Vehicular Applications Research Articles

Compact Multichannel Filter With Improved Bandwidth for Vehicular Communication System Application

Compact Multichannel Filter With Improved Bandwidth for Vehicular Communication System Application

Learning-based cooperative content caching and sharing for multi-layer vehicular networks

Caching and sharing the content files is critical and fundamental for various future vehicular applications. However, how to satisfy the content demands in a timely manner with limited storage is an open issue owing to the high mobility of vehicles and the unpredictable distribution of dynamic requests. To better serve the requests from the vehicles, a cache-enabled multi-layer architecture, consisting of a Micro Base Station (MBS) and several Small Base Stations (SBSs), is proposed in this paper. Considering that vehicles usually travel through the coverage of multiple SBSs in a short time period, the cooperative caching and sharing strategy is introduced, which can provide comprehensive and stable cache services to vehicles. In addition, since the content popularity profile is unknown, we model the content caching problems in a Multi-Armed Bandit (MAB) perspective to minimize the total delay while gradually estimating the popularity of content files. The reinforcement learning-based algorithms with a novel Q-value updating module are employed to update the caching files in different timescales for MBS and SBSs, respectively. Simulation results show the proposed algorithm outperforms benchmark algorithms with static or varying content popularity. In the high-speed environment, the cooperation between SBSs effectively improves the cache hit rate and further improves service performance.

Numerical studies on structure optimization and flow characteristics of a hydrogen recirculation ejector under multiple load conditions

Ejectors are ideal hydrogen recirculation devices for PEMFC systems but typically exhibit poor recirculation performance at low loads. For purpose of extending ejector's operating range in vehicular applications, the entraining performance across multiple loads should be focused. In this study, A 40 kW ejector was designed with a 1D theory and optimized using a 2D CFD model. Simulations were conducted to study the influence of various diameter and axial dimensions on the ejector's performance and flow characteristics. The results reveal that as the load increases from 20% to 100%, the suboptimal diffuser exit diameter decreases from 2.5 to 1.5 times the mixing chamber diameter, the optimal nozzle exit position increases from 0.59 to 0.63 times, and the optimal mixing chamber length increases from 2.2 to 4.3 times. A convergent nozzle with a short throat significantly improves low-load entraining performance and achieves the highest performance growth in the variable-nozzle design. Additionally, an optimization method for axial dimensions based on the proposed parameter flow uniformity was established, allowing for precise determination of the optimal dimensions through a few simulations.

A multiple-input fluid queue model for performance evaluation of fog server in an intelligent vehicular network

Nowadays, there is a growing trend towards fog computing which is a relatively new concept that has been introduced as an extension of cloud computing. It is considered as a promising paradigm for intelligent vehicular networks due to its ability to reduce delays and enhance network efficiency. The utilization of fog computing plays a crucial role in tackling the distinct obstacles encountered in vehicular computing, including issues like real-time data processing, and limitations on bandwidth. By relocating computational resources closer to the network’s edge, it enhances the effectiveness, dependability, and safety of vehicular applications while simultaneously boosting privacy and security measures. In addition, fog offloading has played a pivotal role in fog computing and edge computing structures. Its objective is to efficaciously allocate the data and assign the processing of tasks among edge devices, fog nodes, and cloud resources. Efficient fog offloading strategies are adaptable, scalable, and are able to handle faults, making them indispensable for optimizing these architectures. Although efficiency and quality of service are crucial objectives in a fog computing based intelligent vehicular network environment, performance remains a significant concern that cannot be overlooked. In light of this, to evaluate the efficacy of the probabilistic offloading approach on a fog server, a thorough performance evaluation is necessary. This research proposes a fluid queue approach that accounts for the constant flow of data packets while evaluating a fog server’s performance. For a fog computing-based intelligent vehicular network (FCIVN) with numerous heterogeneous smart vehicles (SVs), we construct a multiple-input fluid queue to model the tasks handed over to the fog server in order to evaluate its performance. In an FCIVN, the arrivals are drawn from various sources at the fog server. Accordingly, in the proposed model the fluid queue is modulated by more than one independent and distinct finite-state birth–death processes (BDPs) which control the variable inflow, and another BDP which controls the variable outflow. We present the details concerning an intermediate fog server’s buffer occupancy distribution within an FCIVN. Further, we assess the performance measures in terms of the offered load, expected buffer-level, average throughput and average latency of the tasks in an FCIVN. Finally, quantitative illustrations are presented to demonstrate the appropriateness of the fluid queue model developed in this study. The results are found to be in consistence with the expected behaviour of these performance indicators.

