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Automotive-Industry 5.0 will use emerging 6G communications to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other intelligent transportation system entities. Nonorthogonal multiple access (NOMA) and backscatter communications are two key techniques of 6G communications for enhanced spectrum and energy efficiency. In this article, we provide an introduction to green transportation and also discuss the advantages of using backscatter communications and NOMA in Automotive Industry 5.0. We also briefly review the recent work in the area of NOMA empowered backscatter communications. We discuss different use cases of backscatter communications in NOMA-enabled 6G vehicular networks. We also propose a multicell optimization framework to maximize the energy efficiency of the backscatter-enabled NOMA vehicular network. In particular, we jointly optimize the transmit power of the roadside unit and the reflection coefficient of the backscatter device in each cell, where several practical constraints are also taken into account. The problem of energy efficiency is formulated as nonconvex, which is hard to solve directly. Thus, first, we adopt the Dinkelbach method to transform the objective function into a subtractive one, then we decouple the problem into two subproblems. Second, we employ dual theory and KKT conditions to obtain efficient solutions. Finally, we highlight some open issues and future research opportunities related to NOMA-enabled backscatter communications in 6G vehicular networks. [ABSTRACT FROM AUTHOR]

Some specific services for Internet of Things, such as real-time map and providing local weather information, depend strongly on geographical time and location. We refer to the data for such service as spatio-temporal data (STD). When STD is used in a query response system similar to conventional Internet services, users not only need to acquire data actively as required, they must also have functions for retrieving data available STD. Therefore, we propose an STD retention system that uses vehicles as information hubs (InfoHubs) for disseminating and retaining the data in a specific area. In our system, InfoHubs diffuse, maintain, and advertise STD over places and times where the STD are strongly dependent, thereby allowing users to receive such data passively within the specific area. Additionally, because STD are associated with a particular space, the system can reduce search costs. We also propose an adaptive transmission control method that each vehicle effectively operates its wireless resources autonomously and STD are retained and distributed efficiently. Finally, we evaluated our proposed method using simulations and clarified that our proposed system is capable of achieving a coverage rate of nearly 100 percent for STD while reducing the number of data transmissions compared to existing systems. [ABSTRACT FROM AUTHOR]

As vehicle complexity and road congestion increase, combined with the emergence of electric vehicles, the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential. The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks. Particularly, 5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities. Machine Learning (ML), a powerful methodology for adaptive and predictive system development, has emerged in both vehicular and conventional wireless networks. Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions, such as traditional control loop design and optimization techniques. This article provides a short survey of ML applications in vehicular networks from the networking aspect. Research topics covered in this article include network control containing handover management and routing decision making, resource management, and energy efficiency in vehicular networks. The findings of this paper suggest more attention should be paid to network forming/deforming decision making. ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies, such as mobile edge computing for real-world deployment. Research datasets, simulation environment standardization, and method interpretability also require more research attention.

The efficient deployment of Roadside Units (RSUs) in an infrastructure based on IEEE 802.11p is essential for delivering Internet-based services to vehicles. In this paper, we introduce novel strategies that, in contrast to prior works, exclusively rely on the average vehicular density within specific urban areas, and these strategies depend on a performance model of IEEE 802.11p for guidance and decision-making regarding RSU placement. This minimal upfront information contributes to the practicality and ease of implementation of our strategies. We apply our strategies to three real-world urban scenarios, utilizing the ns-3 and sumo simulators for validation. This study contributes to three fundamental aspects. First, we establish that any efficient deployment of RSUs is closely linked to the unique characteristics of the city under consideration such as the street layout and spatial density of vehicles. In other words, the characteristics of an efficient RSU deployment are unique to each city. Second, we show that the optimal strategy is not to place the RSUs at the locations with the highest traffic density. Instead, with the help of an analytical performance model of IEEE 802.11, we propose a more efficient strategy wherein the location of each RSU is determined to maximize the number of vehicles receiving the target QoS. This can lead to a significant drop in the number of RSUs required to equip a city. Finally, we demonstrate that, by preventing the use of the lowest transmission rate of IEEE 802.11p at each RSU, a collective benefit can be achieved, even though each RSU experiences a shorter radio range. [ABSTRACT FROM AUTHOR]

