Vehicular Communications最新文献

With the increasing prevalence of drones, guaranteeing their authentication and secure communication has become paramount in drone networks to mitigate unauthorized access and malicious attacks. Cross-domain authentication is crucial in the context of the Internet of Drones (IoD) for safely verifying and establishing trust between diverse drones and their respective control stations, which may belong to different regions or organizations. Effectively accessing resources or services in another domain while maintaining security and efficiency poses a significant challenge. Conventional authentication mechanisms relying on challenging problems like discrete logarithm and integer factorization might not be sufficient to guarantee the security and effectiveness of drone-based systems in the post-quantum era. To address this, we propose a distributed post-quantum cryptography and Physical Unclonable Function (PUF) based cross-domain authentication protocol for IoD. Key contributions of this work include the elimination of secret key storage on drones, mutual authentication, emphasis on hardware security, incorporation of post-quantum security measures, efficient cross-domain authentication and resilience against cyber attacks such as eavesdropping, impersonation, replay attack, untraceability, and PUF-modeling attack. The performance of the proposed protocol is assessed utilizing metrics like processing time, communication cost and storage utilization. In operations associated to the blockchain ledger, variables such as latency, throughput, CPU utilization, and memory utilization are also examined. The protocol shows a reduced computation time and zero sensitive data storage in drone memory, despite a slightly higher communication cost that is manageable with 5G-enabled drones. Comparative analysis against existing solutions in the domain highlights the superior security of the proposed protocol, positioning it as a promising solution for the evolving quantum landscape.

Communication reliability is one of the most important factors in ensuring the safe operation of unmanned aerial vehicles (UAVs). The reliability of wireless communication for a UAV swarm is typically characterized by the signal-to-interference-and-noise ratio (SINR). However, previous work on UAV swarm communication reliability did not use a comprehensive SINR model. In this paper, we derived two novel closed-form approximations – lognormal and generalized beta prime (GBP) - of UAV swarm communication reliability based on a comprehensive SINR model. The model includes shadowing, multipath fading, the dependence of fading on external factors, which are the probability of line-of-sight (LoS) and the physical environment, as well as all possible interference within the system, which may be from the UAVs and the ground control station (GCS). For typical swarm heights between 60 – 300 m and up to 32 UAVs, comparisons of the approximate and simulated reliabilities for UAV swarms with a radius up to a critical value (which increases with the number and transmit power of the UAVs) show that only the lognormal approximation is accurate for all uplink and downlink communications, up to a critical horizontal distance from the GCS (which increases with height). Benchmarking of the lognormal approximation shows that neglecting either shadowing or multipath fading leads to high approximation errors. An example of how the lognormal approximation can be used to evaluate and maintain the communication reliability of a UAV swarm during deployment is given. Furthermore, it can be used to derive other SINR-dependent performance metrics, such as ergodic capacity and symbol error rate, which are useful in UAV network design and monitoring.

In flying ad hoc networks (FANETs), unmanned aerial vehicles (UAVs) communicate with each other without any fixed infrastructure. Because of frequent topological changes, instability of wireless communication, three-dimensional movement of UAVs, and limited resources, especially energy, FANETs deal with many challenges, especially the instability of UAV swarms. One solution to address these problems is clustering because it maintains network performance and increases scalability. In this paper, a dynamic clustering scheme based on fire hawk optimizer (DCFH) is proposed for FANETs. In DCFH, each cluster head calculates the period of hello messages in its cluster based on its velocity. Then, a fire hawk optimizer (FHO)-based dynamic clustering operation is carried out to determine the role of each UAV (cluster head (CH) or cluster member (CM)) in the network. To calculate the fitness value of each fire hawk, a fitness function is suggested based on four elements, namely the balance of energy consumption, the number of isolated clusters, the distribution of CHs, and the neighbor degree. To improve cluster stability, each CH manages the movement of its CMs and adjusts it based on its movement in the network. In the last phase, DCFH defines a greedy routing process to determine the next-hop node based on a score, which consists of distance between CHs, energy, and buffer capacity. Finally, DCFH is simulated using the network simulator version 2 (NS2), and its performance is compared with three methods, including the mobility-based weighted cluster routing scheme (MWCRSF), the dynamic clustering mechanism (DCM), and the Grey wolf optimization (GWO)-based clustering protocol. The simulation results show that DCFH well manages the number of clusters in the network. It improves the cluster construction time (about 55.51%), cluster lifetime (approximately 11.13%), energy consumption (about 15.16%), network lifetime (about 2.6%), throughput (approximately 3.9%), packet delivery rate (about 0.61%), and delay (approximately 14.29%). However, its overhead is approximately 8.72% more than MWCRSF.

