Vehicular Communications最新文献

In response to the escalating challenges posed by traffic bottlenecks, accidents, and intersection delays, the deployment of platooning mechanisms emerges as an imperative remedy. This investigation revolves around a pivotal scenario wherein a closely-knit platoon of cooperative vehicles approaches an intersection. Orchestrated by a lead vehicle, this convoy navigates the traffic light to expedite the journey of its followers towards their respective destinations. However, given that the size of the platoon can be increased, it will become difficult for the leader to manage it smoothly. Therefore, dividing the platoon into small groups led by co-leaders who deal directly with the leader is considered an ideal solution to speed up and facilitate the process of maintaining platoon stability. In this paper, we propose a Cooperative Adaptive Platooning Control Algorithm (CAPCA) for the hybrid communication topology in platooning. In fact, CAPCA aims to achieve stability in the platoon by distributing tasks in a parallel manner to mini-platoons. Moreover, inherent complexities arise as select members within the platoon strive to undermine its stability. Within this context, the research addresses the intricacies of Vehicle Platooning Disruption (VPD) attacks, a menacing phenomenon characterized by deliberate efforts to destabilize or seize control of a platoon. These attacks manifest through tactics such as false data injection (FDI) and replaying of control messages. In response, a robust and multifaceted countermeasure is conceptualized. The Vehicle Platooning Disruption Attacks Detection Protocol (VPD-ADP) takes center stage as a foundational component. By identifying instability within the platoon's dynamics model, VPD-ADP lays the essential groundwork for mitigating the repercussions of FDI and replay attacks. Employing advanced stochastic time series analysis, the impact of VPD attacks is scrutinized via anomalies in the Cartesian coordinates of vehicles deviations from the trajectory anticipated by the platoon. Moreover, the study introduces the Reputation-based Reliable Mitigation Protocol (RRMP), a pioneering approach that leverages collaborative data gleaned from neighboring vehicles. RRMP is devised to assess the veracity of received messages and gauge their reliability in real-time. Through extensive simulations and experiments, the proposed approach's effectiveness in fortifying the resilience of vehicle platooning systems against an array of disruption attacks is convincingly demonstrated.

The vehicular task offloading technology enhances the transportation system by offloading tasks of task-requesting vehicles (TRV) to either vehicular fog nodes (VFN) or road-side units (RSU) using single-hop or multi-hop communication. Due to the limited availability of VFN/RSU within a single-hop of TRV, a multi-hop path is employed for offloading tasks from TRV to VFN/RSU. However, the multi-hop path selection approaches have several issues, such as frequent re-connections due to varying vehicle speed, lower successful offloading when task deadline is missed, increased outage time when no VFN/RSU is within communication range of TRV, and inefficient vehicle selection to offload task leading to longer path lifetime. To address these issues, we have proposed a novel approach called Time-Computation-Variance based deadline sensitive path selection (TCV-D), which considers contextual information from k-hop neighbors. The approach offers four offloading modes: Direct mode, RSU mode, VFN mode, and Search mode, depending on the availability of RSUs/VFNs in single and multi-hop scenarios. To ensure tasks are delivered within deadlines, the proposed approach executes tasks by task forwarding vehicle or neighbor of task forwarding vehicle instead of designated VFN/RSU if delivering the task directly to the destination would exceed the deadline constraint. Extensive result analysis demonstrates substantial improvements compared to the existing k-hop-limited offloading time-based path selection (k-hop-limited-OTS) approach, including a 60% reduction in re-connections, a 35% decrease in path life time, a 30% decrease in outage time, and an 84% increase in successful offloading ratio, approximately.