The evolution of technology has raised concerns regarding cybersecurity for intelligent connected vehicles (ICVs). In-vehicle network in ICVs lacks robust protection mechanisms, making it vulnerable to cyber threats. In response, intrusion detection systems (IDSs) for ICVs have been developed to protect vehicles from malicious cyber attacks. However, current IDS methods solely rely on independent features, limiting their learning capabilities and increasing the number of false detections. Moreover, many IDSs require the knowledge of mapping between network messages and contents, which restricts their application. To address these limitations, we propose the Multi-order Feature Interaction-aware Intrusion Detection (MIFI) scheme for ICVs. Feature attention cross network is designed to address higher-order feature interactions, while factorization machine is used for second-order interactions. Then a discriminator is utilized to detect the attacks. MIFI expands the feature space through features interaction, thereby enhancing its ability to detect attacks. Moreover, it perceives the relationships of vehicle messages, facilitating intrusion detection without knowing the corresponding rules of vehicle messages. The performance of the proposed method is evaluated on two real-vehicle datasets, affirming its effectiveness and robustness. MIFI achieves an accuracy of over 99% in detecting different attacks. The proposed method can improve the accuracy of traditional IDS to a maximum of 99.99%, and increase the highest F1-score to 97.18%, demonstrating the model's ability of achieving multi-order feature interactions. Ultimately, MIFI is suitable for intrusion detection in different types of ICV networks, significantly contributing to the cybersecurity of ICVs. • We reveal a robust similarity between different real-vehicle attacks data. • Feature attention cross network explores interactions between different features. • No need for prior knowledge of vehicle message rules for intrusion detection. • Proposed scheme detects intrusion with 99% accuracy in real vehicle data. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks consist of self-organizing nodes using multi-hop wireless links for communication without any infrastructure support. Traditionally, ad hoc routing protocols use the minimum hop count for their routing metric since a smaller number of transmissions is typically equivalent to a higher throughput, lower delay, and minimal power consumption. However, with the muti-rate capability of emerging radio interfaces, e.g., 802.11ax/be standards, the min-hop metric no longer results in high throughput. For instance, if the higher data rate links are selected for the route, it could result in a higher throughput even if the route takes more hop counts. In this paper, we propose a high throughput routing scheme, called MARV, which makes two key contributions. MARV searches for high throughput paths using an on-demand route searching algorithm so that the routing overhead is smaller compared to other multi-rate-aware routing schemes. MARV also searches for multiple paths to maintain both min-hop and high-throughput paths to select the adequate path depending on the data packet size. We conduct simulations to demonstrate that MARV outperforms not only min-hop path metrics but also previously proposed high-throughput metrics.

In this paper, we optimize the secrecy capacity of the legitimate user under resource allocation and security constraints for a multi-antenna environment for the simultaneous transmission of wireless information and power in a dynamic downlink scenario. We study the relationship between secrecy capacity and harvested energy in a power-splitting configuration for a nonlinear energy-harvesting model under co-located conditions. The capacity maximization problem is formulated for the vehicle-to-vehicle communication scenario. The formulated problem is non-convex NP-hard, so we reformulate it into a convex form using a divide-and-conquer approach. We obtain the optimal transmit power matrix and power-splitting ratio values that guarantee positive values of the secrecy capacity. We analyze different vehicle-to-vehicle communication settings to validate the differentiation of the proposed algorithm in maintaining both reliability and security. We also substantiate the effectiveness of the proposed approach by analyzing the trade-offs between secrecy capacity and harvested energy.

The drone-small-cell-assisted air-ground integrated network is a promising architecture for enabling diverse vehicle applications. This paper presents a joint resource slicing and vehicle association framework for drone-assisted vehicular networks, which facilitates spectrum sharing among heterogeneous base stations (BSs) and achieves dynamic resource provisioning in the presence of network load dynamics. We formulate the network utility maximization problem as mixed-integer nonlinear programming, considering traffic statistics, quality-of-service (QoS) constraints, varying vehicle locations, load conditions in each cell, and interdrone interference. The original maximization problem is transformed into a biconcave optimization problem to ensure mathematical tractability. An alternate concave search algorithm is then designed to iteratively solve vehicle association patterns and spectrum partitioning among heterogeneous BSs until convergence. Simulation results show that the proposed scheme achieves a significant performance improvement in throughput and spectrum utilization compared with two other baseline schemes.