In today's rapidly evolving world, wireless communication has become a pervasive force, profoundly impacting various facets of our daily lives. Wireless Vehicular Networks stand out as a captivating realm of research, with a key focus on fostering information exchange among autonomous vehicles. As researchers witness surging demand in this domain, there is a growing emphasis on devising advanced techniques to augment network performance, particularly within the context of Fifth-generation (5G) applications, such as vehicular communication. The concept of Vehicle-to-vehicle (V2V) communications is poised to play a pivotal role in the future, presenting formidable challenges for the air interface by accommodating asynchronous multiple access and high mobility. Within this dynamic landscape, security and privacy issues loom large for 5G-enabled vehicle networks, many of which remain largely unexplored. The conventional waveforms, including Orthogonal Frequency Division Multiplexing (OFDM), may fall short of meeting these evolving standards. In this paper, authors delve into a comparative exploration of two waveform families, namely Filter Bank Multicarrier (FBMC) and Universal Filtered Multi-Carrier (UFMC), concerning their design and performance trade-offs. authors also examine their compatibility with various digital modulation schemes like 4-Quadrature Amplitude Modulation (QAM), 16-QAM, Offset Quadrature Phase Shift Keying (OQPSK), and Shaped offset OQPSK (SOQPSK). Through MATLAB simulations, our research vividly demonstrates the superior performance of UFMC when juxtaposed with OFDM and FBMC, especially concerning Bit Error Rate (BER) in both Rayleigh and Nakagami fading channels. In particular, authors consider a Nakagami shape parameter of 10, which yields a remarkable minimum BER for UFMC.

Ensuring the security and reliability of cooperative vehicle-to-vehicle (V2V) communications is an extremely challenging task due to the dynamic nature of vehicular networks as well as the delay-sensitive wireless medium. In this context, the moving target defense (MTD) paradigm has been proposed to overcome the challenges of conventional solutions based on static network services and configurations. Specifically, the MTD approach involves the dynamic altering of network configurations to improve resilience to cyberattacks. Nevertheless, the current MTD solution for cooperative networks poses several limitations, such as that they require high synchronization modules that are resource-intensive and difficult to implement; and they rely heavily on attack-defense models, which may not always be accurate or comprehensive to use. To overcome these challenges, the proposed approach introduces an adaptive defense strategy within the MTD framework. This strategy proposes an intelligent spatiotemporal diversification-based MTD scheme to defend against eavesdropping attacks in cooperative V2V networks. It involves altering the system configuration spatially through relay selection and adjusting the percentage of injected fake data over time. This approach aims to balance reducing intercept probability while ensuring high throughput. Our methodology involves modeling the configuration of vehicular relays and data injection patterns as a Markov decision process, followed by applying deep reinforcement learning to determine the optimal configuration. We then iteratively evaluate the intercept probability and the percentage of transmitted real data for each configuration until convergence is achieved. To optimize the security-real data percentage (S-RDP), we developed a two-agent framework, namely MTD-DQN-RSS & MTD-DQN-RSS-RDP. The first agent, MTD-DQN-RSS, tries to minimize the intercept probability by injecting additional fake data, which in turn reduces the overall RDP, while the second agent, MTD-DQN-RSS-RDP, attempts to inject a sufficient amount of fake data to achieve a target S-RDP. Finally, extensive simulation results are conducted to demonstrate the effectiveness of our proposed solution, which improved system security compared to the conventional relay selection approach.

Vehicular ad hoc networks (VANETs) have revolutionized communication between vehicles and infrastructure, notably enhancing traffic management and passenger safety. However, VANETs are vulnerable to security threats, especially regarding data authenticity. Aggregate signature is a powerful technique that reduces computational and communication burdens by aggregating multiple signatures from different signers into a single signature. Traditional aggregate signature schemes, based on large prime number decomposition and the discrete logarithm problem, cannot effectively resist quantum attacks. This paper introduces a novel quantum secure certificateless aggregate signature (QSCLAS) scheme designed to enhance data security and privacy in VANETs. Our proposed scheme employs the number theory research unit (NTRU) algorithm. As a lattice-based cryptographic algorithm, NTRU is renowned for its security against quantum computer attacks, making it an essential component of our quantum-secure solution. By eliminating the need for expensive bilinear pairing operations, our proposed scheme achieves high efficiency and practicality in resource-limited VANETs environments. The security analysis demonstrates our scheme's resilience against both Type-I and Type-II adversaries in the random oracle model under the small integer solution (SIS) problem on the NTRU lattice. Furthermore, compared with existing approaches, the results illustrate that our proposed scheme offers significant advantages in signature generation and verification cost, as well as lower transmission overhead than other lattice-based schemes, thereby making it highly suitable for VANETs environments.

