Vehicular Communications最新文献

The future of autonomous transportation systems depends on energy sustainability and secure information exchange from low-power vehicular sensors, hence the increased interest in vehicular sensor charging using simultaneous wireless information and power transfer (SWIPT). This study investigates the physical layer security of a SWIPT-based radio frequency energy harvesting cooperative vehicular relaying network subjected to cascade Nakagami-m and double Nakagami-m (DN) fading channels. In the considered system model, a stationary source communicates with a mobile destination through a power-splitting-based decode-and-forward relay in the presence of a mobile passive eavesdropper. Based on the Gamma-distributed first term of the Laguerre series, new statistical probability density function (PDF) and cumulative distribution function (CDF) expressions for the DN are derived to accurately model the complex cascaded fading scenario. The secrecy performance metrics analyzed are the secrecy outage probability (SOP), the probability of non-zero secrecy capacity (PNZSC), and the intercept probability (IP). In addition, the asymptotic SOP (ASOP) is investigated in the high signal-to-noise ratio (SNR) to enhance the comprehension of the secrecy performance. Based on the derived ASOP, the secrecy diversity order (SDO) of the proposed system is determined and examined. Particularly, we present analytical closed-form expressions for the secrecy performance metrics and provide a detailed understanding of the impact of the system parameters under the cascade fading scenario. Then, a power splitting (PS) optimization problem is formulated to minimize the SOP. The results demonstrate a reduction in the SOP with the proposed PS scheme compared to the equal PS scheme. The obtained analytical findings are validated using Monte Carlo simulations.

Communication is essential for Cooperative Intelligent Transportation Systems (C-ITS) to achieve better road efficiency, especially for Autonomous Intersection Management (AIM) which coordinates vehicles to pass the intersection safely and efficiently. Communication delays cause severe safety crises (i.e., collisions) and vehicular-performance degradation regarding intersection capacities and vehicular delays. Targeting the delays, network researchers have been working on low-latency communication technologies, and C-ITS researchers have proposed delay-tolerant AIM systems to avoid collisions in intersections. The impacts of communication delays are observed and discussed in the literature; however, models and assessments of the delay requirements for AIM are needed to provide insights for future network and C-ITS research. Here, we model the impact of communication delays on vehicular performance at an autonomous intersection and validate the models with the simulation results from over two million experiments, two types of multi-lane intersections (a typical 4-legged intersection and a roundabout), and four AIM systems. The simulations are conducted with SUMO simulator and AIM systems, where communication delays are inserted into the message exchanges during the simulation. The models are represented in linear, quintic, and cubic polynomials, showing that communication delay between 0 to 100 milliseconds is linearly related to vehicular performance in terms of intersection capacity and vehicular delay. According to the models, we show that by reducing communication delay from 100 to 10 milliseconds, the capacity degradation can be reduced from 7-10% to 0.7-1.0%. Moreover, communication delays must be less than 247 milliseconds to allow AIM systems to outperform traditional traffic lights.

IoT-based UAV networks comprise interconnected UAVs outfitted with sensors and microcontrollers to simplify data exchange in environments such as smart cities. In light of open-access communication landscapes, IoT-based UAV networks could pose security challenges, encompassing authentication vulnerabilities and the inadvertent disclosure of location and other confidential information to unauthorised parties. Henceforth, we have proposed a lightweight and secure authentication protocol: Hyperelliptic Curve and Fuzzy Extractor based Authentication in IoT-based UAV networks (HCFAIUN) leveraging Hyperelliptic Curve Cryptography(HCC), Fuzzy Extractor (FE), XOR operations and hash functions. HCC's maximum key size is 80 bits, differing from the 160-bit requirement of the elliptic curve, making it apt for UAVs with limited resources. The proposed scheme utilises biometrics traits of users to avoid exposing data from stealing smart devices using FE. This protocol facilitates the mutual authentication of users and UAVs, allowing them to exchange a session key for secure communication. The Hyperelliptic Curve (HC) scalar multiplication protects the user's private key from attackers, even in public channels. The obfuscation identity of the user and UAVs generated through the hash function and timestamp makes the external user and UAV anonymous. The efficacy of this proposed framework is examined using the Scyther verification tool and Random oracle model-based formal analysis, and informal analysis is also discussed, which validates its robustness against well-known potential physical and logical attacks. The performance analysis shows that the HCFAIUN scheme has lower computation, communication, and storage costs, i.e., 3.832 ms and 1456 bits and 1128 bits, respectively, compared to existing schemes.