This research proposes a unique platform for lane change assistance for generating data-driven lane change (LC) decisions in vehicular networks. The goal is to reduce the frequency of emergency braking, the rate of vehicle collisions, and the amount of time spent in risky lanes. In order to analyze and mine the massive amounts of data, our platform uses effective Machine Learning (ML) techniques to forecast collisions and advise the driver to safely change lanes. From the unprocessed large data generated by the car sensors, kinematic information is retrieved, cleaned, and evaluated. Machine learning algorithms analyze this kinematic data and provide an action: either stay in lane or change lanes to the left or right. The model is trained using the ML techniques K-Nearest Neighbor, Artificial Neural Network, and Deep Reinforcement Learning based on a set of training data and focus on predicting driver actions. The proposed solution is validated via extensive simulations using a microscopic car-following mobility model, coupled with an accurate mathematical modelling. Performance analysis show that KNN yields up to best performance parameters. Finally, we draw conclusions for road safety stakeholders to adopt the safer technique to lane change maneuver.

Proactively caching content at the network edge is particularly effective in high-mobility vehicular networks, where intermittent connection is the major challenge for seamless content transmission. The objective of this paper is to achieve proactive caching in vehicular networks by mobility prediction, specifically by predicting the next roadside unit (RSU) for a vehicle with reinforcement learning techniques. The paper proposes two proactive caching algorithms based on multi-armed bandit (MAB) learning, non-contextual MAB and contextual MAB, respectively. This paper fills the void in the literature regarding the application of MAB learning to mobility-prediction based proactive caching. Their feasibility, superiority, and applicability are evaluated with simulation in two modern cities: Las Vegas, USA with a grid road layout, and Manchester, UK with a more historical layout. Additionally, this paper is the first that proposes to investigate the uncertainty associated with proactive caching systems in the form of entropy with a specifically extended Subjective Logic framework, in order to provide an insight into the underlying link between prediction accuracy and uncertainty.

As an indispensable part of intelligent transportation system, a traffic-sensitive navigation system can assist drivers in avoiding traffic congestion by providing navigation services. The navigation service provider (NSP) utilizes road condition information from nearby vehicles collected by roadside units (RSUs) to guide a vehicle through an optimal route provided by the navigation services. This paper mainly focuses on tackling three key issues in such navigation system. Firstly, it is essential to ensure the privacy of the vehicles’ personal identifiable information (PII). Secondly, the location, start point destination and route of the vehicle should not be leaked to RSUs and also should not be linked to its PII by RSUs and NSP. Thirdly, the process of vehicles obtaining the navigation services should be lightweight. Because of such problems, this paper proposes a dual privacy-preserving lightweight navigation system for vehicular networks through designing a novel signature scheme and combining other cryptographic primitives to keep dual privacy including PII and route related information. And the correctness analysis of the proposed system, security proof of the designed signature scheme adopted in the proposed system, and privacy analysis of the system are thoroughly provided. In addition, the performance of the proposed system is evaluated and compared with the existing systems to illustrate that the proposed system is efficient.

The concept of the “connected car” offers the potential for safer, more enjoyable and more efficient driving and eventually autonomous driving. However, in urban Vehicular Networks (VNs), the high mobility of vehicles along roads poses major challenges to the routing protocols needed for a reliable and flexible vehicular communications system. Thus, urban VNs rely on static Road-Side-Units (RSUs) to forward data and to extend coverage across the network. In this paper, we first propose a new Q-learning-based routing algorithm, namely Infrastructure-aided Traffic-Aware Routing (I-TAR), which leverages the static wired RSU infrastructure for packet forwarding. Then, we focus on the multi-source, multi-destination problem and the effect this imposes on node availability, as nodes also participate in other communications paths. This motivates our new hybrid approach, namely Hybrid Infrastructure-aided Traffic Aware Routing (HI-TAR) that aims to select the best Vehicle-to-Vehicle/Infrastructure (V2V/I) route. Our findings demonstrate that I-TAR can achieve up to 19% higher average packet-delivery-ratio (APDR) compared to the state-of-the-art. Under a more realistic scenario, where node availability is considered, a decline of up to 51% in APDR performance is observed, whereas the proposed HI-TAR in turn can increase the APDR performance by up to 50% compared to both I-TAR and the state-of-the-art. Finally, when multiple source-destination vehicle pairs are considered, all the schemes that model and consider node availability, i.e. limited-availability, achieve from 72.2% to 82.3% lower APDR, when compared to those that do not, i.e. assuming full-availability. However, HI-TAR still provides 34.6% better APDR performance than I-TAR, and ~40% more than the state-of-the-art.

