Roadside Units Research Articles

PDTM: A Provenance-Driven dynamic Trust Management model for IoVs

Internet of Vehicles (IoVs) play an essential role in traffic safety and travel efficiency. However, there is still risk where malicious vehicles without validation or evaluation, to tamper with messages and pollute network with compromised information. Upon fruitful computing resource in 6G, this paper proposes a Provenance-Driven dynamic Trust Management (PDTM) model for IoVs. PDTM integrates data provenance into the trust evaluation of vehicles and messages. Different from literature that only evaluates trust through vehicle, the high reliability of Road Side Unit (RSU) is concerned herein. Specifically, the recommendation trust is calculated by RSU, to eliminate false recommendations of malicious vehicles. Considering the dynamic and real-time nature of trust, this paper proposes a global-based threshold update mechanism. The mechanism can dynamically adjust the threshold, according to the trust value of vehicles. Simulation experiments verify the validity of PDTM in the detection rate of malicious vehicles and delivery rate of authentic messages.

Collaborative caching relay algorithm based on recursive deep reinforcement learning in mobile vehicle edge network

With the rapid development of Internet of vehicles (IoV) and the continuous emergence of vehicle information applications, the demand for content in vehicle networking is growing at an alarming speed. Mobile vehicular edge caching is regarded as a promising technology in improving Quality of Service (QoS) and reducing latency. Many caching algorithms have been proposed, which usually place contents in the Road Side Units (RSUs) to provide services to users near them. However, due to the high-speed movement of vehicles and limitations of RSU coverage, caching interrupts often occur frequently, leading to a deterioration in service quality. To deal with this problem, we make full use of Vehicle-to-Vehicle (V2V) collaboration to construct a caching system which does not require RSU support, and propose a Recursive Deep Reinforcement Learning based Collaborative Caching Relay strategy (RDRL-CR). On purpose to minimize the service delay under capacity constraints, the caching problem is formulated as an integer linear programming problem, and caching decisions are achieved through partially observable Markov Decision Process (MDP). Specifically, this strategy utilizes a Graph Neural Network (GNN) to predict vehicle trajectories, and then selects vehicles that can serve as caching nodes by calculating link stability metrics between vehicles. The Long Short Term Memory (LSTM) network is embedded into a deep deterministic strategy gradient algorithm to achieve the final caching decision. Compared with existing caching strategies, the proposed caching strategy in this paper improves the caching hit rate by about 25% and reduces content access latency by about 20%.

Multi-Agent Caching Strategy for Spatial-Temporal Popularity in IoV

Motivated by connected vehicles, the internet of vehicles (IoV) has a prosperous development, thus a variety of IoV applications have emerged, which causes the dramatic growth of data traffic and content request, thus affecting the quality of service (QoS) of IoV. Edge caching can effectively improve the QoS by caching the contents in the road side unit (RSU) near the requester. Moreover, most existing IoV caching strategies only consider that the content popularity has temporal correlation or assume that the content popularity is given, which will impact the accuracy of content popularity. In this paper, we propose a spatial-temporal correlation approach to predict the content popularity, which can improve the accuracy of the content popularity by the spatial-temporal correlation analyzing of the RSU historical content requests. Then, we introduce a multi-agent reinforcement learning (MARL) caching strategy based on the result of prediction, where each RSU agent individually chooses the action with the constraint of caching resources and updates the action to maximize the cumulative reward. Simulation results demonstrate that the proposed caching strategy has better performance. Take the Zipf parameter is 0.8 as an example, compared with the probability caching scheme (PCS), the proposed caching strategy improves the caching hit ratio by 15% and reduces the delay by 34%.