The ever-evolving technology has given rise to a dynamic and intricate Radar environment, profoundly impacting warfare. Repeater jammers pose a significant threat to Radar systems by re-transmitting intercepted Radar signals with added noise or false information, thereby adding false targets in Radar detection process. Traditional Radar waveform designs often struggle to counter such jammers due to their adaptive and sophisticated nature. To address this challenge, this paper proposes utilizing a set of waveform during a coherent processing interval (CPI) which help cancel out any waveform received out of order. This requires a set of waveform which are orthogonal to each other in delay and Doppler. To design such a set of waveform, this paper proposes leveraging reinforcement learning (RL). The process involves offline training on simulated Radar environments, learning from the consequences of actions, and gradually improving its decision-making capabilities to generate effective anti-jamming waveform. This study presents experimental results demonstrating the effectiveness of the proposed approach in enhancing Radar system performance by mitigating the impact of Repeater jammers. Simulation results show that the proposed set of waveform have improved auto-correlation peak sidelobes as compared to Barker code and random sequences, while reducing cross-correlation sidelobes as compared to Gold codes and Golay codes.

As a promising distributed learning paradigm, Federated Learning (FL) is expected to meet the ever-increasing needs of Machine Learning (ML) based applications in Intelligent Transportation Systems (ITS). It is a powerful tool that processes the large amount of on-board data while preserving its privacy by locally learning the models. However, training and transmitting the model parameters in vehicular networks consume significant resources and time, which is not suitable for applications with strict real-time requirements. Moreover, the quality of the data, the mobility of the participating vehicles, as well as their heterogeneous capabilities, can impact the performance of FL process, bringing to the forefront the optimization of the data selection and the clients resources. In this paper, we propose CRAS-FL, a Clustered Resource-Aware Scheme for FL in Vehicular Networks. The proposed approach bypasses (1) communication bottlenecks by forming groups of vehicles, where the Cluster Head (CH) is responsible of handling the communication and (2) computation bottlenecks by introducing an offloading strategy, where the availability of the extra resources on some vehicles is leveraged. Particularly, CRAS-FL implements a CH election Algorithm, where the bandwidth, stability, computational resources, and vehicles topology are considered in order to ensure reliable communication and cluster stability. Moreover, the offloading strategy studies the quality of the models and the resources of the clients, and accordingly allows computational offloading among the group peers. The conducted experiments show how the proposed scheme outperforms the current approaches in the literature by (1) reducing the communication overhead, (2) targeting more training data, and (3) reducing the clusters response time.

Advancements in technology and the adoption of innovative developments have significantly simplified our lives. One notable innovation in the networking domain is Software Defined Networking (SDN), which revolutionizes the network layer by enabling centralized network administration and re-programmability. SDN technology finds application in diverse fields such as Vehicular Ad-hoc Networks (VANETs), Wireless Sensor Networks (WSNs), Internet of Things (IoT) communications, and cloud-fog computing. The integration of SDN technology into VANET systems has notably improved network performance but has also raised security concerns. To tackle this issue, this paper focuses on devising an authentication mechanism to facilitate secure communication across various VANET levels by establishing a shared session key. Moreover, the research proposes a method to mitigate the challenge of single points of failure, a typically difficult issue to address. To ensure the security of all confidential information during protocol execution, simulations are conducted using the Scyther tool. Additionally, an informal security analysis is performed, demonstrating the robustness of the proposed protocol. The proposed protocol also performs outstanding in terms of performance results

With the evolution of fifth generation (5G) of mobile communication, vehicular edge computing (VEC) and moving small cell (MSC) network are gaining attention because of their capability to provide improved quality-of-service (QoS) to vehicular users. The ultimate goal of VEC-enabled MSC network is to diminish the vehicular penetration effect and path loss resulting in improved network performance for vehicular users. In this paper, we explore distributed resource block (RB) allocation for fronthaul links of MSCs with probabilistic mobility in VEC-enabled MSC network. The proposed work exploits the computational power of road side units (RSUs) deployed with VEC servers present along the road sides to allocate the resources to MSCs in a distributed manner by exchanging limited information, with the objective of maximizing the data rate achieved by MSC network. Moreover, we propose federated learning (FL)-based position prediction of MSCs to predict the trajectory of MSCs in advance for efficient prediction of resource allocation in MSC network. Simulations results are presented to compare the position prediction dependent distributed and centralized time interval dependent interference graph-based resource allocation to MSCs in terms of RB utilization and average achievable data rate of MSC network. For comparison, we further investigated centralized as well as distributed threshold time dependent interference graph-based allocation of resources to MSC network.

