Dedicated Short Range Communication Research Articles

Fast synchronization with enhanced switching control for grid-tied single-phase square wave inverter using FPGA

Research on grid synchronization has been conducted worldwide by researchers in conjunction with the development of innovative technologies, such as dedicated short-range communication (DSRC) and cellular vehicle-to-everything (C-V2X). However, grid-connected inverters face several challenges, mainly the mismatch in voltage amplitude, frequency, and phase angle, as well as grid voltage disturbance and grid faults. Thus, the control algorithm of this research mainly focused on a half-cycle algorithm to design an enhanced digital switching control for fast synchronization using an FPGA. The control algorithm was developed based on zero-crossing detection (ZCD) and digital phase-locked loop (PLL) modeling techniques using the hardware description language (HDL) and a combination of digital logic blocks in Quartus II software, where the proposed switching was applied using the square-wave switching technique through a 300-watt full-bridge experimental prototype. The performance of the proposed technique was studied, where the total harmonic distortion (THD) for voltage and current resulted in a percentage reduction of 89.29% and 78.05% for voltage and current, respectively, after filter implementation. Also, the resulting signal synchronized in every half cycle and matched the voltage amplitude, frequency, and phase angle of the grid signal in 10 ms.

Compact design of circularly polarized microstrip patch antenna with double slip ring resonator for vehicle-to-vehicle communication

The paper mainly focuses on the compact design of circularly polarized microstrip patch antenna for vehicle-to-vehicle communication. The charging time of electric vehicle is longer waiting time than compared to traditional gasoline-based vehicles. The charging is done at the public charging stations. When the vehicles need charging while driving, they communicate with Dedicated Short Range Communications (DSRC) band to send the information to the nearest vehicles and receive information from the charging station. Moreover, DSRC band is used in many applications area such as Intelligent Transportation System (ITS), Electronic Toll Collection (ETC), Collision Avoidance, and connected vehicles. The research problem in this study is that when installing an antenna on a vehicle, it is important to have a compact size and achieve circular polarization. The research solution for the problem statement in this study is emphasized on 45? inclined slot and corner trimmed is added in rectangular patch to make the current part longer and the impedance matching better, and to get circular polarization. After that, Swiss roll structure CSRR is added in ground plane to improve bandwidth. Finally, outer SRR is added in the ground plane to improve S11. The proposed design has been validated for car-to-car (5.85-5.925 GHZ) communication.

Optimizing Hybrid V2X Communication: An Intelligent Technology Selection Algorithm Using 5G, C-V2X PC5 and DSRC

Cooperative communications advancements in Vehicular-to-Everything (V2X) are bolstering the autonomous driving paradigm. V2X nodes are connected through communication technology, such as a short-range communication mode (Dedicated Short Range Communication (DSRC) and Cellular-V2X) or a long-range communication mode (Uu). Conventional vehicular networks employ static wireless vehicular communication technology without considering the traffic load on any individual V2X communication technology and the traffic dynamics in the vicinity of the V2X node, and are hence inefficient. In this study, we investigate hybrid V2X communication and propose an autonomous and intelligent technology selection algorithm using a decision tree. The algorithm uses the information from the received Cooperative Intelligent Transport Systems (C-ITS) Cooperative Awareness Messages (CAMs) to collect statistics such as inter vehicular distance, one-way end-to-end latency and CAM density. These statistics are then used as input for the decision tree for selecting the appropriate technology (DSRC, C-V2X PC5 or 5G) for the subsequent scheduled C-ITS message transmission. The assessment of the intelligent hybrid V2X algorithm’s performance in our V2X test setup demonstrates enhancements in one-way end-to-end latency, reliability, and packet delivery rate when contrasted with the conventional utilization of static technology.

