Vehicular Communications最新文献

As the automotive industry advances towards greater automation, the proliferation of electronic control units (ECUs) has led to a substantial increase in the connectivity of in-vehicle networks with the external environment. However, the widely used Controller Area Network (CAN), which serves as the standard for in-vehicle networks, lacks robust security features, such as authentication or encrypted information transmission. This poses a significant challenge to the security of these networks. Despite the availability of powerful intrusion detection methods based on machine learning and deep learning, there are notable limitations in terms of stability and accuracy in the absence of a supervised learning process with labeled data. To address this issue, this paper introduces a novel in-vehicle intrusion detection system, termed IDS-DEC. This system combines a spatiotemporal self-coder employing LSTM and CNN (LCAE) with an entropy-based deep embedding clustering. Specifically, our approach involves encoding in-vehicle network traffic into windowed messages using a stream builder, designed to adapt to high-frequency traffic. These messages are then fed into the LCAE to extract a low-dimensional nonlinear spatiotemporal mapping from the initially high-dimensional data. The resulting low-dimensional mapping is subjected to a dual constraint in conjunction with our entropy-based pure deep embedding clustering module. This creates a bidirectional learning objective, addressing the optimization problem and facilitating an end-to-end training pattern for our model to adapt to diverse attack environments. The effectiveness of IDS-DEC is validated using both the benchmark Car Hacking dataset and the Car Hacking-Attack & Defense Challenge dataset. Experimental results demonstrate the model's high detection accuracy across various attacks, stabilizing at approximately 99% accuracy with a 0.5% false alarm rate. The F1 score also stabilizes at around 99%. In comparison with unsupervised methods based on deep stream clustering, LSTM-based self-encoder, and classification-based methods, IDS-DEC exhibits significant improvements across all performance metrics.

The increasing number of connected and automated vehicles has led to a sharp increase in the demand for network access of moving vehicles. Although 5G networks support terminals with high mobility, the traffic load is too heavy to bear if all the vehicles have a large amount of data for transmission. Therefore, IEEE 802.11-based wireless network is a complementary offload solution to provide high-speed network access for vehicles with low cost, easy deployment and high scalability. However, frequent network handover of moving vehicles between multiple roadside access points (APs) results in network performance degradation, which is one of the challenges in vehicular communications. In this paper, we propose a framework (referred to as ASAP) based on the up-to-date IEEE 802.11ax standard to provide moving vehicles with seamless handover between multiple APs. By leveraging the high efficiency (HE) sounding protocol of IEEE 802.11ax, each AP is capable to monitor the current location of moving vehicles in real time. In addition, a mechanism is also proposed for AP uplink/downlink transmissions through collaboration between the APs and the backbone network to achieve seamless handover for moving vehicles. Since ASAP is based on IEEE 802.11ax, the compatible security scheme such as IEEE 802.11i can be applied to ASAP for security enhancement. The proposed solution does not require any modification on the user terminals, making it possible to be implemented in practice. Extensive simulations show that ASAP significantly reduces the network handover delay to microsecond level, and improves network throughput up to 59% compared with the state-of-the-art methods.

Vehicular edge computing networks (VECNs) can provide a promising solution to support efficient task execution of vehicles. Consider the channel and access time variations caused by the high mobility of vehicles in a vehicular environment when designing task offloading strategies in VECNs. In this paper, we perform multi-path offloading for a task vehicle with serial tasks based on both dynamic communication distances of vehicle-to-infrastructure (V2I) links, that of vehicle-to-vehicle (V2V) links, and slowly varying large-scale fading information of wireless channels. Considering the task vehicle's low delay requirements, our goal is to minimize the maximum task completion time of the task vehicle. A multi-path dynamic offloading scheme (MPDOS), composed of three parts, is proposed to achieve maximum delay minimization. The maximum processing capability of links between a task vehicle and roadside units (RSUs) is first taken as the objective to find the required communication links, which can decrease the total processing time by increasing transmission rate and execution capacity. Then, a task allocation scheme based on a multi-knapsack algorithm matches tasks and RSUs. Finally, a balancing scheme is leveraged to provide load-balancing computing performance across all computation devices. Numerical results show that our proposed scheme outperforms 30.7% of the RA algorithm, and the task completion rate can reach 99.55%.