In this survey, we analyze the proposals of vehicular communication systems in the context of road traffic management. Starting with the definition of communications between vehicles (V2V), vehicles-to-infrastructure (V2I) and vehicles-to-everything (V2X), we first focus on the requirements and current standards for the Intelligent Transport Systems (ITS), including the maximum communication delay, the communication range and the size of messages (in the case of V2I transmission). After that, we analyze the use cases in line with the implementation of intelligent traffic management and review the respective methods that support or directly manage traffic on roads. One of the primary objectives of this paper is to highlight the architectures of four classes of systems able to support vehicular traffic management and communication between vehicles and roadside infrastructure, namely: vehicular cloud computing (VCC), cloudlets, Mobile Edge Computing (MEC) and fog computing. In this context, we also present our classification of the methods for these four classes of architectures. In the end, we provide our opinion on problems and limitations concerning the deployment of mechanisms belonging to each considered architecture class.

Autonomous driving and intelligent transportation demand ultra-low latency and high reliability communication in future vehicular networks. Proactive wireless communication can facilitate minimal latency by open-loop communication, which discards traditional feedback control mechanisms. However, appropriate radio resource allocation in such proactive mobile networks has not been fully studied due to lacking channel state information (CSI) and the alleviation of multiple access interference (MAI) in multiple virtual cells. This paper aims to ensure the reliability of downlink communication by a novel radio resource allocation scheme in proactive vehicular networks with ultra-low latency. We regard data transmission success rate as the reliability indicator and propose a joint radio resource allocation model based on the “generalized closed-loop”, where anchor node (AN) uses the radio resource utilization information (RRUI) from the vehicle in the immediate past uplink as a guide to assist resource allocation. Subsequently, we study the radio resource allocation model solution on the vehicle side and the network side respectively. On the vehicle side, vehicles use the local or global data transmission experience to select the radio resource with the best quality as the RRUI. On the network side, according to the latest RRUI of vehicle and resource occupancy information, deep reinforcement learning is proposed to make appropriate radio resource allocation decisions. Simulations demonstrate the effectiveness of the intelligent joint radio resource allocation scheme under the cooperation between vehicles and AN. When the resource load rate reaches 40%, the joint radio resource allocation scheme can achieve a data transmission success rate of more than 98%.

Cooperative Vehicular Platooning (Co-VP) is one of the most prominent and challenging applications of Intelligent Traffic Systems. To support such vehicular communications, the ETSI ITS G5 standard specifies event-based communication profiles, triggered by kinematic parameters such as speed. The standard defines a set of threshold values for such triggers but no careful assessment in realistic platooning scenarios has been done to confirm the suitability of such values. In this work, we investigate the safety and performance limitations of such parameters in a realistic platooning co-simulation environment. We then propose more conservative threshold values, that we formalize as a new profile, and evaluate their impact in the longitudinal and lateral behaviour of a vehicular platoon as it carries out complex driving scenarios. Furthermore, we analyze the overhead introduced in the network by applying the new threshold values. We conclude that a pro-active message transmission scheme leads to improved platoon performance for highway scenarios, notably an increase greater than 40% in the longitudinal performance of the platoon, while not incurring in a significant network overhead. The obtained results also demonstrated an improved platoon performance for for semi-urban scenarios, including obstacles and curves, where the heading error decreases in 26%, with slight network overhead.

The automobile industry has been evolving with rapid growth over the last two decades. Conventionally, the narrow band dedicated short-range communications is used for the wireless connectivity of vehicular networks in the automobile industry. However, due to its shorter wireless range, higher latency, and lower capacity; cellular-assisted vehicular communications can be adopted to improve networks performance by disrupting the nearby infrastructure. The performance of cellular-assisted vehicular communications is typically compromised if jammers are present in their vicinity. Thus, it is important to characterize the interference in cellular-assisted vehicular communications with jammers. In this paper, we investigate jamming interference due to clustered-jammers for vehicular networks by considering that vehicles, road-side units, and pedestrians are modeled using an independent one-dimensional Poisson line process while cellular base stations are modeled using an independent two-dimensional homogeneous Poisson point process. The results show that jamming disrupts the performance of the vehicular networks and that this performance is severely compromised by increasing the density of jamming clusters and their transmission power. Therefore, the prime focus of the system designers should be to introduce anti-jamming scenario, when the transmission power and the number of jamming clusters in a network increase.