In fog-based VANETs, fog nodes are close to the network edge that analyze data uploaded by vehicles, and then provide value-added services. It is suitable to adopt data sharing schemes to provide value-added services. However, applying existing data sharing schemes directly for service provision in fog-based VANETs has limitations in terms of security and efficiency. Therefore, we propose a dynamic secure service provision scheme in fog-based VANETs called DSSPS. Firstly, we construct an efficient subscription permission authentication mechanism that allows vehicles to request service from different fog nodes after a single subscription at TA. Secondly, a subgroup selecting algorithm is proposed, which effectively reduces the decryption overhead of vehicles. Formal security proof demonstrates DSSPS is secure. At the same time, DSSPS achieves better performance in terms of computation and communication costs.

As one of the most promising industries, consumer-grade Unmanned Aerial Vehicles (UAVs), also known as drones, have changed our lives. Although significant progress in drones has been made, adversary impersonation attacks still pose severe risks to flying drones. In addition, authorized pilot miss-operations also has become a critical factor leading to drone flight accidents. To validate the pilot's legal status and remind the authorized pilot about their miss-operations, we propose a multi-task learning-based drone pilot identification and operation evaluation scheme named MTL-PIE. Specifically, we first present qualitative and quantitative guidelines to evaluate pilot operation proficiency. Then, we design a pilot identification module and an operation evaluation module to resist pilot impersonation attacks and assess pilot operation proficiency, respectively. Finally, we propose a soft-parameter sharing mechanism to transfer knowledge between two modules and a dynamic weight-adjusting algorithm to prevent domain-dominant problems. Numerical results show that MTL-PIE can verify pilot legal status with an accuracy of 95.36% (outperforming our previous work with a margin of 2%-3%) and act as assessors to evaluate pilot operation proficiency with an accuracy of 94.47%. Note that MTL-PIE needs only 35 ms to verify pilot legal status and assess pilot operation proficiency; it has great potential to reduce drone flight accidents.

Electric vehicles (EV) are gradually replacing traditional fuel-based vehicles to reduce carbon emissions and protect the human living environment. EVs can be easily integrated into the smart grid infrastructure, and achieve the rapid demand response of power exchange in the vehicle-to-grid (V2G) ecosystem, and achieve optimal energy allocation. However, it is a challenge to simultaneously solve the security, privacy and efficiency of communication in V2G. To meet this challenge, we propose a three-party key agreement protocol based on Physical Unclonable Function (PUF). The proposed protocol just uses a small amount of XOR operations, PUF functions, and hash functions with low computation cost. It is a truly lightweight and efficient three-party key agreement protocol. It realizes the privacy protection of one-time random pseudonym under the condition that the server only needs to store two pseudonyms. And it also provides a supplementary sub-protocol for pseudonym error conditions to achieve high robustness of the authentication system. Strict security formal proof shows the proposed protocol meets various security requirements. Compared with similar protocol, the proposed protocol reduces the total communication cost by 73% and the computing cost by 79%.

The crowdsourcing paradigm has been used for last-mile delivery applications to offer cost-efficient delivery through crowdsourced vehicles. The increasing demand from users for fast delivery led to the adoption of Unmanned Aerial Vehicles (UAVs) due to their speed and cost, for instance, in peak hours. However, existing frameworks do not offer a comprehensive solution that utilizes vehicles and UAVs for transparent, timely, and cost-efficient delivery. This paper proposes a novel supply chain management framework on a consortium blockchain with last-mile delivery through crowdsourced vehicles and UAVs. The blockchain-based framework aims to provide last-mile delivery for time-critical tasks in a transparent and cost-efficient manner. A blockchain-hosted Gale-Shapley Matching mechanism is designed for allocating delivery tasks to UAVs and vehicles that will perform them timely and cost-efficiently. The allocation mechanism is incorporated in the proposed management framework consisting of smart contracts that autonomously respond to members' interactions. In addition, the framework stores the supply chain process data transparently and immutably. The performed experiments explore the feasibility of the proposed solution showing the increase in task allocation percentage (at least 6%) and reward (at least 50%) while reducing the delivery time (at least 23%) compared to the considered benchmarks.