A general task offloading and resources allocation strategy for multi-RSUs with load unbalance and priority awareness

Vehicular Edge Computing is a new computing paradigm that enables real-time response to vehicular applications and servers by performing data processing on edge computing devices near the vehicle. However, on the one hand, the random distribution and the mobility of vehicles may lead to load unbalance among different Roadside Units (RSUs), and some tasks may not be able to get timely response due to inadequate computing resources and communication resources in the high-load RSU areas. On the other hand, considering the different urgency of the tasks, the service quality of the system will be seriously affected if these tasks are not treated indistinguishably. To address the above challenges, this paper constructs a priority-aware task offloading and computing&communication resources allocation problem in a general scenario of unbalanced load among multi-RSUs, aiming at minimizing the average delay. In the problem, considering the absence of communication resources, the relay vehicle is used to offload the subtasks of splittable tasks to the RSUs that are in the neighbouring and low-load. Moreover, to take full advantage of computing resources, the task can be reasonably split into at most four parts and processed in parallel on a relay vehicle, a current RSU, a neighbouring RSU and a local vehicle. To solve the problem, a Split-Hop Offloading and Resources Allocation Strategy (SHORAS) based on an improved particle swarm optimisation algorithm is proposed, which uses a penalty function to incline resources towards high priority tasks. Simulation results show that SHORAS improves 24% in terms of the total system delay and effectively reduces the processing delay in the high-load areas compared to other strategies, while ensuring the delay requirements of high priority tasks.

Fuzzy-based task offloading in Internet of Vehicles (IoV) edge computing for latency-sensitive applications

As vehicular applications continue to evolve, the computational capabilities of individual vehicles alone are no longer sufficient to meet the increasing demands. This has led to the integration of edge computing in the Internet of Vehicles (IoV) as an essential solution. Due to the limited resources within vehicles, there is often a need to offload tasks to edge nodes. However, task offloading in IoV environments presents several challenges, including high mobility, dynamic network topology, and varying node density. Traditional offloading methods fail to effectively address these challenges. Moreover, tasks differ in importance, necessitating a mechanism for edge nodes to prioritize tasks based on their urgency. To overcome these challenges, we propose a Vehicle-to-Vehicle (V2V) fuzzy-based task offloading scheme. In this scheme, fuzzy logic plays a critical role by enabling dynamic prioritization of tasks based on their urgency and the available computational resources at edge nodes, ensuring intelligent, context-aware decision-making. The user vehicle selects an appropriate edge node using an edge selection mechanism, which guarantees prolonged connection time and sufficient computational resources. Tasks at the edge are then organized based on their latency requirements and evaluated using a fuzzy rule-based inference system. Our simulation results demonstrate improved task execution rates, reduced overall system delay, and minimized queuing delays.

A multi-objective hierarchical deep reinforcement learning algorithm for connected and automated HEVs energy management

Connected and autonomous vehicles have offered unprecedented opportunities to improve fuel economy and reduce emissions of hybrid electric vehicle (HEV) in vehicular platoons. In this context, a hierarchical control strategy is put forward for connected HEVs. Firstly, we consider a deep deterministic policy gradient (DDPG) algorithm to compute the optimized vehicle speed using a trained optimal policy via vehicle-to-vehicle communication in the upper level. A multi-objective reward function is introduced, integrating vehicle fuel consumption, battery state-of-the-charge, emissions, and vehicle car-following objectives. Secondly, an adaptive equivalent consumption minimization strategy is devised to implement vehicle-level torque allocation in the platoon. Two drive cycles, HWFET and human-in-the-loop simulator driving cycles are utilized for realistic testing of the considered platoon energy management. It is shown that DDPG runs the engine more efficiently than the widely-implemented Q-learning and deep Q-network, thus showing enhanced fuel savings. Further, the contribution of this paper is to speed up the higher-level vehicular control application of deep learning algorithms in the connected and automated HEV platoon energy management applications.

A Comprehensive Review of Energy-Efficient Techniques for UAV-Assisted Industrial Wireless Networks

The rapid expansion of the Industrial Internet-of-Things (IIoT) has spurred significant research interest due to the growth of security-aware, vehicular, and time-sensitive applications. Unmanned aerial vehicles (UAVs) are widely deployed within wireless communication systems to establish rapid and reliable links between users and devices, attributed to their high flexibility and maneuverability. Leveraging UAVs provides a promising solution to enhance communication system performance and effectiveness while overcoming the unprecedented challenges of stringent spectrum limitations and demanding data traffic. However, due to the dramatic increase in the number of vehicles and devices in the industrial wireless networks and limitations on UAVs’ battery storage and computing resources, the adoption of energy-efficient techniques is essential to ensure sustainable system implementation and to prolong the lifetime of the network. This paper provides a comprehensive review of various disruptive methodologies for addressing energy-efficient issues in UAV-assisted industrial wireless networks. We begin by introducing the background of recent research areas from different aspects, including security-enhanced industrial networks, industrial vehicular networks, machine learning for industrial communications, and time-sensitive networks. Our review identifies key challenges from an energy efficiency perspective and evaluates relevant techniques, including resource allocation, UAV trajectory design and wireless power transfer (WPT), across various applications and scenarios. This paper thoroughly discusses the features, strengths, weaknesses, and potential of existing works. Finally, we highlight open research issues and gaps and present promising potential directions for future investigation.

