain the optimal solution. Simulation results show that our proposed TDJORA can realize reasonable task offloading and optimal resource allocation for three sides.

Driven by the ever-increasing demands of vehicular services, edge computing has become a promising paradigm to facilitate edge services in vehicular networks by using edge computing devices (ECDs). To enhance the service experience, we develop a reservation service framework, where the reservation service request of a vehicle needs to be relayed to one of the ECDs which is ahead of its driving direction. However, due to the various behaviors of vehicles, not all the vehicles are trustworthy and willing to join in the service request relay process. Therefore, how to exploit the cooperation between ECDs and vehicles to relay the service request by considering the dynamic traffic status and the behaviors of vehicles becomes a challenge. As an effort to address this problem, we propose a trusted relay selection scheme for edge services to facilitate the proposed reservation service framework. Specifically, we first design the request relay mechanism based on the dynamic traffic status to guarantee the efficiency of the relay process. Then, the reputation management mechanism is presented to constrain the behaviors of vehicles, where a vehicle with high reputation value can enjoy the price discount for computing service. Based on the designed request relay and reputation management mechanisms, a reputation-based auction approach is then proposed to select relay vehicles (RVs) to reduce the cost of the relay service. Simulation results show that the proposed reservation service framework can manage vehicles efficiently and lead to the lowest cost for the relay services compared with the conventional schemes.

The software defined heterogeneous vehicular networks (SD-HetVNETs), which consist of cellular base stations (CBSs) and roadside units (RSUs), have emerged as a promising solution to address the fundamental problems imposed by the surge increase of vehicular content demand. However, due to the ever increasing requirement of the vehicles' quality of experience (QoE) and the network vendors' utilities, there come new challenges to motivate CBS to cooperate with RSU for content delivery in order to maximize their utilities and improve the efficiency of the networks. Therefore, in this paper, we propose a collaborative content delivery scheme to improve the utilities of the participants (i.e., CBS, RSU and vehicles) in the SD-HeVNETs, where the CBS can cooperate with RSUs by serving a group of vehicles with multicast technology. We first define the utility models to map the profits of the participants in the networks and formulate the utilities of CBS and RSU as two optimization problems. Then, we exploit the double auction game to motivate CBS to cooperate with RSU for the multicast assisted content delivery to address the two maximization problems. Next, the optimal bidding strategies of CBS and RSU in the game are analyzed when the Bayesian Nash equilibrium is achieved. With the optimal bidding strategies, both CBS and RSU can bid for the multicast assisted content delivery services to maximize their utilities based on the network status. Finally, the performance of the proposed cooperative scheme is evaluated by using simulations. The simulation results demonstrate that the utilities of all the participants in the networks can be enhanced and the efficiency of the networks can be improved.

Content dissemination, in particular, small-volume localized content dissemination, represents a killer application in vehicular networks, such as advertising distribution and road traffic alerts. The dissemination of contents in vehicular networks typically relies on the roadside infrastructure and moving vehicles to relay and propagate contents. Due to instinct challenges posed by the features of vehicles (mobility, selfishness, and routes) and limited communication ability of infrastructures, to efficiently motivate vehicles to join in the content dissemination process and appropriately select the relay vehicles to satisfy different transmission requirements is a challenging task. This paper develops a novel edge-computing-based content dissemination framework to address the issue, composed of two phases. In the first phase, the contents are uploaded to an edge computing device (ECD), which is an edge caching and communication infrastructure deployed by the content provider. By jointly considering the selfishness and the transmission capability of vehicles, a two-stage relay selection algorithm is designed to help the ECD selectively deliver the content through vehicle-to-infrastructure (V2I) communications to satisfy its requirements. In the second phase, the vehicles selected by the ECD relay the content to the vehicles that are interested in the content during the trip to destinations via vehicle-to-vehicle (V2V) communications, where the efficiency of content delivery is analyzed according to the probability that vehicles encounter on the path. Using extensive simulations, we show that our framework disseminates contents to vehicles more efficiently and brings more payoffs to the content provider than the conventional methods.

The heterogeneous vehicular networks (HetVNETs), which apply the heterogeneous access technologies (e.g., cellular networks and WiFi) complementarily to provide seamless and ubiquitous connections to vehicles, have emerged as a promising and practical paradigm to enable vehicular service applications on the road. However, with different costs in terms of latency time and price, how to optimize the connection along the vehicle's trip toward the lowest cost represents fundamental challenges. This paper investigates the issue by proposing an optimal access control scheme for vehicles in HetVNETs. In specific, with different access networks, we first model the cost of each vehicle to download the requested content by jointly considering the vehicle's requirements of the requested content and the features of the available access networks, including conventional vehicle to vehicle communication and the heterogeneous access technologies. A coalition formation game is then introduced to formulate the cooperation among vehicles based on their different interests (contents cached in vehicles) and requests (contents to be downloaded). After forming the coalitions, vehicles in the same coalition can download their requested contents cooperatively by selecting the optimal access network to achieve the minimum costs. The simulation results demonstrate that the proposed game approach can lead to the optimal strategy for the vehicle.