Multimodal Deep Learning for Estimating Lane-Level Urban Traffic by Fusing Closed-Circuit Television and Dedicated Short-Range Communication Data

Seamless, accurate, and reliable traffic information is critical for planning and operational strategies in traffic management. Urban networks are increasingly being equipped with various types of detectors to collect continuous traffic data. However, the widespread installation and management of these detectors at all intersections and road sections are limited by cost constraints and various problems. Moreover, the direct integration and utilization of heterogeneous data sources are challenging because of their distinct and interconnected characteristics. This study introduces a multimodal deep learning model, combining closed-circuit television (CCTV) and dedicated short-range communication (DSRC) data, to estimate lane-level urban traffic volumes. The proposed model employs a multilayer perceptron to extract features from each modality. These features are then fused and used as input into a recurrent neural network model that estimates the lane-level traffic volume. We present the multimodal deep learning model in three forms: (1) fusion of traffic volume, occupancy, and queue length from CCTV data; (2) fusion of traffic volume from CCTV data with travel time from DSRC data; and (3) fusion of different attributes from CCTV and heterogeneous DSRC data. In addition, we develop a single-modality model that solely utilizes CCTV data on traffic volume to compare the performance with the proposed multimodal model and identify scenarios where the multimodal approach is essential. The proposed model demonstrates significant improvements over the single-modality model, providing enhanced accuracy for higher temporal resolutions and lanes that permit left or right turns.

A Comprehensive Study and Analysis of the Third Generation Partnership Project’s 5G New Radio for Vehicle-to-Everything Communication

Recently, the Third Generation Partnership Project (3GPP) introduced new radio (NR) technology for vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing long-term evolution (LTE) cellular-vehicle-to-everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the fifth-generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from dedicated short-range communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios.

Evaluating Signal Preemption Requests in Utah Using Vehicle-to-Everything Dedicated Short-Range Communication Equipped Snowplows

At the start of the 2019 to 2020 snow season, vehicle-to-everything (V2X) systems using dedicated short-range communication (DSRC) were placed on Utah Department of Transportation snowplows and traffic signal controllers on selected state routes. This study was conducted to understand the overall impacts of snowplows using V2X DSRC to request signal preemption. Roadside units (RSUs) were deployed on five corridors throughout the Salt Lake metropolitan area. Similar routes without RSUs were selected as a control and were used in the analysis to quantify results. Each snowplow on these corridors was equipped with an onboard unit (OBU). Based on data collected, analysis was performed on both traffic signal performance and vehicle performance data. Within the traffic signal performance analysis, it was found that the V2X DSRC system was utilized often, with snowplows requesting preemption in more 50% of the occasions they approached a signalized intersection. Of those requests, signal controllers granted preemption in over 80% of cases. On average, signal controller coordination was affected for less than 5 min. The vehicle performance analysis found that the snowplows on equipped routes had travel speeds that were less affected when there was snow than on corresponding not-equipped routes. Vehicle crash data also showed that there was a greater decrease in crashes on equipped routes than not-equipped routes. Anecdotal evidence gathered from snowplow drivers indicated that snowplows stopped less when using signal preemption. Drivers also noted a benefit to overall snow removal operations on corridors equipped with the V2X DSRC system.

Optimizing V2V communication in ITS: The benefits of silver-coated DSRC

The silver-coated DSRC of antennas are used for short-range communication between vehicles (V2V) in intelligent transportation systems (ITS), including DSRC (Dedicated Short-Range Communication). It has silver as a conducting material for the antennas and offers several benefits including higher data rates, increased range, and improved reliability for V2V communication. Using multiple antennas in a silver-coated DSRC provides spatial diversity, reducing the effects of multipath fading and interference. The radiation pattern can also be focused in a specific direction to increase the signal strength. The integration of artificial intelligence (AI) and autonomous drivers into ITS is becoming increasingly important, and V2V communication is a crucial aspect of these systems. The use of a silver-coated DSRC can provide a reliable and efficient solution for DSRC communication in V2V communication, which is essential for the success of ITS. The increased data rates, range, and reliability facilitate the exchange of information between autonomous vehicles, enabling them to make more informed decisions and operate more safely on roads.

