Vehicular Communications最新文献

Due to its controllable maneuverability, wide coverage, and low cost, unmanned aerial vehicle (UAV) has great potential in post-disaster rescue, cargo transport and emergency communication. Considering its limited onboard energy, energy-efficient UAV communication is a challenge. This research examines the security of simultaneous wireless information and power transfer (SWIPT) systems assisted by intelligent reflecting surfaces (IRS) and UAVs while considering the flight energy of rotary-wing UAVs. Specifically, an IRS is mounted on a UAV to enhance the quality of legitimate transmission, and artificial noise (AN) is introduced into the base station (BS) to reduce eavesdropping quality. The power splitting (PS) technology is adopted at ground devices (GDs) to simultaneously decode information and harvest energy. First, we jointly design the BS transmit beamforming, UAV-IRS phase shifts and trajectory/velocity as well as GDs PS ratio with the aim of maximizing the sum secrecy rate of all GDs. Then, an iterative algorithm is developed to address the formulated problem. In particular, additional variables are introduced to handle this complicated objective function, and the original problem is decoupled into multiple sub-problems, which can be solved alternately by invoking the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques. Finally, numerical results demonstrate that the proposed scheme exhibits a substantial performance in the security rate of SWIPT systems assisted by UAV-IRS, and its performance is improved by at least 12% compared to benchmark schemes at the flight energy budget $e_{thr}=5KJ$ and the number of reflecting elements $N^r=25$.

The concepts of future mobility such as autonomous, connected, electric and shared vehicles are bringing a huge revolution to the automotive sector. We are seeing technologies typical from data centers fully embedded into vehicles, shifting from a mechanical-centric product, to an electronics-centric one. All the sensors and actuators embedded in vehicles need to exchange data in real time, in a safe and reliable way. As a result, the field of in-vehicle network (IVN) processing is currently an active research area. In previous work, we derived the requirements of future vehicle network processors and analyzed the state of the art of network processing platforms. From our study we concluded that there is currently no solution available capable of fulfilling all the requirements with the right level of performance. Now, in this work, we evaluate the novel Elastic Gateway (eGW) architecture which aims at fulfilling this gap, advancing towards future Gateway System/Network on Chip (SoC/NoC) solutions. Elastic Gateway SoC concept aims at synthesizing a scalable and future proof architecture embracing all new and already established functions and features demanded in a zonal gateway controller for the new era of mobility. It is composed of a set of configurable IP cores that allow for a full HW-based datapath implementation targeting good enough Quality of Service (QoS) and the minimum possible latency. We provide details of the internal architecture and how the different technologies required in future IVNs are integrated in eGW. With this, we are able to show how eGW meets the requirements of future network processing devices, enabling thus the current revolution.

Intelligent assisted driving is an important application in vehicular edge computing networks (VECNs). In the intelligent transportation system (ITS), a group of moving vehicle users need to be coordinated to complete complex vehicular applications. A number of dependent, latency-sensitive, and computation-intensive tasks are generated. However, the existing works have given less consideration to the dependencies among both vehicle users and the subtasks in vehicle, which makes it a huge challenge to complete tasks timely. When interdependent tasks come from different vehicle users, a special task preparation time is needed, which can disrupt the ongoing task processing. Furthermore, the high mobility of vehicles directly affects the data transmission rate. To address the mentioned challenges, we design an efficient mobility and dependency-aware task offloading strategy in VECNs. The objective is to minimize both the overall system task completion delay and the economic cost. We take into account the real-time locations and task preparation time of vehicle users. Additionally, we propose a multi-decision-making offloading algorithm (MDOA) that primarily analyzes the processing priorities for both vehicle users and subtasks. In order to integrate practical applications, the financial expenses of vehicle users are also considered as an indispensable part. As a result, we propose an efficient two-step task offloading algorithm. Through numerous simulation examples, we demonstrate the efficiency and high performance of the proposed task offloading strategies in VECNs when compared to existing algorithms.