The autonomous vehicles (AVs), like that in knight rider, were completely a scientific fiction just a few years ago, but are now already practical with real-world commercial deployments. A salient challenge of AVs, however, is the intensive computing tasks to carry out on board for the real-time traffic detection and driving decision making; this imposes heavy load to AVs due to the limited computing power. To explore more computing power and enable scalable autonomous driving, in this paper, we propose a collaborative task computing scheme for AVs, in which the AVs in proximity dynamically share idle computing power among each other. This, however, raises another fundamental problem on how to incentivize AVs to contribute their computing power and how to fully utilize the pool of group computing power in an optimal way. This paper studies the problem by modeling the issue as a market-based optimal computing resource allocation problem. In specific, we develop a software-defined network (SDN) architecture and consider a star topology where a centered AV outsources its computing tasks to the surrounding AVs for its autonomous driving. A market mechanism is developed in which the surrounding AVs sell their computing power at a cost based on their local idle computing resources. Then, we classify the tasks requested by the centered AV into two types which are task with time to live (TTL) and task without TTL, respectively. With different task types, we define corresponding cost models of the centered AV and formulate them as two minimization problems. The optimal solutions of the problems are achieved to guide the centered AV to wisely allocate computing tasks to surrounding AVs towards minimal cost. Finally, the performance of the proposed scheme is evaluated using simulations, which show that the proposed scheme can result in the guaranteed computing performance yet the lowest costs compared with other conventional schemes.

Heterogeneous vehicular networks (HetVNETs), which applies the heterogenous access technologies (e.g., cellular and WiFi) complementarily to provide seamless and ubiquitous connections to vehicles, have emerged as a promising and more practical paradigm to enable vehicular service applications on the road. However, with different access technologies presenting different costs in terms of download latency and bandwidth cost, how to optimize the connection along the vehicle's trip towards the lowest cost represents fundamental challenges. This paper investigates the issue by proposing an optimal access control scheme for vehicles in HetVNETs. In specific, with different access links, we first model the cost of each vehicle to download its content by jointly considering the conventional vehicle to vehicle (V2V) communication and the available access links. A coalition formation game is then introduced to formulate the cooperation among vehicles based on different interests (content cached in vehicles) and requests (content needs to download). After forming the coalition, vehicles in the same coalition can download their requested content by selecting the optimal access link to achieve the minimum cost. Simulation results demonstrate that the proposed game approach can lead to the optimal strategy for vehicles and reduce the cost.

As an emerging solution to mitigate the problems of the shortage of power resources, electric vehicles (EVs) have advocated to provide safety and convenient driving recently. However, with the ever increasing number of EVs and the new demand of services, how to optimally schedule the charging services becomes a challenge. Therefore, in this paper we present a novel pricing mechanism to optimally schedule the charging demands with user utilities. Firstly, a framework with a nonpreemptive priority charging service is shown for users to queue up. Secondly, based on queuing theory, a novel pricing mechanism is designed to balance the load of charging station by considering the characteristics of different regions and the status of queue. Thirdly, the user utility is studied according to the distance, waiting time as well as the expense, in order to improve the user utility. Finally, simulation results show that the proposed scheme can optimally distribute the charging demand and improve the user utility more efficiently than other conventional methods.

Content dissemination, in particular small-volume popular content dissemination, represents a killer application of vehicular networks, which is also fundamental to the delivery of advanced infotainment applications, such as vehicular social networks, road traffic alerts, etc. The content dissemination in vehicular networks relies on moving vehicles to relay and propagate contents. Due to challenges including diverse mobilities of vehicles, strict timeliness and limited vehicular communication bandwidth, to appropriately select the relay vehicles towards the optimal system performance is a challenging task. This paper investigates the issue by devising a novel content dissemination scheme composed of two phases. In the first phase, contents are uploaded to a road-side cache infrastructure called roadside buffer (RSB). By examing the transmission capability of vehicles, the RSB then selectively disseminates content files to drive-thru vehicles with an optimal relay selection scheme. In the second phase, the vehicles selected by the RSB relay the content to other vehicles which have interest in the content during the trip to destinations. Using extensive simulations, we show that our scheme disseminates content to vehicles more efficiently than the conventional method.

Mobile charging stations (MCSs) can provide electric vehicles (EVs) with better charging services than the fixed charging stations, as the flexible and efficient charging sites can be available. However, how to schedule the tasks from the EVs and optimally place the MCSs becomes a new challenge. Therefore, in this paper we present a novel approach to help EVs' charging with MCSs through heterogeneous networks. Firstly, a novel heterogeneous network model is presented to improve the communication between EVs and MCSs by using macro cells and small cells. Next, a novel model is developed to make optimal decisions for MCSs to schedule the tasks from EVs. Then, a chaotic evolution particle swarm optimization (CEPSO) algorithm is presented to determine the optimal placement of MCSs based on the charging demand and the maintenance cost. Finally, the simulation experiments prove that the proposed approach can outperform the conventional methods.