Fuzzy decision-making framework for explainable golden multi-machine learning models for real-time adversarial attack detection in Vehicular Ad-hoc Networks

This paper addresses various issues in the literature concerning adversarial attack detection in Vehicular Ad-hoc Networks (VANETs). These issues include the failure to consider both normal and adversarial attack perspectives simultaneously in Machine Learning (ML) model development, the lack of diversity preprocessing techniques for VANETs communication datasets, the inadequate selection guidelines for real-time adversarial attack detection models, and the limited emphasis on explainability in adversarial attack detection. In this study, we propose an original fuzzy decision-making framework that incorporates multiple fusion standpoints. Our framework aims to evaluate multi-ML models for real-time adversarial attack detection in VANETs, focusing on three stages. The first stage involves identifying and preprocessing Dedicated Short-Range Communication (DSRC) data using standard and fusion preprocessing approaches. Two communication scenarios, normal and jammed, are considered, resulting in two DSRC datasets. In the second stage, we develop multi-ML models based on the DSRC datasets using standard preprocessing and feature fusion preprocessing for dataset-1 and dataset-2, respectively. The third stage evaluates the multi-ML models using a fuzzy decision-making approach based on the Fuzzy Decision by Opinion Score Method (FDOSM) and an adversarial attack decision fusion matrix. The External Fusion Decision (EFD) settings of the FDOSM address individual ranking variance, provide a unique rank and select the best model. Experimental results demonstrate that the K-Nearest Neighbors Algorithm (kNN) model achieves the highest explain score of 0.2048 in dataset-1 using standard preprocessing, while the Random Forest (RF) model applied to dataset-2 using fusion preprocessing emerges as the most robust and golden model against adversarial attacks, with a score of 0.1819. This finding suggests that the fusion preprocessing approach using Principal Component Analysis (PCA) is more suitable for addressing normal and adversarial attack perspectives. Furthermore, our fuzzy framework undergoes evaluation in terms of systematic rank, sensitivity analysis, explainability analysis, and comparison analysis. Overall, this framework provides valuable insights for researchers and practitioners in VANETs, informing the execution, selection, and interpretation of multi-ML models to tackle adversarial attack detection problems effectively. The new fuzzy framework demonstrates that multi-ML models based on feature fusion preprocessing are more effective.

Performance comparison based on priority between 802.11p and C-V2X

With the increasing number of vehicles, the issue of traffic safety has become more and more prominent. There is a growing demand for real-time vehicle monitoring and vehicle-to-vehicle communication, which has led to the rapid development of connected car technology. Currently, the two mainstream wireless access technologies that support vehicle-to-everything (V2X) communication are Dedicated Short-Range Communications (DSRC) and Cellular-V2X (C-V2X) or LTE-V2X. This paper aims to compare the performance of these two technologies by studying two types of information, Cooperative Awareness Messages (CAM) and Decentralized Environmental Notification Messages (DENM), as well as the prioritization defined for C-V2X. Additionally, this paper compares the prioritization of 802.11p with different priority levels of C-V2X. Through simulation experiments, it is ultimately proven that 802.11p outperforms C-V2X in terms of latency but is inferior to C-V2X in terms of collision probability.

Resource-aware DBSCAN-based re-clustering in hybrid C-V2X/DSRC vehicular networks.

5G wireless networks are paying increasing attention to Vehicle to Everything (V2X) communications as the number of autonomous vehicles rises. In V2X applications, a number of demanding criteria such as latency, stability, and resource availability have emerged. Due to limited licensed radio resources in 5G cellular networks, Cellular V2X (C-V2X) faces challenges in serving a large number of cars and managing their network access. A reason is the unbalanced load of serving Base Stations (BSs) that makes it difficult to manage the resources of the BSs optimally regarding the frequency reuse in cells and its subsequent co-channel interference. It is while the routing protocols could help redirect the load of loaded BSs to neighboring ones. In this article, we propose a resource-aware routing protocol to mitigate this challenge. In this regard, a hybrid C-V2X/ Dedicated Short Range Communication (DSRC) vehicular network is considered. We employ cluster-based routing that enables many cars to interface with the network via some Cluster Heads (CH) using DSRC resources while the CHs send their traffic across C-V2X links to the BSs. Traditional cluster-based routings do not attend the resource availability in BSs that are supporting the clusters. Thus, our study describes an enhanced clustering method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) that re-clusters the vehicles based on the resource availability of BSs. Simulation results show that the proposed re-clustering method improves the spectrum efficiency by at least 79%, packet delivery ratio by at least 5%, and load balance of BSs by at least 90% compared to the baseline.