Federated learning is a promising paradigm for achieving distributed intelligence by protecting user privacy in vehicular networks. Considering limited computing and communication resources, it is important to select appropriate clients from a huge number of users to participate in the training process. In vehicular networks, the problem of choosing proper clients is particularly complex due to the heterogeneity of network users, including the differences in the data, computation capability, available throughput, and samples freshness. We design a fuzzy logic based client selection scheme to address this issue. The proposed scheme considers the number of local samples, samples freshness, computation capability, and available network throughput based on a fuzzy logic approach. Extensive simulation results show that the proposed scheme outperforms other baselines.

Location privacy of the vehicles is considered as the potential information that needs to be protected with maximum care in the vehicular network. This location privacy depends on the method of the pseudonym strategy scheme adopted in the mix zone of vehicular networks. The maximum degree of privacy is required in the mixed zone because the decentralization and lower vehicular density of vehicles in the mixed zone have the maximum probability of reducing the strength of facilitating location privacy. However, location privacy in the mixed zone depends on the number of cooperative vehicles interacting within the spatio temporal environment. In this paper, an incentive-based co-operation motivating pseudonym changing strategy for privacy preservation in mixed zones is proposed for resolving the issue of pseudonym change and influence of cooperative vehicle density. This proposed mechanism uses a one-way hash function and an improved pseudonym scheme for estimating vehicular incentives in order to facilitate privacy protection. The extensive investigation of the proposed mechanism is confirmed to be excellent in terms of location privacy under different distances, a number of vehicles and the number of mixed zones compared to the pseudonym changing privacy preservation schemes proposed for addressing privacy in mixed zones of vehicular networks.

Abstract Millimeter Wave (mmWave) technology has been regarded as a feasible approach for future vehicular communications. Nevertheless, high path loss and penetration loss raise severe questions on mmWave communications. These problems can be mitigated by directional communication, which is not easy to achieve in highly dynamic vehicular communications. The existing works addressed the beam alignment problem by designing online learning-based mmWave beam selection schemes, which can be well adapted to high dynamic vehicular scenarios. However, this kind of work focuses on network throughput rather than network energy efficiency, which ignores the consideration of energy consumption. Therefore, we propose an Energy efficiency-based FML (EFML) scheme to compensate for this shortfall. In EFML, the energy consumption is reduced as far as possible under the premise of meeting the basic data rate requirements of vehicle users, and the users requesting the same content in close proximity can be organized into the same receiving group to share the same mmWave beam. The simulation results demonstrate that, compare with the comparison method with best energy efficiency, the proposed EFML improves energy efficiency by 17–41% in different scenarios.

Autonomous driving systems are highly expected to be used on public roads to improve traffic throughput and road safety, but it will likely take a long transition period before all human-driven vehicles can be replaces with autonomous vehicles. Hence, CAVs have to safely cooperate with the surrounding human drivers, in order to gain the benefits of autonomous driving technologies during such transition periods. In this paper, we present a Distributed Synchronous Intersection Protocol (DSIP) and a Cooperative Perception-based High-Definition Map (CP-HD Map) for mixed traffic environments, in which autonomous vehicles co-exist with human-driven vehicles. First, in DSIP, each CAV utilizes dynamic decision-making mechanisms to adaptively change the vehicle behaviors based on the surrounding environments by using vehicle states and vehicle mode. In addition, in CP-HD Map, each CAV uses Vehicle-to-Vehicle (V2V) communications to share the information of detected objects to improve the road safety in the mixed traffic environments. Under these protocols, human-driven vehicles simply follow the traffic lights just like they do today. Finally, we show that DSIP and CP-HD Map increase the traffic throughput around the road intersections when we compared to existing signalized intersections and other V2V communications-based protocols.