Joint Task Offloading and Resource Allocation for Vehicular Edge Computing With Result Feedback Delay

In this paper, we study the problem of joint Task offloading and resource Allocation for vehicular edge computing with Result Feedback Delay (TARFD). Specifically, we consider a typical roadside unit (RSU) and vehicles within its coverage area, and optimize the task offloading decisions of vehicles as well as the uplink bandwidth allocation and the computation resources allocation on the RSU. The TARFD problem is formulated as a non-convex mixed integer nonlinear programming (MINLP) to minimize the average delay consisting of task offloading delay, task computation delay, and result feedback delay. We derive a lower bound of the optimum to the TARFD problem, based on which we propose an approximate algorithm of the TARFD problem, called A-TARFD. The A-TARFD algorithm can effectively deliver solutions for small-scale scenarios. To tackle large-scale scenarios, a low-complexity algorithm for the TARFD problem, called L-TARFD, is developed by constructing an iteratively updated sequence of locally tight approximate geometric programming (GP) problems. The L-TARFD algorithm can converge to a Karush-Kuhn-Tucker (KKT) point and forces the offloading decisions arbitrarily close to binary values. By comparison with the lower bound, simulation results show that the proposed two algorithms have near-optimal performance over a wide range of parameter settings.

Toward Optimal Deployment of UAV Relays in UAV-Assisted Internet of Vehicles

The past decade has witnessed an explosive growth of unmanned aerial vehicles (UAVs), which have attracted great attention in assisting Internet of Vehicles (IoV) communication systems thanks to the advantages of cost-effectiveness, high maneuverability, and flexible deployment. Yet several issues, such as UAVs' transmit power and energy resources as well as the high-speed mobility of IoV user equipment (VUE), pose challenges to the development of UAVs in UAV-assisted IoV networks. To provide reliable and high-throughput wireless links for VUEs in a given ground area, this paper considers a scenario in which a ground VUE is facing difficulties in receiving service from a ground roadside unit (RSU) owing to possible sudden RSU malfunction or poor signal coverage. In this case, UAVs are generally deployed in the air, serving as relays to establish communication links between uncovered VUE and the neighboring RSU. To maximize the system capacity, we investigate the deployment problem of UAV relays under single-UAV relaying and multi-UAV relaying cases upon considering VUE mobility, and exploit the optimal deployment strategy of UAV relays under fixed total power consumption. Comprehensive simulation results verify that the proposed algorithm can outperform benchmark methods on significant critical indicators such as the average achievable user rate, and the outage probability. Also, evaluations and analyses have highlighted the effects of key system parameters (e.g., altitude, number, and transmit power of UAV relays) on optimal UAV relay deployment position and system performance.

Communication-Efficient Federated Double Distillation in IoV

6G will be the next horizon from connected people and things to intelligence-of-everything. Machine learning (ML) combines artificial intelligence with vehicular edge computing (VEC), further releasing the potential of data. Federated learning (FL) is used to manage the collaborative training of vehicular clients and the road side unit (RSU), which can efficiently complete the distributed data processing. However, the vehicular clients upload a large number of model parameters to occupy regular communication resources, and the stability of the link cannot be guaranteed due to the mobility of the vehicles, which may lead to expensive communication overhead in VEC. Thus, this paper proposes a communication-efficient federated double distillation (FedDD) framework, which comprehensively considers the three-dimensional attributes of the vehicles and dynamically selects the cluster-heads (CHs) to improve the transmission efficiency. Then, knowledge distillation is further integrated into the federated learning to accomplish multiple rounds of parameter distillation, thereby significantly reducing the communication overhead. Experimental results show that compared to traditional FedAvg, FedDD can reduce communication overhead by three orders of magnitude. The communication overhead of FedDD is reduced by 82% compared to FTTQ, which improves the communication efficiency of FL while sacrificing a small amount of accuracy.

Broadcast and Silence Period (BSP): A Pseudonym Change Strategy

Vehicles in Internet of Vehicles must constantly broadcast basic safety messages to keep other vehicles informed of their location, speed, and other parameters. Attackers can use the content of these messages to follow the path of the vehicle and invade vehicle owner privacy. Vehicle location privacy was ensured by broadcasting these messages using pseudonyms and periodically changing these pseudonyms. However, in mix-zone-based strategies, vehicles are frequently restricted from changing their pseudonyms in specific places. Furthermore, most previous change strategies only safeguarded against the syntactic linking attack and did not account for the semantic linking attack. Thus, in this study, we proposed a broadcast and silence period pseudonym change strategy to overcome these difficulties. Mix zones were obtained by analyzing the locations where vehicles frequently stop. The vehicle broadcasts or silences in a mix zone according to our broadcast and silence period strategy and changes to the pseudonym assigned by the road side unit during the silence phase until it leaves the zone. Our safety analysis and experimental results indicate that the broadcast and silence period prevent both linking attacks while reducing the impact of radio silence on vehicle safety by limiting the silent time, thereby providing better location privacy protection for vehicles compared with the existing schemes. In addition, the number of signatures required to be verified each time it receives messages and the overhead incurred in changing pseudonyms for vehicles is acceptable.