Generalized Adaptive Spreading Modulation: A Novel Waveform for Integrated Sensing and Communication Oriented Vehicular Applications

Generalized Adaptive Spreading Modulation: A Novel Waveform for Integrated Sensing and Communication Oriented Vehicular Applications

Dependency-aware online task offloading based on deep reinforcement learning for IoV

The convergence of artificial intelligence and in-vehicle wireless communication technologies, promises to fulfill the pressing communication needs of the Internet of Vehicles (IoV) while promoting the development of vehicle applications. However, making real-time dependency-aware task offloading decisions is difficult due to the high mobility of vehicles and the dynamic nature of the network environment. This leads to additional application computation time and energy consumption, increasing the risk of offloading failures for computationally intensive and latency-sensitive applications. In this paper, an offloading strategy for vehicle applications that jointly considers latency and energy consumption in the base station cooperative computing model is proposed. Firstly, we establish a collaborative offloading model involving multiple vehicles, multiple base stations, and multiple edge servers. Transferring vehicular applications to the application queue of edge servers and prioritizing them based on their completion deadlines. Secondly, each vehicular application is modeled as a directed acyclic graph (DAG) task with data dependency relationships. Subsequently, we propose a task offloading method based on task dependency awareness in deep reinforcement learning (DAG-DQN). Tasks are assigned to edge servers at different base stations, and edge servers collaborate to process tasks, minimizing vehicle application completion time and reducing edge server energy consumption. Finally, simulation results show that compared with the heuristic method, our proposed DAG-DQN method reduces task completion time by 16%, reduces system energy consumption by 19%, and improves decision-making efficiency by 70%.

An innovative 3D wave channel design for boosting the dynamic response of proton exchange membrane fuel cell stack

The dynamic response of proton exchange membrane fuel cell (PEMFC) is a crucial metric for evaluating these systems in vehicular applications. The forced convection effect can significantly enhance the dynamic response capabilities of proton exchange membrane fuel cells. In this study, we propose the application of the forced convection effect within a parallel flow field. This approach retains the advantages of low pressure drop at the inlets and outlets of the parallel flow field and facilitates rapid distribution of reaction gases, while simultaneously enhancing the dynamic response of actual PEMFC stacks without compromising drainage efficiency. we propose an innovative 3D wave channel design for PEMFC. Specifically, we design and assemble a stack optimized for performance. The proposed design is tested against the traditional 2D straight channel stack, with the 3D wave channel stack demonstrating an increase in peak power density by 8.05% to 33.92% across a relative humidity range of 20% to 100%. This novel design outperforms the 2D straight channel design in terms of gas distribution uniformity, and enhancedmass transfer to the diffusion layer, as well as reduction of concentration polarization in the PEMFC. It also enhances self-humidification under low humidity conditions. Most importantly, the 3D wave channel stack exhibits a superior dynamic response, with voltage fluctuations after current loading being 74.36% to 91.15% lower than those of the 2D straight channel stack. Furthermore, the 3D wave channel stack maintains superior voltage stability under starvation conditions. The findings from this study contribute significantly to optimizing the dynamic response capability of PEMFC systems, highlighting the potential of the 3D wave channel design in enhancing PEMFC performance.

Low-latency intelligent service combination caching strategy with density peak clustering algorithm in vehicle edge computing

In the dynamic field of Vehicle Edge Computing (VEC), the demand for intelligent vehicular systems to process vast amounts of data is escalating, driven by advancements in autonomous driving and real-time navigation technologies. Optimizing service latency and minimizing transmission costs are crucial for enhancing the performance of vehicular networks. Traditional service caching strategies, which largely rely on the popularity of individual services, often fail to account for the intricate interdependencies between services. The oversight can result in redundant data transfers and inefficient use of storage resources. In response, our paper introduces a novel approach to service combination caching within a heterogeneous computational framework comprising vehicles, edge servers, and the cloud. Our strategy focuses on minimizing user wait times and data transmission costs during task execution, while adhering to the caching budget constraints of service providers. Key contributions include the development of an Improved Density Peak Clustering (IDPC) algorithm to facilitate cooperative clustering among edge servers and the design of a Service Combination Caching Strategy (SCCS). The SCCS approach reduces caching costs by categorizing servers, forming efficient clusters, and strategically allocating storage. Simulation results demonstrate that the method outperforms existing strategies by significantly decreasing task execution delays and transmission costs, thereby greatly enhancing the quality of service in vehicular applications.