As the number of in-vehicle electronic devices and system complexity increases, it drives the evolution of the in-vehicle electrical/electronic architecture (E/E). The most promising future architecture is the zone architecture divided by physical location. Zones are connected via high-speed Ethernet, and time-sensitive networking enhances Ethernet in terms of real-time and determinism. In service-oriented communication methods, there are issues with quality assurance and data inconsistency. This paper addresses these issues by combining traffic scheduling strategies in time-sensitive networking, proposing an algorithm for quality assurance and data consistency. Simulation results demonstrate that, under the premise of satisfying the quality of service, the data stream of the same service can reach multiple subscribers simultaneously. This further demonstrates the feasibility and effectiveness of the proposed method.

The recent development of the Internet of Vehicles (IoV) relies heavily on the secure store and analysis of reliable driving data. Blockchain technology has been widely used in the IoV field due to its security advantages, where consensus and query algorithms are two key modules determining its performance. However, existing solutions do not perform well enough in terms of delay performance, thus limiting their ability to support IoV environments with ultra-low-latency requirements. To address this challenge, we design a blockchain-based data storage architecture, focusing on the design of delay-aware consensus and data query algorithms. Specifically, to reduce the consensus delay, we propose a reputation-assisted and grouping-simplified Practical Byzantine Fault Tolerant (RGS-PBFT) algorithm. First, we develop a novel node credit value evaluation model by combining the behavior and configuration reputation to select the primary node with both high security and high computing capacity. Second, after determining the primary node, we reduce the communication complexity of traditional PBFT from $O\left(Z^2\right)$ to $O\left(Z^{\frac{4}{3}}\right)$ by establishing a grouping-simplified consensus structure. These contributions effectively reduce the consensus delay. To reduce the query delay and index construction time, we propose an index query algorithm based on the data tracking chain (DTC-Index query). By introducing the LevelDB database, it establishes a transaction index chain coupled to vehicle ID in the blockchain. This approach avoids the time-consuming block traversal during data querying. Experimental results demonstrate that the proposed algorithms outperform existing solutions, achieving significant improvements in both consensus delay and query time reduction. Notably, our solutions offer exceptional delay performance, making them well-suited for IoV environments.

To solve the privacy leakage problem faced by Internet of Vehicles (IoV) users when enjoying location-based services (LBS), a privacy-protecting predictive cache method based on blockchain and machine learning (BML-PPPCM) is proposed. First, a Bi-directional Long-Short Term Memory (Bi-LSTM) model is used to predict query requests over a future period based on historical request information. The predicted results are recommended to neighbors and broadcast to requestors. Then, deep Q-learning (DQN) is utilized to determine the optimal cache decision. Finally, a trust mechanism is introduced to calculate trust values, and blockchain is used to store transaction data and trust data, preventing malicious tampering by attackers. The simulation results show that BML-PPPCM has a higher cache hit ratio than other similar schemes and performs well in privacy protection and suppression of malicious and incentive denial of service providers.

To cope with safety risks and operational efficiency problems, it is of paramount importance to ensure high data rates and meet the latency requirements in Connected and Autonomous Vehicles. The problem in such environments is two-fold: 1. Heavy load on the network due to increasing demands; 2) Resource imbalance, due to variations in the vehicular traffic density in certain regions. The consequences of these two phenomena may lead to service disruptions, as well as the fairness of resource allocation across vehicles. In this work, we propose a resource allocation method that distributes high workload among edge nodes and allocates network resources efficiently and fairly. Performance of the proposed method is evaluated under realistic scenarios, and compared to the state-of-the-art approaches in the literature. Behavior and performance of all methods in overload conditions in certain regions were analyzed. Simulation results exhibit a 38% improvement in the successful demand rate and a 48% improvement in capacity usage compared to state-of-the-art approaches.