Vehicular Communications最新文献

Vehicles of today are composed of over 100 electronic embedded devices known as Electronic Control Units (ECU), each of which controls a different component of the vehicle and communicates via the Controller Area Network (CAN) bus. However, unlike other network protocols, the CAN bus communication protocol lacks security features, which is a growing concern as more vehicles become connected to the Internet. To enable the detection of intrusions on the CAN bus, numerous intrusion detection systems (IDS) have been proposed. Although some are able to achieve high accuracy in detecting specific attacks, no IDS has been able to accurately detect all types of attacks against the CAN bus. To overcome the aforementioned issues, we propose a multimodal analysis framework named CANival, which consists of time interval-based and signal-based analyzers developed by designing a novel Time Interval Likelihood (TIL) model and optimizing an existing model CANet. Experimental results show that our multimodal IDS outperforms the base models and enhances the detection performance testing on two recent datasets, X-CANIDS Dataset and SynCAN, achieving average true positive rates of 0.960 and 0.912, and true negative rates of 0.997 and 0.996, respectively.

Due to the high mobility, high chance of line-of-sight (LoS) transmission, and flexible deployment, unmanned aerial vehicles (UAVs) have been used as mobile edge computing (MEC) servers to provide ubiquitous computation services to mobile users (MUs). However, the limited energy storage, caching capacity, and computation resources of UAVs bring new challenges for UAV-aided MEC, e.g., how to recharge UAVs and how to jointly optimize service-caching, computation-offloading, and UAVs flight trajectories. To overcome the above-mentioned difficulties, in this paper we study the joint optimization for service-caching, computation-offloading, and UAVs flight trajectories for UAV-aided MEC, where multiple rechargeable UAVs cooperatively provide MEC services to a number of MUs. First, we formulate an energy minimization problem to minimize all MUs' energy consumptions by taking into account the mobility of MUs and the energy replenishment of UAVs. Then, using the hierarchical multi-agent deep reinforcement learning (HMDRL), we develop a two-timescale based joint service-caching, computation-offloading, and UAVs flight trajectories scheme, called HMDRL-Based SCOFT. Using HMDRL-Based SCOFT, we derive UAVs' service-caching policies in each time frame, and then derive UAVs flight trajectories and MUs' computation-offloading in each time slot. Finally, we validate and evaluate the performances of our proposed HMDRL-Based SCOFT scheme through extensive simulations, which show that our developed scheme outperforms the other baseline schemes to converge faster and greatly reduce MUs' energy consumptions.

In this paper, we consider a multi-tier cellular network in which a hovering Unmanned Aerial Vehicle (UAV) assists the network in the absence of the terrestrial Macrocell base station. The orthogonal sub channels are assumed for communication between the UAV and its attached users. The Femtocell users and Device-to-Device (D2D) pairs transmit their data to the corresponding receivers in the same sub-channels. Achieving the outage probability of the ground users, is a challenge for the operators considering the dominant small scale and large scale fading over the channels, Line-of-Sight and None-Line-of-Sight conditions together. The mentioned problem becomes worse in the presence of cross-tier interferences. We investigate the outage probability of the ground UAV users to evaluate the performance of the network. Due to intractability of the calculations to derive the exact outage probability, the closed-form expressions are derived for the upper bound of outage probability under Rayleigh and Nakagami-m fading. The effect of UAV altitude, density of D2Ds and corresponding transmission powers are discussed. The results verify the simulations and confirm that the proposed approach outperforms the existing upper bound methods.

Physical layer security (PLS) aims to ensure the confidentiality and authenticity of transmitted data by capitalizing on the inherent randomness of wireless channels. Owing to the popularity of intelligent transportation systems (ITSs), PLS research has sparked renewed interest in the wireless research community. This paper investigates the performance of secure communication in the context of a vehicle-to-vehicle (V2V) communication scenario by employing a reconfigurable intelligent surface (RIS). Further, we introduce the concept of non-orthogonal multiple access (NOMA) to reduce latency and improve communication efficiency in V2V networks. This study aims to comprehensively analyze secrecy performance, encompassing parameters like average secrecy capacity (ASC), secrecy outage probability (SOP) and probability of non-zero secrecy capacity (PNZSC). Our research aims to highlight the efficacy of RIS in providing secure and reliable communication within V2V NOMA networks. Ultimately, our study contributes to advancing secure communication protocols in intelligent transportation systems.