TM–IoV: A First-of-Its-Kind Multilabeled Trust Parameter Dataset for Evaluating Trust in the Internet of Vehicles

The emerging and promising paradigm of the Internet of Vehicles (IoV) employ vehicle-to-everything communication for facilitating vehicles to not only communicate with one another but also with the supporting roadside infrastructure, vulnerable pedestrians, and the backbone network in a bid to primarily address a number of safety-critical vehicular applications. Nevertheless, owing to the inherent characteristics of IoV networks, in particular, of being (a) highly dynamic in nature and which results in a continual change in the network topology and (b) non-deterministic owing to the intricate nature of its entities and their interrelationships, they are susceptible to a number of malicious attacks. Such kinds of attacks, if and when materialized, jeopardizes the entire IoV network, thereby putting human lives at risk. Whilst the cryptographic-based mechanisms are capable of mitigating the external attacks, the internal attacks are extremely hard to tackle. Trust, therefore, is an indispensable tool since it facilitates in the timely identification and eradication of malicious entities responsible for launching internal attacks in an IoV network. To date, there is no dataset pertinent to trust management in the context of IoV networks and the same has proven to be a bottleneck for conducting an in-depth research in this domain. The manuscript-at-hand, accordingly, presents a first of its kind trust-based IoV dataset encompassing 96,707 interactions amongst 79 vehicles at different time instances. The dataset involves nine salient trust parameters, i.e., packet delivery ratio, similarity, external similarity, internal similarity, familiarity, external familiarity, internal familiarity, reward/punishment, and context, which play a considerable role in ascertaining the trust of a vehicle within an IoV network.

Design of high flexible multi-band conformal printed patch antenna for sub 6 GHz 5G-based vehicular communication

ABSTRACT Vehicular communications (VCs) play a major role in intelligent transportation systems (ITS). In addition to preventing unintentional collisions and traffic jams, this system can lower fuel and energy usage in the transportation system. The Internet of Vehicles emerges by integrating the IoT with vehicles for smooth communication. In IoV, vehicular communications may be from vehicle to infrastructure (V2I), vehicle to vehicle (V2V), vehicle to pedestrian (V2P), and vehicle to network (V2N) or vehicle to roadside unit (V2R). The antenna used in vehicular communication has some problems, such as less efficiency and high return loss. The frequency response in the existing methods should not give sufficient data for selecting a better band. To avoid this kind of issue and improve the performance by selecting a better frequency band, this work presents a highly flexible multi-band conformal printed patch antenna for sub-6 GHz 5 G-based vehicular communication application. The substrate used to design the antenna is polyimide, and the microstrip patch antenna is designed with cross-shaped slots. The multi-band circularly polarised antenna is designed to operate in vehicular communication bands such as 2.4 GHz, 4.7 GHz, 5.3 GHz, and 5.9 GHz. The proposed work is simulated in HFSS, and the results of directivity, gain, reflection coefficient, Voltage Standing Wave Ratio (VSWR), radiation pattern, axial ratio, Left-Hand Circular Polarisation (LHCP), Right-Hand Circular Polarization (RHCP) and surface current distribution are evaluated for the conformal antenna.

A Comprehensive Review of Recent Developments in VANET for Traffic, Safety & Remote Monitoring Applications

Strategic integration of Wireless Sensor Networks (WSNs) and IoT (Internet of Things) into VANET infrastructure is crucial for ensuring vehicular safety, mobility management, and vehicular applications. The integration collects information on traffic and road conditions without relying on traditional internet connectivity. It also addresses applications such as early warnings in areas with limited coverage, safety and health emergency messages in highly congested zones, and air monitoring without depending on traditional TCP/IP internet connectivity. This article provides a comprehensive view of network technologies, data acquisition devices, clustering techniques, and energy-efficient routing protocols to optimize Vehicle-to-Everything (V2X) communications in VANETs. This study also addresses how to leverage the frequency channels of the 802.11p protocol and expands the possibilities for developing numerous applications dedicated to remote ambient, traffic and safety monitoring without compromising network performance. Moreover, the recent developments of clustering algorithms and energy-efficient schemes for these VANET applications are analyzed from a novel perspective.