An RSU-Assisted Hybrid Emergency Message Broadcasting Protocol for VANETs

In vehicular ad hoc networks (VANETs), emergency messages broadcasting has been considered as one of the important parts for safety related applications. The efficiency and reliability of the emergency message broadcasting are severely affected by the network structure, message redundancy, channel contention, etc. To address this problem, in this paper, we propose an RSUs-assisted hybrid emergency message broadcasting (RA-HEMB) protocol for two-way grid roads in urban VANETs. In doing so, the broadcasting target region is first generated based on the instantaneous traffic status and types of the emergency messages. To balance the deliver latency and the reliability, an adaptive forwarding nodes selection scheme based on position is proposed to dynamically determine the forwarding area according to different vehicle densities and vehicle-to-vehicle (V2V) communication ranges. Then, based on the forwarding nodes selection scheme and roadside unit (RSU) deployment, an RA-HEMB protocol is established by properly utilizing both the wired links via RSUs and the wireless links to broadcast the emergency message in hybrid vehicular networks. Simulation results show that the proposed RA-HEMB can greatly shorten the broadcasting time needed to notice the nodes within the target region performance compared to the benchmark protocols.

Task Offloading and Resource Allocation in Vehicular Networks: A Lyapunov-Based Deep Reinforcement Learning Approach

Vehicular Edge Computing (VEC) has gained popularity due to its ability to enhance vehicular networks. VEC servers located at Roadside Units (RSUs) allow low-power vehicles to offload computation-intensive and delay-sensitive applications, making it a promising solution. However, optimal resource allocation between edge servers is a complex issue due to vehicle mobility and dynamic data traffic. To address this issue, we propose a Lyapunov-based Multi-Agent Deep Deterministic Policy Gradient (L-MADDPG) method that jointly optimizes computing task distribution and radio resource allocation to minimize energy consumption and delay requirements. We evaluate the trade-offs between the performance of the optimization algorithm, queuing model, and energy consumption. We first examine delay, queue and energy models for task execution at the vehicle or RSU, followed by the L-MADDPG algorithm for jointly optimizing task offloading and resource allocation problems to reduce energy consumption without compromising performance. Our simulation results show that our algorithm can reduce energy consumption while maintaining system performance compared to existing algorithms.

Integrated Localization and Planning for Cruise Control of UGV Platoons in Infrastructure-Free Environments

This paper investigates the cruise control problem of unmanned ground vehicle (UGV) platoons from the implementation perspective. Unlike most existing works related to platoon cruise control which rely on positioning infrastructures such as lane markings, roadside units, and global navigation satellite systems (GNSS), this paper explores a new problem: platoon cruise control in environments without positioning infrastructures. The introduction of this constraint disables most existing cruise control approaches. To address this problem, an integrated localization and planning framework is proposed, which is composed of three modular algorithms. Firstly, to localize multiple vehicles in a common coordinate system, a collaborative localization algorithm is developed through matching local perceptions of different vehicles. Secondly, to maintain the desired platoon configuration, the historical trajectory of the preceding vehicle is reconstructed, based on which the target state is planned for the following vehicle. Finally, a virtual controller based algorithm is designed to generate feasible trajectories for the following vehicle in real time. The proposed framework has two salient features. Firstly, it does not depend on positioning infrastructures and does not introduce additional positioning sensors, such as GNSS/INS modules, ultra-wideband (UWB) devices, magnetic meters and so on, as long as each vehicle is equipped with a perception sensor (Lidar, radar or camera), which however is essential equipment for nowaday autonomous systems. Secondly, the proposed framework does not depend on direct observations between vehicles to achieve relative localization, making it applicable in non-line-of-sight (non-LOS) situations. Real-world experiments have been conducted to validate the effectiveness, robustness and practicality of the proposed framework.