Nowadays, unmanned aerial vehicles (UAVs) organized in a flying ad hoc network (FANET) can successfully carry out complex missions. Due to the limitations of these networks, including the lack of infrastructure, wireless communication channels, dynamic topology, and unreliable communication between UAVs, cyberattacks, especially wormholes, weaken the performance of routing schemes. Therefore, maintaining communication security and guaranteeing the quality of service (QoS) are very challenging. In this paper, a novel Q-learning-based secure routing scheme (QSR) is presented for FANETs. QSR seeks to provide a robust defensive system against wormhole attacks, especially wormhole through encapsulation and wormhole through packet relay. QSR includes a secure neighbor discovery process and a Q-learning-based secure routing process. Firstly, each UAV gets information about its neighboring UAVs securely. To secure communication in this process, a local monitoring system is designed to counteract the wormhole attack through packet relay. This system checks data packets exchanged between neighboring UAVs and defines three rules according to the behavior of wormholes. In the second process, UAVs perform a distributed Q-learning-based routing process to counteract the wormhole attack through encapsulation. To reward the safest paths, a reward function is introduced based on five factors, the average one-hop delay, hop count, data loss ratio, packet transmission frequency (PTF), and packet reception frequency (PRF). Finally, the NS2 simulator is applied for implementing QSR and executing different scenarios. The evaluation results show that QSR works better than TOPCM, MNRiRIP, and MNDA in terms of accuracy, malicious node detection rate, data delivery ratio, and data loss ratio. However, it has more delay than TOPCM.

The Intelligent Transportation Systems (ITS) is a leading-edge, developing idea that seeks to revolutionize how people and things move inside and outside cities. Internet of Vehicles (IoV) forms a networked environment that joins infrastructure, pedestrians, fog, cloud, and vehicles to develop ITS. The IoV has the potential to improve transportation systems significantly, but as it is networked and data-driven, it poses several security issues. Numerous solutions to these IoV issues have recently been put forth. However, significant computing overhead and security concerns afflict the majority of them. Moreover, malicious vehicles may be injected into the network to access or use unauthorized services. To improve the security of the IoV network, the Mayfly algorithm is used to optimize the private keys continuously. To address these difficulties, we propose a novel VESecure system that provides secure communication, mutual authentication, and key management between vehicles, roadside units (RSU), and cloud servers. The scheme undergoes extensive scrutiny for security and privacy using the Real-or-Random (ROR) oracle model, Tamarin, and Scyther tools, along with the informal security analysis. An Objective Modular Network Testbed in OMNet++ is used to simulate the scheme. We prove our scheme's efficiency by comparing it with other existing methods regarding communication and computation costs.

Vehicular Ad Hoc Networks (VANETs) offer a promising solution to bring drivers comfortable driving experiences and also improve road safety in intelligent transportation systems, but also faces many security issues. Collusive attack is one of the most challenging threats in VANETs because it violates the fundamental assumption made by VANET-based applications that all received information be correct and trustworthy. Collusive attackers can not only generate and send false or forged messages, but also purposely manipulate the reputation value of normal or malicious vehicular nodes. To address these issues, we analyze the behaviors characteristics of collusive attacks and propose a generic, lightweight, and fully distributed detection scheme against collusive attacks in VANETs. This scheme integrates two methods to identify different collusive attacks for fraud reputation and fraud message, respectively, as well as an incentive method to restrain collusive nodes. Simulation-based experiments are conducted and the results illustrate the superiority of the proposed security scheme over state-of-the-art methods.

With the generation of massive traffic information in the smart transportation system, the traffic control center efficiently utilizes broadcast communication to send multiple messages to multiple vehicles. Besides, diversified privacy disclosure and security attack issues also emerged spontaneously. To achieve secure communication between the traffic control center and vehicles in the smart transportation system, we design a broadcast signcryption scheme with equality test in the smart transportation system based on the certificateless cryptosystem and equality test. The scheme realizes message confidentiality and vehicle privacy by using the Lagrange interpolation theorem to encrypt messages and vehicle identities, while also achieving classify ciphertext by using the equality test and facilitate road traffic information management. Through numerical experiment analysis, the proposed work has higher operation efficiency and is more suitable for application in smart transportation systems.