Congestion is caused by the continually expanding number of vehicles worldwide. Intelligent Transportation Systems (ITSs) could be used to build a system that efficiently uses the existing infrastructure and current technological advancements. Several Vehicular applications use the restricted vehicle battery in V2X systems, and a key concern is communication secrecy. Earlier research has focused on channel measurement, autonomous piloting, high-speed transmissions, secretive communications, and QoS analysis but battery-limited V2X has received far less attention. Therefore, resolving the abovementioned complications in the current model is imperative. Thus, a new V2X communication model is developed based on NB-IoT (NarrowBand Internet of Things) approaches. NB -IoT is utilized in the V2X communication model to offer low-cost, high-range, and minimal power to the communication environment. The objective of the proposed NarrowBand of Vehicular Things Communication (NBVTCS)System is to analyze the performance metrics of vehicular communication including energy efficiency rate with different SNR, bit error rate with different vehicular positions, and overall throughput of the system. The simulation was carried out to attain an effective energy efficiency rate, the system's transmit power, bit error rate, and overall system throughput. The proposed vehicular architecture also optimized the abovementioned vehicular metrics with the help of the proposed Hybrid Pelican Beetle Swarm Optimization (HPBSO) approach to achieve a higher accuracy in communication parameters. Finally, the simulation outcome demonstrates that the developed vehicular model is achieved with a minimal energy consumption rate and maximum throughput and also compares the proposed HPBSO technique with other optimization techniques including DOX, EFO, POA, and BSO.

In this work, we investigate a longitudinal platooning control problem of heterogeneous vehicles with a focus on external unknown disturbances, parameter uncertainties and intermittent communications. When vehicle platooning encounter intermittent communications, the performance of the platooning will be degraded. Nevertheless, the existing researches can not deal with the aforementioned three issues effectively. To this end, a novel nonsingular dynamic terminal sliding-mode control (NDTSMC) law is contrived. First, for a heterogeneous cooperative adaptive cruise control (CACC) or adaptive cruise control (ACC) platooning system of mixed vehicles, a hybrid mathematical reference model is developed. Then, we propose a CACC-ACC switched approach which activates either a CACC mode or an enhanced ACC mode relied on communication reliability. The unknown disturbances and parameter uncertainties can be together served as a unknown lumped matched (or mismatched) disturbance, depending on the circumstances. The unknown lumped matched (or mismatched) disturbance can be estimated by a finite-time disturbance observer (FTDO). Based on the observation, a novel switched controller consisting of a baseline controller part and an observation-based NDTSMC law part is proposed. Furthermore, combined with Lyapunov stability theory, it can be demonstrated that the stability of the string of mixed vehicles in the heterogeneous platoon can be robustly guaranteed after switching. Simulation examples show that the proposed approach exhibits satisfactory control properties for addressing intermittent communications. The convincing performances for attenuating parameter uncertainties and external unknown disturbances are achieved, which are also shown in simulations.

In Internet of Things (IoT) systems, sensor nodes are frequently placed in remote and unattended locations to monitor environmental data. One significant challenge is ensuring the timely and efficient transmission of data generated by these sensor nodes back to the base station. The use of unmanned aerial vehicles (UAVs) can provide a practical solution to this challenge by acting as mobile relay nodes for facilitating data transmission. In most existing works, UAVs are typically restricted to collecting data within their designated areas and returning to the base station for data offloading, resulting in suboptimal data timeliness due to long-distance flights. A limited number of works have explored the utilization of relay collaboration by UAVs for data collection, enabling efficient and immediate transmission of sensor node data to the base station. Nevertheless, UAVs positioned at significant distances from the base station face challenges in obtaining timely energy replenishment. This makes them unable to effectively support long-duration data collection missions. In order to tackle these challenges, we develop a UAV-aided IoT collaborative data collection mechanism and propose a matching games-based data collection (MGDC) scheme. In this scheme, we begin by identifying convergence nodes within the ground sensor network, responsible for uploading sensor-generated data to passing UAVs. Furthermore, we divide the mission area into multiple subareas based on the number of available UAVs. Subsequently, using a matching game algorithm, we establish relay relationships between UAVs to enable efficient relay transmissions among paired UAVs. To achieve efficient data collection of UAVs, we employ an improved adaptive large neighborhood search (IALNS) algorithm for UAV flight path planning. Finally, we incorporate an alternating charging mode to ensure all UAVs have the opportunity to return to the base station for energy recharge. Through comprehensive experimentation, we confirm the significant enhancement provided by our proposed data collection scheme compared to existing schemes. This scheme effectively reduces system age of information (AoI) and extends the runtime of the system.