Assistant vehicle locating based on DOA estimation of deep unfolded network

In view of the decline in the positioning accuracy of the vehicles with small snapshots, this paper proposes a direction of arrival(DOA) estimation method based on deep unfolded network to assist the global positioning system to achieve high-precision locating. Firstly, the signal emitted from the on board unit installed on the vehicle is received by the array antenna of the road side unit(RSU), then RSU transmits the array data to cloud service platform, in which the traditional fixed point continuation algorithm is improved by using l1 norm instead of l2 norm, and the adaptive outlier point tracking algorithm is used to deal with the problem of symbol flipping caused by noise. Then we unfold the improved fixed point continuous algorithm for obtaining a neural network. In order to improve the DOA estimation performance and convergence speed, this paper uses the black widow optimization algorithm to determine the soft threshold shrinkage parameters. According to the mapping between the signal and the array manifold matrix, the DOA of the target is estimated, and its location can be evaluated from the geometry relation between vehicle and RSU. The simulation results show that the algorithm proposed in this paper has better direction finding performance and improves the vehicle positioning accuracy under small snapshots and high signal-to-noise ratio effectively.

TCaS‐VN: A novel V2V communication scheme to improve traffic control and safety in vehicular cloud networks

SummaryRoad accidents and traffic congestion are the unavoidable consequences of the growing number of vehicles on the road. Conventional management systems are not effective in mitigating these issues. However, a range of technologies, including sensors, processors, vehicle‐based resources, and mobile cloud computing, can help solve traffic and safety problems. Vehicular cloud networks, a cutting‐edge technology for the development of intelligent transportation systems, face significant challenges, such as ensuring reliable service providers that maintain stable connections between vehicles. Roadside units (RSUs) play a crucial role in these networks, but they come with several drawbacks, including high costs, imbalanced load distribution, and unreliable connections. To address these issues, we propose the Traffic Control and Safety in Vehicular Networks (TCaS‐VN) algorithm, a cloud‐based sharing services framework for vehicles that eliminates the need for RSUs. This approach offers several advantages, including a twofold improvement in the lifetime of clusters, the elimination of noncluster head nodes, a 54% reduction in overhead, a 58% reduction in service search time, and a success rate of over 95% in service provisioning.

Access Control Strategy for the Internet of Vehicles Based on Blockchain and Edge Computing

Data stored in the Internet of Vehicles (IoV) face problems with ease of tampering, easy disclosure and single access control. Based on this problem, we propose an access control scheme for the IoV based on blockchain, trust values and weighted attribute-based encryption, called the Blockchain Trust and Weighted Attribute-Based Access Control Strategy (BTWACS). First, we utilize both local and global blockchains to jointly maintain the generation, verification and storage of blocks, achieving distributed data storage and ensuring that data cannot arbitrarily be tampered with. Local blockchain mainly uses Road Side Unit (RSU) technology to calculate trust values, while global blockchain is mainly responsible for data storage and access policy selection. Secondly, we design a blockchain-based trust evaluation scheme called Blockchain-Based Trust Evaluation (BBTE). In this evaluation scheme, the trust value of the vehicle node is based on four factors: initial trust, historical experience trust, recommendation trust and RSU observation trust. CRITIC is used to determine the optimal weights of four factors to obtain the trust value. Then, we use the Network Simulator version 3 (NS3) to verify the security and accuracy of BBTE, improving the recognition accuracy and detection rate of malicious vehicle nodes. Finally, by mining the association relationships between attribute permissions among various roles, we construct a hierarchical access control strategy based on weight and trust, and further optimize the access strategy through pruning techniques. The experiment results indicate that this scheme can effectively respond to gray hole attacks, defamation attacks and collusion attacks from other vehicle nodes. This method can effectively reduce the computing and transmission costs of vehicles and meet the access requirements of multiple entities and roles in the IoV.