Cost-effective live video streaming for internet of connected vehicles using heterogeneous networks

Smart vehicles of Internet of Connected Vehicles (IoCV) create Ad-Hoc networks and are persistently connected to the Internet through different network access technologies like Wifi, cellular, and dedicated short range communication (DSRC). A vehicle may prefer one network access technology over another based on availability, coverage area, and network access cost. The choice also depends on application requirements, like delay-tolerant or delay-sensitive, and application objectives, like maximizing data rate, maximizing cellular traffic offloading, etc. Cellular traffic offloading using Wifi access points and vehicle-to-vehicle (V2V) collaborations are very challenging due to the intermittent short coverage areas of Wifi access points and highly dynamic connectivity of V2V Ad-Hoc network. Most existing works on traffic offloading have considered delay-tolerant or on-demand services, whereas delay-constrained live video streaming services are mostly ignored. This work is focused on live video streaming services over IoCVs, where the non-triviality of the network selection task is demonstrated by formulating the task as an integer linear programming problem (ILPP). Accordingly, a distributed algorithm is proposed, which greedily maximizes cellular traffic offloading while significantly reducing the control packet overheads. Compared to existing works, the proposed work provides the best service effectiveness with a balanced performance on traffic offloading, service efficiency, and user satisfaction.

Deep reinforcement learning‐based dual‐mode congestion control for cellular V2X environments

AbstractThe Society of Automotive Engineers (SAE) J2945/1 standard for Dedicated Short Range Communication (DSRC) environmentutilizes transmit (Tx) power control and rate control elements for the periodic BSM transmissions, which are intended to work in a complementary manner. An equivalent standard for the cellular vehicle‐to‐everything (C‐V2X) communication environment is J3161/1, but it eliminates Tx power control and uses only rate control. However, the consequence is the degraded update delay of neighbouring vehicles’ kinematics, potentially undermineing driving safety. In this Letter, the authors propose to retain the dual‐mode control in the C‐V2X environment and find a policy through reinforcement learning (RL) to adjust the rate control function to maintain synergy. Moreover, the authors can extract the RL‐created policy from the neural network so that it can be explicitly specified in the standard, and downloaded and used more conveniently by vehicles. Finally, the RL‐generated policy achieves a better packet delivery frequency than J2945/1 or J3161/1.

A Conformal Dual-Band CPW-MIMO Antenna for 5G-V2X Communication

A Co-Planar Waveguide (CPW) fed conformal Multiple Input Multiple Output (MIMO) antenna is presented by deploying circular and rectangular patch in a stacked configuration. The analytical equations for the antenna are modeled and compared with the proposed antenna. The antenna is designed on a flexible polyimide substrate featuring a dielectric value of 3.5 with 0.1 mm thickness resonating at two frequencies, LTE-42 Band (5G-V2X) 3.4-3.6 GHz and Dedicated Short Range Communication (DSRC) 5.850-5.925 GHz. The design is further extended as a 4 × 4 MIMO structure by positioning the antenna elements orthogonal, separated by a distance of 10 mm. The antenna exhibits a reflection coefficient < −10 dB with a gain value between 4.5 and 5.25 dB over the desired frequencies. The radiation efficiency of the antenna is around 90% at both bands. MIMO parameters were measured to analyze the MIMO performance of the proposed antenna and the placement analysis of the antenna is studied by mounting the antenna on various vehicle positions and their corresponding pattern were characterized. The designed MIMO antenna is fabricated and tested, having an overall size of 85 × 85 × 0.1 mm3, offering extremely flexible design functioning to be a promising candidate for next-generation V2X Communication.

Exploring the Potential and Challenges of Vehicular Ad Hoc Networks: A Comprehensive Review

The paper discusses the architecture of vehicular ad hoc networks (VANETs) and their applications, simulation tools, communication domains, and challenges. VANETs enable wireless communication between moving vehicles using dedicated short-range communication (DSRC) technology, and vehicle-to-infrastructure communication with roadside units. The paper describes the three main components of VANETs: the roadside unit (RSU), application unit (AU), and on-board unit (OBU). The purpose of the paper is to provide researchers and developers with detailed information about VANETs and help them understand their aspects and challenges. The information provided in the paper could facilitate the development of VANETs for enhancing road safety and providing comfortable driving experiences.