This study proposes an integrated system that combines energy harvesting (EH) enabled unmanned aerial vehicles (UAVs) with non-orthogonal multiple access (NOMA) to enhance communication system performance within a cellular network. Addressing the limitations of existing analyses that often assume an infinite blocklength scenario, we explore EH-enabled UAV-NOMA systems within a cellular framework under a finite blocklength (FBL) scenario. The study investigates the complex interactions and advantages resulting from the integration of EH, NOMA, and UAV technologies, aiming to assess whether EH can sustain communication within this framework. The network model considers base stations (BSs), UAVs, and terrestrial devices distributed with independent Poisson point processes (PPPs) over a large area. In this network, BSs employ NOMA to serve cell center devices directly, while cell edge devices, which are nor in direct contact with BS, are served via simultaneous wireless information and power transfer (SWIPT) enabled UAVs. The study derives metrics including joint harvesting and decoding probability for a randomly selected UAV, coverage probability (CP) for cell devices, and end-to-end block error rate (BLER) probabilities for typical device pairs. The findings demonstrate that the proposed scheme effectively supplies all the necessary transmit power for communication purposes through EH, achieving reasonable reliability. Additionally, the study highlights the importance of considering a combination of blocklengths from different phases to achieve optimal performance, rather than solely relying on an increment in blocklength. Finally, the effects of parameter variations on network performance are examined.

With the prevalence of intelligent driving, the vehicular data corresponding to driving safety and traffic management efficiency is widely applied by the Internet of Vehicles (IoV) applications. Vehicular data is shared frequently in IoV, leading to privacy leakage of the message sender, yet most privacy-preserving measures bring difficulties for receivers to detect malicious messages. To trade-off between privacy and security, conditional privacy-preserving authentication (CPPA) solutions have been proposed. However, CPPA protocols deployed in IoV rely on hardware devices or center servers to manage key generation and updates. This paper proposed a blockchain-based CPPA mechanism for IoV data-sharing to mitigate these challenges. A hierarchical key generation mechanism is presented to protect drivers' privacy and authenticate messages which is suitable for resource-limited IoV nodes. Management nodes can issue temporary pseudo-identity (PID) from their keys for vehicles to interact in their area and trace the malicious behaviors, but know nothing about vehicles' activities outside their administration. A hierarchical and zonal blockchain is presented to realize distributed fine-grained IoV management and enhance efficiency concerning traditional blockchain. Specifically, we propose a cross-domain data-sharing mechanism, which can facilitate efficient communication and a mutual cross-domain chain verification to guarantee the security of each domain blockchain in our IoV system. The security analysis and performance evaluation demonstrate the security as well as computational and storage efficiency of our scheme.

The wireless signal that intentionally disrupts the communication is described as the jamming signal. Clustered jamming is the use of jamming signals of the devices that are clustered in groups, whereas non-clustered jamming refers to the use of the jamming signals of the spatially distributed devices that are un-clustered. The efficiency of the unmanned aerial vehicle (UAV)-assisted cellular networks compromises in the presence of clustered as well as non-clustered jammers. Furthermore, the UAV's antenna 3D beam-width vibrates due to strong atmospheric wind, atmospheric pressure, or mechanical noise influencing UAV-assisted networks' efficiency. Thus, the efficiency characterization of UAV-assisted networks considering jamming and beam-width variations is essential. This paper concentrates on the efficiency of the user equipment's connection with the line-of-sight (LOS) UAV, non-LOS UAV, and cellular base station in terms of association, coverage, and spectrum in the presence of clustered as well as non-clustered jammers and beam-width variations. For a network consisting of jammers and beam-width variations, the analytical expressions are derived to assess the user's association and coverage efficiency. The results show that the network's efficiency decreases drastically with the increasing beam-width variations. Moreover, the non-clustered jamming reduces the efficiency of the networks much more when compared with the clustered jamming. Therefore, to enhance the efficiency of the system; network designers need to consider implementing advanced anti-jamming techniques for a system employing non-clustered jamming and UAV antenna beam-width variations.

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.