We address the problem of shockwave formation in uncoordinated highway traffic. The problem is caused by the combination of heavy traffic and small traffic perturbations or unexpected drivers actions. We propose a novel distributed communication protocol that helps mitigate upstream shockwave formation even with extremely low system penetration rates. Based on traffic information ahead, the Cooperative Advanced Driver Assistance System (CADAS) recommends non-intuitive velocity reductions in order to redistribute traffic more uniformly and eliminate traffic peaks. Simulation results show that CADAS significantly increases the average velocity and therewith reduces the overall travel time and avoids unnecessary slowdowns. As a next step, for realism, we propose to apply CADAS to real traffic traces. Also, we extend the shockwave model from single to multiple lanes (to reduce accidents caused by lane switching).

Modern cities require a tighter integration with Information and Communication Technologies (ICT) for bringing new services to the citizens. The Smart City is the revolutionary paradigm aiming at integrating the ICT with the citizen life; among several urban services, transports are one of the most important in modern cities, introducing several challenges to the Smart City paradigm. In order to satisfy the stringent requirements of new vehicular applications and services, Edge Computing (EC) is one of the most promising technologies when integrated into the Vehicular Networks (VNs). EC-enabled VNs can facilitate new latency-critical and data-intensive applications and services. However, ground-based EC platforms (i.e., Road Side Units—RSUs, 5G Base Stations—5G BS) can only serve a reduced number of Vehicular Users (VUs), due to short coverage ranges and resource shortage. In the recent past, several new aerial platforms with integrated EC facilities have been deployed for achieving global connectivity. Such air-based EC platforms can complement the ground-based EC facilities for creating a futuristic VN able to deploy several new applications and services. The goal of this work is to explore the possibility of creating a novel joint air-ground EC platform within a VN architecture for helping VUs with new intelligent applications and services. By exploiting most modern technologies, with particular attention towards network softwarization, vehicular edge computing, and machine learning, we propose here three possible layered air-ground EC-enabled VN scenarios. For each of the discussed scenarios, a list of the possible challenges is considered, as well possible solutions allowing to overcome all or some of the considered challenges. A proper comparison is also done, through the use of tables, where all the proposed scenarios, and the proposed solutions, are discussed.

Traffic monitoring services collect traffic reports and respond to users’ traffic queries. However, the reports and queries may reveal the user’s identity and location. Although different anonymization techniques have been applied to protect user privacy, a new security threat arises, namely, n-by-1 jamming attack, in which an anonymous contributing driver impersonates $n$ drivers and uploads $n$ normal reports by using $n$ reporting devices. Such an attack will mislead the traffic monitoring service provider and further degrade the service quality. Existing traffic monitoring services do not support customized queries, and private information retrieval techniques cannot be applied directly in traffic monitoring. We formally define the new attack and propose a traffic monitoring scheme TraJ to defend the attack and achieve user-defined location privacy. Specifically, we bridge anonymous contributing drivers without disclosing their speed set by using private set intersection. Each RSU collects time traffic reports and structures a weighted proximity graph to filter out malicious colluding drivers. We design a user-defined privacy-preserving query method by encoding complex road network. We leverage the uploading phase from private aggregation to collect traffic conditions and allow requesting drivers to dynamically and privately query traffic conditions. We provide a formal analysis of TraJ to prove its privacy and security properties. We also construct a prototype based on a real-world dataset and Android smartphones to demonstrate its feasibility and efficiency. A formal analysis demonstrates the privacy and security properties. Extensive experiments illustrate the performance and defense efficacy. [ABSTRACT FROM AUTHOR]

In vehicular networks, the benefits of jointly-optimizing data prefetching and caching with broadcast transmission scheduling and rate adaptation strategies at road-side units (RSUs) have already been demonstrated in the literature. Nevertheless, the effectiveness of the solution depends greatly on the accurate knowledge of vehicular trajectories. In practice, as specified in the standards, this is at odds with the important issue of privacy. One of the main contributions of this work is to address this issue by providing a scheme that jointly optimizes the throughput of broadcast-transmission scheduling from RSUs to vehicles, and the privacy of vehicles’ locations, by enabling them to disseminate obfuscated locations to the server from time to time. We formulate this problem as a reinforcement learning (RL) problem, where the vehicles learn when to report obfuscated locations, in order to maximize a utility that encompasses both the network capacity (related to vehicles’ throughput) and the level of privacy achieved. The proposed scheme is shown to consistently outperform alternative randomized schemes considered in past work, and in particular it proves to be robust against prediction errors of the future locations of the vehicles. [ABSTRACT FROM AUTHOR]