Sensor based traffic signal pre-emption for emergency vehicles using efficient short-range communication network

Roads are becoming increasingly congested due to urbanization and the increased use of private vehicles. As a result, emergency vehicles like ambulances, police cars, and fire trucks get stuck in traffic and take longer to get where they're going, which may cause risk, damage to property, and losing valuable lives. The acoustic-based pre-emption technology uses the EV's siren to alert oncoming cars. Therefore, acoustic reflection from a building or other large container vehicle might cause a fall. This paper implements a vehicle-to-infrastructure (V2I) communication system that anticipates traffic signals using Global Positioning System (GPS) and Dedicated Short Range Communication (DSRC). Line of sight (LOS) is not required when choosing the desired range of DSRC communication, and the traffic signal unit receives pre-emption messages anytime an emergency vehicle is nearby. Thus, these messages cause the traffic light to turn green for the emergency vehicle instead of operating normally. As a result, this type of system necessitates two hardware modules, one at each vehicle On-Board-Unit (OBU) and one at each intersection Road-Side-Unit (RSU), as well as the Decision Support System i.e., Traffic Management Controller (TMC). In the hardware OBU and RSU, the IMX-6 Processor, GPS Module, and DSRC transmitter and receivers are used. The Emergency Vehicle (EV) equipped with the OBU transmits requests to the intersection unit, which is fully autonomous and can be employed as a component of the Intelligent Transportation System (ITS), negating the need for a driver in this method.

Using Radio Technical Commission for Maritime Services Corrections in a Consumer-Grade Lane-Level Positioning System for Connected Vehicles

&lt;div&gt;Connected vehicle (CV) technology has the potential to greatly improve the safety, mobility, and environmental sustainability of traffic. Many CV applications require the vehicle position as input, which is primarily provided by global navigation satellite systems (GNSS). Although a large number of those applications (e.g., Intersection Movement Assist) require vehicle positioning to have lane-level accuracy, it has been shown that the type of positioning system typically used by CVs currently cannot provide consistent lane-level accuracy, even under open-sky conditions. In order to address this gap, we have evaluated an enhanced positioning system that adds little, if any, to the cost of the CV. It consists of a single-frequency real-time kinematic (RTK)-capable GNSS receiver onboard the vehicle, which utilizes Radio Technical Commission for Maritime Services (RTCM) differential corrections transmitted over dedicated short-range communications (DSRC) by the roadside infrastructure. Tests on a moving vehicle show that this system could provide lane-level accuracy over 95% of the time in open-sky conditions. These tests also show the DSRC to be an effective means of disseminating RTCM corrections, given the intersection spacings and communication ranges in the test. However, neither RTCM nor the more commonly used space-based augmentation system (SBAS) differential corrections appeared to improve the positioning accuracy of GNSS in urban canyons.&lt;/div&gt;

Heterogeneous Architecture for DSRC/LTE Vehicular Communication Networks Based on the ITS Reference Architecture with Fuzzy Logic for Decision-Making

Methods to integrate different access technologies such as heterogeneous communication architecture remains a challenge. Among a variety of V2X-supporting access technologies, Dedicated Short Range Communication (DSRC) and cellular communications, e.g., Long Term Evolution (LTE) is promising reliable and efficient vehicular communications. DSRC has been designed to allow for direct low-latency communications among different vehicles (i.e., V2V) and between vehicles and roadside units (RSU). On the other hand, DSRC suffers from link quality degradation with the presence of buildings and vehicles, especially in urban areas, where channel collisions become serious when vehicle density is high. LTE networks can provide wide area coverage and are favorable to bandwidth-greedy applications, which require high data rates and reliability. Considering the relatively high end-to-end delay for message transmission due to the long transmission time interval (TTI) current LTE networks have drawbacks regarding latency to support highfrequency safety-related information exchange among vehicles in local areas. Combining LTE and DSRC approaches as a heterogeneous solution is essential to fast introduce V2X services for future automated driving. By this intelligent Vertical Handover (VHO) algorithms are needed to ensure seamless connectivity of vehicles to the best network at a particular point in time. For this paper, we propose a 3- input fuzzy-logic-based VHO scheme for Heterogeneous DSRC/LTE Vehicular Communication Networks. The simulation results in this paper show that the proposed 3- input fuzzy-logic model can be able to make decisions and select the best network to connect to base on the network with the highest HF value. The 3-input fuzzy-logic model was also tested in a vehicular highway scenario simulation. The proposed model shows optimized communication results in terms of the mean end-to-end delay against that of the literature.