Mobility-aware Vehicular Cloud formation mechanism for Vehicular Edge Computing environments

Rapid advancements in vehicular technology and increased vehicle modernization have led to the emergence of intelligent and interconnected entities. As a result, the Vehicular Edge Computing (VEC) paradigm has gained prominence. This paradigm enables the provision of cloud computing services close to vehicular users by utilizing the idle computational resources of vehicles to execute tasks that require computing power beyond what is available locally. Aggregating these computational resources in the vehicular context is known as Vehicular Cloud (VCloud) formation. However, leveraging and aggregating these resources poses several challenges due to the dynamic nature of the vehicular environment. One of the main challenges is the efficient selection of vehicles to assume management roles in the distribution of computational power within the group, often referred to as leading vehicles. This research presents a mobility-aware mechanism called PREDATOR to enhance the VCloud formation process. In this mechanism, the Roadside Unit (RSU) provides vehicular mobility predictions, enabling the selection of the most stable vehicles within the RSU coverage area to assume leadership roles in the VCloud. In this context, vehicle stability is associated with a vehicle’s time within the RSU coverage area, known as dwell time. PREDATOR employs a microscopic perspective to select vehicles with the longest dwell time in the VCloud, allowing for efficient management of computational resource utilization. Simulation results have demonstrated that PREDATOR not only increases the VCloud lifetime but also minimizes leader changes, reduces network message exchange, mitigates packet collisions, and facilitates the effective utilization of aggregated vehicular resources compared to state-of-the-art approaches.

Maximizing VANET performance in cluster head selection using Intelligent Fuzzy Bald Eagle optimization

Vehicular Ad-hoc Network (VANET) is a type of wireless network that allows communication among vehicles and roadside units to develop advanced intelligent transportation systems. The success of VANETs depends on the stability of wireless communication, among vehicles, which is challenging to achieve due to high vehicle speed, rapidly changing topology, and unstable communication links. Moreover, the instability of the network caused by the mobile nature of vehicles in VANET reduces the performance of the network. Clustering in VANETs is a crucial technique that organizes the network and forms the basis of the routing protocol. Clustering algorithms are designed for VANETs to work efficiently and require several phases that must be integrated into the process before a clustering decision can be made. Therefore, this paper presents a novel Intelligent Fuzzy Bald Eagle (IFBE) optimization to enhance the performance of VANETs by optimizing the cluster head selection (CHS) process. The experimental results prove that the IFBE approach outperforms other existing mechanisms in terms of energy consumption, end-to-end delay, and packet delivery ratio. The proposed mechanism utilizes an intelligent fuzzy system to optimize the CHS process. The fuzzy system uses a set of rules and membership functions to evaluate the candidate nodes' suitability for being cluster heads. The Bald Eagle Search (BES) optimization meta-heuristic algorithm is used to find the optimal values for the fuzzy system's membership functions. The proposed mechanism was evaluated using the MATLAB simulator. Finally, the experimental result proved that the IFBE approach achieved minimum delay and energy consumption of 13.58 ms, and 15.5 J, and higher clustering efficiency and packet delivery rate of 94.15% and 97.65% respectively, which show that it performs better than other existing approaches.

Blockchain‐assisted twin migration for vehicular metaverses: A game theory approach

AbstractAs the fusion of automotive industry and metaverse, vehicular metaverses establish a bridge between the physical space and virtual space, providing intelligent transportation services through the integration of various technologies, such as extended reality and real‐time rendering technologies, to offer immersive metaverse services for vehicular metaverse users (VMUs). In vehicular metaverses, VMUs update vehicle twins (VTs) deployed in RoadSide Units (RSUs) to obtain metaverse services. However, due to the mobility of vehicles and the limited service coverage of RSUs, VT migration is necessary to ensure continuous immersive experiences for VMUs. This process requires RSUs to contribute resources for enabling efficient migration, which leads to a resource‐trading problem between RSUs and VMUs. Moreover, a single RSU cannot support large‐scale VT migration. To this end, we propose a blockchain‐assisted game approach framework for reliable VT migration in vehicular metaverses. Based on the subject logic model, we first calculate the reputation values of RSUs considering the freshness of interaction between RSUs and VMUs. Then, a coalition game based on the reputation values of RSUs is formulated, and RSU coalitions are formed to jointly provide bandwidth resources for reliable and large‐scale VT migration. Subsequently, the RSU coalition with the highest utility is selected. Finally, to incentivize VMUs to participate in VT migration, we propose a Stackelberg model between the selected coalition and VMUs. Numerical results demonstrate the reliability and effectiveness of the proposed schemes.