The advent of the next generation of connected and autonomous cars offers immense opportunities for both users as well as service providers. In particular, fiber-wireless (FiWi) based vehicle-to-infrastructure (V2I) network can facilitate some of the stringent requirements of sixth-generation (6G) vehicular networks, including higher capacity, lower delay, and ubiquitous connectivity. FiWi based V2I network integrates the next generation passive optical network 2 (NG-PON2) with IEEE 802.11p based V2I network. In this work, we first review the various kinds of vehicular data traffic and their desired key performance indicators (KPIs), namely throughput, delay, and reliability. Depending on the KPI requirements, the V2I traffic is classified among four classes and assigned to different transmission containers (T-CONTs) of the optical network unit (ONU). Further, in order to minimize the delay of the network, we propose a machine learning (ML) based T-CONT priority assignment wavelength allocation algorithm that minimizes the number of wavelength switching instances in the PON. The performance of the proposed ML-based wavelength allocation algorithm is compared with the other approaches, namely random and equal T-CONT based wavelength allocation algorithms. Simulation results demonstrate the efficiency of the proposed algorithm vis-a-vis other approaches in terms of end-to-end (e2e) delay, throughput, and reliability.

This work presents a resource management framework for optimizing the sum-rate in a sparse code multiple access (SCMA)-assisted UAV downlink system. We formulate two optimization problems for maximizing the overall sum-rate: the first problem addresses UAV 3D deployment and trajectory optimization with energy constraints, while the second focuses on optimizing SCMA subcarrier and power allocation optimization, subject to factor graph matrix (FGM) constraints and a minimum user data rate. Since the optimization problems are non-convex, the complexity of finding the global optimal solutions is prohibitive. We propose a gradient ascent-based iterative algorithm to compute the optimal UAV 3D deployment and trajectory. Further, an effective channel state information-based algorithm is proposed for FGM assignment, followed by a Lagrange dual decomposition method to solve the power allocation problem efficiently. Our research findings demonstrate that the optimization of the UAV trajectory gives improved sum-rate within the specified energy budget. Further, employing CSI-based multiple subcarrier allocation and strategic power allocation can significantly improve system performance compared to the benchmark schemes.

In Location-Based Service (LBS), the vehicle user sends query messages, such as the nearest health centre, restaurant, etc., to the service provider for efficient and accurate service. The service provider gathers data regarding the received query, takes the service charge from the vehicle user, and provides these services efficiently and accurately. The service provider may reveal the vehicle user's privacy if the service provider is malicious. Similarly, if the vehicle user is malicious, he/she may also try to opt for the service from the service provider without paying the service charges. Therefore, vehicle users' queries and the service provider's data privacy are serious concerns in LBS. Some schemes, such as k-anonymity, cryptographic algorithms, etc., have been proposed to overcome these issues. The k-anonymity technique only offers vehicle users' query privacy. Cryptographic algorithms such as homomorphic encryption, oblivious transfer, etc., provide vehicle users' query and the service provider's data privacy at high communication and computation costs but reveal the vehicle user's identity. To overcome these problems, this work proposes an Anonymous and Linkable Ring Signcryption (ALRS) scheme for LBS in VANETs. It provides the service provider's data privacy and the vehicle user's query privacy, keeping the vehicle user's identity anonymous from the service provider. In addition, it maintains linkability (the number of times the same user requests a query) without revealing the identity of the vehicle user. Implementation and detailed results show that the ALRS scheme performed better than the current scheme regarding communication and computation costs and preserves all the privacy requirements.

The future of mobility is cooperative, connected, and autonomous leading to new technological challenges in the development of Cooperative Intelligent Transport Systems (C-ITS). Therefore, Vehicle to Everything (V2X) and, more specifically, Vehicle to Infrastructure (V2I) deployments are key to enabling these features around the highways as well as along the cities. The communication range of the RoadSide Units (RSUs) is one of the most important aspects when implementing Vehicle-to-Infrastructure (V2I) communications as it has a direct impact on efficiency and the economy of the installation. The aim is to maximise the communication range with the minimum number of RSUs and to optimise the deployments, thus having a realistic simulation tool is key. To be realistic, simulations rely on adequate propagation models, which ideally would adapt to the environment without a high computational need. Therefore, an appropriate characterisation of the different V2X environments as well as a simple and versatile propagation model is an important instrument for deciding the location of the RSUs. In this paper, we characterise different environments for ITS-G5 communications and provide an adjusted propagation model with an α parameter that depends on the environment. Thus, eradicating the need to model the environment and the obstacles in it. For that purpose, a methodology for the modeling and characterisation of the ITS-G5 propagation model is proposed,after that the methodology is applied and the results validated. The methodology is presented and the characterisation of the ITS-G5 environments is made. Later, tests were carried out in different environments to measure how the signal power decreases with the distance. After that, the propagation model for ITS-G5 communications, specifically V2I communications, is presented along with the methodology applied to obtain it. Then, an α value is assigned to each environment. Finally, the validation is made by comparing our adjusted propagation model with other propagation models and applying the adjusted propagation model to a new RSU installation.