DSRC Versus LTE-V2X: Empirical Performance Analysis of Direct Vehicular Communication Technologies

Vehicle-to-Vehicle (V2V) communication systems have an eminence potential to improve road safety and optimize traffic flow by broadcasting Basic Safety Messages (BSMs). Dedicated Short-Range Communication (DSRC) and LTE Vehicle-to-Everything (V2X) are two candidate technologies to enable V2V communication. DSRC relies on the IEEE 802.11p standard for its PHY and MAC layer while LTE-V2X is based on 3GPP’s Release 14 and operates in a distributed manner in the absence of cellular infrastructure. There has been considerable debate over the relative advantages and disadvantages of DSRC and LTE-V2X, aiming to answer the fundamental question of which technology is most effective in real-world scenarios for various road safety and traffic efficiency applications. In this paper, we present a comprehensive survey of these two technologies (i.e., DSRC and LTE-V2X) and related works. More specifically, we study the PHY and MAC layer of both technologies in the survey study and compare the PHY layer performance using a variety of field tests. First, we provide a summary of each technology and highlight the limitations of each in supporting V2X applications. Then, we examine their performance based on different metrics.

Toward the Age of Intelligent Vehicular Networks for Connected and Autonomous Vehicles in 6G

Twenty-two years after the advent of the firstgeneration vehicular network, i.e., dedicated short-range communications (DSRC) standard/IEEE 802.11p, the vehicular technology market has become very competitive with a new player, Cellular Vehicle-to-Everything (C-V2X). Currently, C-V2X technology likely dominates the race because of the big advantages of comprehensive coverage and high throughput/reliability. Meanwhile, DSRC-based technologies are struggling to survive and rebound with many hopes betting on success of the secondgeneration standard, IEEE P802.11bd. While the standards battle to attract automotive makers and dominate the commercial market landing, the research community has started thinking about the shape of the next-generation vehicular networks. This article details the state-of-the-art progress of vehicular networks, particularly the cellular V2X-related technologies in specific use cases, compared to the features of the current generation. Through the typical examples, we also highlight why 5G is inadequate to provide the best connectivity for the vehicular applications and then 6G technologies can fill up the vacancy.

Application of IoT Technologies in Seaport Management

Seaports have a very important role in connecting freight by sea. Goods transported through seaports in the world are increasing day by day to meet human needs. This increases the pressure to apply more technologies for better port management. The world's seaports in the 4th generation, the generation in which seaports enhance connectivity to form a large network, have shown high efficiency when applying technology to port management. This optimizes port operations and connects port information into a network that improves productivity and reduces loading and unloading times. Today, the Internet of Things is the foundation for technologies to manage and optimize operations in various fields. It is considered by scientists to be a highly influential technology in the “4.0” era. The Internet of Things (IoT) technology directly affects the activities and processes of loading and unloading goods at seaports. Modern IoT-based port management technologies such as Radio Frequency Identification (RFID) and Dedicated Short Range Communications (DSRC) are contributing to the increased speed and safe movement of goods through seaports. The application of IoT in port management has become an inevitable trend and will be presented in this article. In the next generation, seaports tend to develop into smart ports based on rapidly developing technology platforms such as IoT, blockchain, and cloud computing. Smart port development also poses many issues to be resolved, including environmental issues. In this paper, the authors present some solutions to develop smart ports in an environmentally friendly manner.