Joint computation offloading and resource allocation for end-edge collaboration in internet of vehicles via multi-agent reinforcement learning

Vehicular edge computing (VEC), a promising paradigm for the development of emerging intelligent transportation systems, can provide lower service latency for vehicular applications. However, it is still a challenge to fulfill the requirements of such applications with stringent latency requirements in the VEC system with limited resources. In addition, existing methods focus on handling the offloading task in a certain time slot with statically allocated resources, but ignore the heterogeneous tasks’ different resource requirements, resulting in resource wastage. To solve the real-time task offloading and heterogeneous resource allocation problem in VEC system, we propose a decentralized solution based on the attention mechanism and recurrent neural networks (RNN) with a multi-agent distributed deep deterministic policy gradient (AR-MAD4PG). First, to address the partial observability of agents, we construct a shared agent graph and propose a periodic communication mechanism that enables edge nodes to aggregate information from other edge nodes. Second, to help agents better understand the current system state, we design an RNN-based feature extraction network to capture the historical state and resource allocation information of the VEC system. Thirdly, to tackle the challenges of excessive joint observation-action space and ineffective information interference, we adopt the multi-head attention mechanism to compress the dimension of the observation-action space of agents. Finally, we build a simulation model based on the actual vehicle trajectories, and the experimental results show that our proposed method outperforms the existing approaches.

A series‐fed frequency scanning antenna for millimeter wave wireless communication

AbstractIn the realms of 5G and the upcoming 6G communications, the millimeter‐wave bands offer substantially greater bandwidth. However, due to their propagation characteristics, aligning antennas becomes a challenging issue, particularly at the node side in industrial and vehicular network applications. Angle‐scanning antennas can significantly enhance communication reliability or provide new possibilities for spatial multiplexing. Previous studies have predominantly concentrated on frequency‐scanning antennas characterized by either low‐gain individual units or expensive digital arrays. Consequently, there has been a scarcity of solutions that are both cost‐effective and high‐gain. In this study, a method is proposed for achieving frequency scanning by extending the feed lines between elements. Based on this approach, a millimeter‐wave high‐gain serial‐feed frequency‐scanning antenna is developed. This antenna operates within the 60–64 GHz millimeter‐wave ISM (Industrial, Scientific, and Medical) band and is capable of performing a 10 scan in the E‐plane. Moreover, the antenna maintains a width of less than half a wavelength, allowing for the assembly of multiple antennas into a MIMO array.

Joint optimization of offloading strategy and resource allocation for multi-user in dynamic vehicular edge computing systems

Internet of Vehicles (IoV) relies heavily on its computing capability to facilitate various vehicular applications. Since the cloud computing or mobile edge computing (MEC) only cannot satisfy the latency requirement due to the limitation of the resource coverage, the cloud–edge-end cooperative computing has become an emerging paradigm. A comprehensive IoV architecture is considered and a joint optimization problem is formulated to minimize the system function value. To optimize the resource allocation and the task offloading strategy, the simulated spring system algorithm (SSSA) is designed where the initial problem is decoupled into two sub-problems with priority. The first one is to allocate computing resources based on KKT conditions, thus the individual optimal solution is achieved. The second one is solved based on the idea of simulated spring system such that the task offloading strategy is obtained. Two sub-problems iterate mutually to update each other until finishing the binary tree traversal. Thus, the proposed solution adapts to various conditions and the computational complexity is also reduced compared with traditional methods. Simulation verifies that the proposed algorithm reduces the maximum system function value by about 31% compared with the benchmark methods and performs efficiently in various road conditions.

Dual-Slope Path Loss Model for Integrating Vehicular Sensing Applications in Urban and Suburban Environments.

The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions.

Boosting quantum-structured solar cell light absorption through compressively strained superlattices

The escalating demand for improved energy conversion efficiency in vehicular applications of solar cells underscores the need for innovative solutions. This study focuses on enhancing the current density of GaAs middle cells within conventional Ge-based triple-junction solar cells to realize unprecedented levels of energy conversion efficiency. We introduced a novel superlattice configuration termed a compressively strained superlattice (CSSL) and demonstrated its integration into a p-i-n junction GaAs solar cell, achieving a current density increase of 1.03 mA cm−2 over conventional GaAs solar cells. Prior investigations have explored a strain-balanced superlattice (SBSL) to enhance GaAs middle cell current density. However, our findings establish the superiority of the CSSL over the SBSL in terms of current density improvement, with the CSSL featuring 1.59 times more quantum wells per unit length than the SBSL. This increase in quantum well quantity significantly enhances light absorption efficiency and consequently, the current density.