A Cooperative Positioning Enhancement for Self-Powered Vehicular Networks With V2I Trustworthy Data Sharing

The emergence of connected vehicles (CV) and cooperative vehicle-infrastructure systems (CVIS) have paved the way for new innovative prospects for improving the safety and efficiency of transportation systems. Unleashing the communication and positioning capabilities of connected vehicles and roadside units’ storage and computing capabilities, the CVIS can enhance the positioning signal coverage and improve the positioning accuracy of the vehicles with low positioning accuracy by sharing the positioning error correction model. In particular, based on the particle swarm optimization algorithm, we first design an improved deep neural network to train the positioning error model. Second, the selection crossover factors of the genetic algorithm are used to optimize the network. Then, based on the consensus algorithms, the positioning error model is shared and stored in the blockchain network to ensure the security of vehicles and roadside units that provide positioning correction information. Finally, the trained network is used to predict and correct the positioning errors of other vehicles with low positioning accuracy. Additionally, the proposed method’s performance and effectiveness in terms of accuracy, timeliness, and security are verified in different scenarios.

A Machine Learning-Based Interest Flooding Attack Detection System in Vehicular Named Data Networking

A vehicular ad hoc network (VANET) has significantly improved transportation efficiency with efficient traffic management, driving safety, and delivering emergency messages. However, existing IP-based VANETs encounter numerous challenges, like security, mobility, caching, and routing. To cope with these limitations, named data networking (NDN) has gained significant attention as an alternative solution to TCP/IP in VANET. NDN offers promising features, like intermittent connectivity support, named-based routing, and in-network content caching. Nevertheless, NDN in VANET is vulnerable to a variety of attacks. On top of attacks, an interest flooding attack (IFA) is one of the most critical attacks. The IFA targets intermediate nodes with a storm of unsatisfying interest requests and saturates network resources such as the Pending Interest Table (PIT). Unlike traditional rule-based statistical approaches, this study detects and prevents attacker vehicles by exploiting a machine learning (ML) binary classification system at roadside units (RSUs). In this connection, we employed and compared the accuracy of five (5) ML classifiers: logistic regression (LR), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and Gaussian naïve Bayes (GNB) on a publicly available dataset implemented on the ndnSIM simulator. The experimental results demonstrate that the RF classifier achieved the highest accuracy (94%) in detecting IFA vehicles. On the other hand, we evaluated an attack prevention system on Python that enables intermediate vehicles to accept or reject interest requests based on the legitimacy of vehicles. Thus, our proposed IFA detection technique contributes to detecting and preventing attacker vehicles from compromising the network resources.

A systematic review of congestion control in internet of vehicles and vehicular ad hoc networks: Techniques, challenges, and open issues

SummaryThe Internet of Vehicles (IoV) integrates various technologies to enable real‐time data exchange between vehicles, pedestrians, roadside units, unmanned aerial vehicles, road‐added things, and so forth. IoV is an improved internet‐enabled vehicular ad hoc network (VANET), which is similarly affected by data traffic aggravated by time‐variant network density and the network's topological and informational changes. A high density of simultaneous vehicular communication leads to channel saturation and data congestion, which leads to rapid increases in data loss, packet latency, and lower service quality. Due to the importance of real‐time data communications in high dynamic networks, we used the systematic literature review (SLR) approach to identify 54 articles up to May 2023 to discuss the ways to deal with data traffic congestion in VANETs and IoV networks as well as existing and upcoming issues in this area.

Privacy Issues in Vehicular Ad-hoc Networks: A Review

Vehicle Ad-hoc Network (VANET) is a type of wireless network that enables communication between vehicles and Road Side Units (RSUs) to improve road safety, traffic efficiency, and service delivery. However, the widespread use of vehicular networks raises serious concerns about users’ privacy and security. Privacy in VANET refers to the protection of personal information and data exchanged between vehicles, RSUs, and other entities. Privacy issues in VANET include unauthorized access to location and speed information, driver and passenger identification, and vehicle tracking. To ensure privacy in VANET, various technologies such as pseudonymization, message authentication, and encryption are employed. When vehicles frequently change their identity to avoid tracking, message authentication ensures messages are received from trusted sources, and encryption is used to prevent unauthorized access to messages. Therefore, researchers have presented various schemes to improve and enhance the privacy efficiency of vehicle networks. This survey article provides an overview of privacy issues as well as an in-depth review of the current state-of-the-art pseudonym-changing tactics and methodologies proposed.