This letter studies sensing-oriented communications in vehicular networks in which multiple vehicles are equipped with visual sensors whose observations are transmitted to the cloud to estimate 3D environment model. Our aim is to maximize the sensing quality under communication constraints, which are twofold, i.e., the number of activated observations and the number of bits. We first propose information-based metrics to evaluate both accuracy and completeness of sensing in the network, and reveal the impacts of communication constraints on sensing based on eigenspace decomposition. Moreover, we design a hierarchical communication resource allocation scheme, with their performance validated by numerical results. [ABSTRACT FROM AUTHOR]

To reduce computing delay and energy consumption in the Vehicular networks, the total cost of task offloading, namely delay and energy consumption, is studied. A task offloading model combining local vehicle computing, MEC (Mobile Edge Computing) server computing, and cloud computing is proposed. The model not only considers the priority relationship of tasks, but also considers the delay and energy consumption of the system. A computational offloading decision method IBES based on an improved bald eagle search optimization algorithm is designed, which introduces Tent chaotic mapping, Levy Flight mechanism and Adaptive weights into the bald eagle search optimization algorithm to increase initial population diversity, enhance local search and global convergence. The simulation results show that the total cost of IBES is 33.07% and 22.73% lower than that of PSO and BES, respectively. [ABSTRACT FROM AUTHOR]

Smart cities have been developed over the past decade, and reducing traffic congestion has been the top concern in smart city development. Short delays in communication between vehicles and Roadside Units (RSUs), smooth traffic flow, and road safety are the key challenges of Intelligent Transportation Systems (ITSs). The rapid upsurge in the number of road vehicles has increased traffic congestion and the number of road accidents. To fix this issue, Vehicular Networks (VNs) have developed many new ideas, including vehicular communications, navigation, and traffic control. Machine Learning (ML) is an efficient approach to finding hidden insights into ITS without being programmed explicitly by learning from data. This research proposed a fusion-based intelligent traffic congestion control system for VNs (FITCCS-VN) using ML techniques that collect traffic data and route traffic on available routes to alleviate traffic congestion in smart cities. The proposed system provides innovative services to the drivers that enable a view of traffic flow and the volume of vehicles available on the road remotely, intending to avoid traffic jams. The proposed model improves traffic flow and decreases congestion. The proposed system provides an accuracy of 95% and a miss rate of 5%, which is better than previous approaches. [ABSTRACT FROM AUTHOR]

Sharing up-to-date environment information collected by intelligent connected vehicles is critical in achieving travel comfort, convenience, and safety in vehicular networks. Individually collected information should be made available to other vehicular nodes, adjacent or distant, to achieve an informed and well-managed vehicular traffic. The coverage reach of sharing these road data can be maximized by allocating roadside units in strategic positions. In this work, we propose an Enhanced Information SHAring via Roadside Unit Allocation (EISHA–RSU) scheme that strategically determines where RSUs must be deployed from all spatial candidate locations. The urban area is irregularly partitioned into effective regions of movement (ERM) according to vehicular capacity with priority. For each ERM, EISHA–RSU greedily allocates the initial RSU to an effective position and optimally assigns the remaining RSUs to spatial locations that capture the maximum I2V/V2I information sharing based on the area’s average road speed. In effect, the proposed deployment scheme addresses both the issues of coverage and connectivity among vehicles and the infrastructure. We evaluate the proposed RSU allocation scheme by employing three urban empirical mobility datasets and compare its network starvation fairness, effectiveness, and efficiency performance measures with three other deployment benchmarks. Overall, EISHA–RSU reduces the number of required RSUs to cover a certain area, exhibits higher connectivity, and achieves maximum I2V/V2I information sharing among the evaluated schemes. [ABSTRACT FROM AUTHOR]