Analyzing vehicular ad hoc networks (VANETs) poses a considerable challenge due to their constantly changing network topology and scarce network resources. Furthermore, defining suitable routing metrics for adaptive algorithms is a particularly hard task since these adaptive decisions should be taken according to the current conditions of the VANET. The literature contains different approaches aimed at optimizing the usage of wireless network resources. In a previous study, we introduced an analytical model based on a straightforward Markov reward chain (MRC) to capture transient measurements of the idle time of the link formed between two VANET nodes, which we denote as $T_{idle}$. This current study focuses on modeling and analyzing the influence of $T_{idle}$ on adaptive decision mechanisms. Leveraging our MRC models, we have derived a concise equation to compute $T_{idle}$. This equation provides a quick evaluation of $T_{idle}$, facilitating quick adaptive routing decisions that align with the current VANET conditions. We have integrated our $T_{idle}$ evaluation into multihop routing protocols. We specifically compare performance results of the 3MRP protocol with an enhanced version, I3MRP, which incorporates our $T_{idle}$ metric. Simulation results demonstrate that integrating $T_{idle}$ as a decision metric in the routing protocol enhances the performance of VANETs in terms of packet losses, packet delay, and throughput. The findings consistently indicate that I3MRP outperforms 3MRP by up to 50% in various scenarios across high, medium, and low vehicular densities.

The automotive field is undergoing significant technological advances, which includes making the next generation of autonomous vehicles smarter, greener and safer through vehicular networks, which are often referred to as Vehicle-to-Everything (V2X) communications. Together with V2X, centralized maneuver management services for autonomous vehicles are increasingly gaining importance, as, thanks to their complete view over the road, they can optimally manage even the most complex maneuvers targeting L4 driving and beyond. These services face the challenge of strictly requiring a high reliability and low latency, which are tackled with the deployment at orchestrated Multi-Access Edge Computing (MEC) platforms. In order to properly manage safety-critical maneuvers, these services need to receive a large amount of data from vehicles, even though the useful subset of data is often related to a specific context on the road (e.g., to specific road users or geographical areas). Decoding and post-processing a large amount of raw messages, which are then for the most part filtered, increases the load on safety-critical services, which should instead focus on meeting the deadlines for the actual control and management operations. On this basis, we present an innovative open-source, 5G & MEC enabled service, called Server Local Dynamic Map (S-LDM). The S-LDM is a service that collects information about vehicles and other non-connected road objects using standard-compliant messages. Its primary purpose is to create a centralized dynamic map of the road that can be shared efficiently with other services managing L4 automation, when needed. By doing so, the S-LDM enables these services to widely and precisely understand the current situation of sections of the road, offloading them from the need of quickly processing a large number of messages. After a detailed description of the service architecture, we validate it through extensive laboratory and pilot trials, involving the MEC platforms and production 5G networks of three major European network operations and two Stellantis vehicles equipped with V2X On-Board Units (OBUs). We show how it can efficiently handle high update rates and process each messages in less than few tenths of microseconds. We also provide a complete scalability analysis with details on deployment options, providing insights on where new instances should be created in practical 5G-based V2X scenarios.

The Internet of Vehicles (IoV) for fog computing (FC) addresses issues such as traffic congestion, transportation efficiency, and privacy. Non-orthogonal multiple access (NOMA) is a popular technology that enhances spectral efficiency and increases the network's access capability. The synchronisation between NOMA and FC radio access networks extends the application of augmented or vehicular networking and other promising uses. However, with the rapid increase in user vehicles and mobile data, the existing IoV has not succeeded in meeting the real-world and dependable communication needs of modern intelligent transportation due to its limited flexibility. To overcome this, we propose a user grouping-based hybrid optimistic framework for resource allocation in NOMA-based FC vehicular networks (FCVR), named the gradient average subtraction-based optimisation (GASBO). Initially, the NOMA-based FCVR is simulated. User grouping is performed based on GASBO using the signal-to-interference-plus-noise ratio and user distance. Finally, resource allocation is achieved using the proposed GASBO, which combines gradient descent optimisation and average subtraction-based optimisation. The analytic measures obtained for energy efficiency, throughput, sub-channel utility, capacity, and penalty function are 5,366,844,362.870 bits/joule, 883.411 Mbps, 82.031, 2316.337, and 0.011, respectively.