In vehicular edge computing networks, electric vehicles can get charging services from edge servers through some road-side charging stations. Several recent studies have investigated how to deal with security requirements in these communications. Nevertheless, simultaneous provision of security and lightness, which is crucial for resource-constrained vehicles, is still an open issue. More critically, the anonymity from the perspective of honest-but-curious edge nodes has not been yet addressed. Thus, this paper proposes a novel ultra-lightweight framework for the secure and anonymous communications of vehicles during their charging reception by means of blockchain. Thanks to the blockchain technology, the accountability of electric vehicle possessors guaranteed. The proposed scheme has been validated in terms of security metrics and also implemented on two ARM-based platforms, one 32-bit ARM microcontroller and one 64-bit ARM processor. Further, its blockchain part has been deployed on a Hyperledger Fabric network. The obtained results besides the comparison with well-respected similar schemes acknowledge the usefulness and practicability of the presented scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the integrated sensing and communication (ISAC) in vehicle-to-infrastructure (V2I) networks. To realize ISAC, an effective beamforming design is essential which however, highly depends on the availability of accurate channel tracking requiring large training overhead and computational complexity. Motivated by this, we adopt a deep learning (DL) approach to implicitly learn the features of historical channels and directly predict the beamforming matrix to be adopted for the next time slot to maximize the average achievable sum-rate of an ISAC system. The proposed method can bypass the need of explicit channel tracking process and reduce the signaling overhead significantly. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds-based sensing constraints is first formulated for the considered ISAC system taking into account the multiple access interference. Then, by exploiting the penalty method, a versatile unsupervised DL-based predictive beamforming design framework is developed to address the formulated design problem. As a realization of the developed framework, a historical channels-based convolutional long short-term memory (LSTM) network (HCL-Net) is devised for predictive beamforming in the ISAC-based V2I network. Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed predictive method not only guarantees the required sensing performance, but also achieves a satisfactory sum-rate that can approach the upper bound obtained by the genie-aided scheme with the perfect instantaneous channel state information available. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-level adaptive resource allocation (TARA) framework to support vehicular safety message transmissions. In particular, three types of safety messages are considered in urban vehicular networks, i.e., event-triggered messages for urgent condition warnings, periodic messages for vehicular status notifications, and messages for environmental perception. Roadside units are deployed for network management, and thus messages can be transmitted through either vehicle-to-infrastructure or vehicle-to-vehicle connections. To satisfy the requirements of different message transmissions, TARA framework consists of a group-level resource reservation module and a vehicle-level resource allocation module. Particularly, the resource reservation module is designed to allocate resources to support different types of message transmissions for each vehicle group at the first level. To learn the implicit relationship between the resource demand and message transmission requests, a supervised learning model is devised in the resource reservation module, where to obtain the training data we further propose a sequential resource allocation (SRA) scheme. Based on historical network information, SRA scheme offline optimizes the allocation of sensing resources, i.e., choosing vehicles to provide perception data, and communication resources. With resources reserved for each group, the vehicle-level resource allocation module is then devised to distribute specific resources for each vehicle to satisfy the differential requirements in real-time. Extensive simulation results demonstrate the effectiveness of TARA framework in terms of the high packet delivery ratio and low latency for message transmissions, and the high quality of collective environmental perception. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITS) aim at integration of innovative technologies in next-generation vehicles in order to increase the safety and efficiency standards of vehicular traffic fluxes. Although many sensors are currently available in modern cars, mutual data exchange between vehicles in real-time will be crucial in future smart vehicles, but this domain, and in particular bidirectional Vehicle-to-Vehicle (V2V) through Visible Light Communications (VLC), remain relatively unexplored yet. In this paper we present a system for bidirectional data transmission between vehicles through VLC. Specifically, we implement for the first time a bidirectional VLC link using real motorcycle headlights (HL) and rear lights (RL), and we provide a characterization of transmission performances under direct sunlight conditions in an outdoor scenario for realistic distances (up to 30 m) and relative positions between two vehicles. The performance of the VLC system has been evaluated in terms of Signal-to-Noise (SNR) maps and Packet Error Rate (PER), measured in various configurations and for different baudrates (28 kBaud / 57 kBaud). Our result show that a bidirectional error-free communication is possible between two consecutive vehicles for distances up to 12 m, whilst successful transmission could be observed up to 21 m. Finally, we compare our bidirectional intensity and PER data in realistic (flat) configuration against the predictions of a physical model which considers non-ideal orientations of the receiver stage through a novel, simple technique that could also be exploited in different VLC implementations. A very good agreement between the experimental data set and the model is found on the whole measurements grid. [ABSTRACT FROM AUTHOR]