Dedicated Short Range Communication Research Articles

A Real-Time Channel Prediction Model Based on Neural Networks for Dedicated Short-Range Communications.

Based on a multiple layer perceptron neural networks, this paper presents a real-time channel prediction model, which could predict channel parameters such as path loss (PL) and packet drop (PD), for dedicated short-range communications (DSRC). The dataset used for training, validating, and testing was extracted from experiments under several different road scenarios including highways, local areas, residential areas, state parks, and rural areas. The study shows that the proposed PL prediction model outperforms conventional empirical models. Meanwhile, the proposed PD prediction model achieves higher prediction accuracy than the statistical one. Moreover, the prediction model can operate in real-time, through updating its training set, to predict channel parameters. Such a model can be easily extended to the applications of autonomous driving, the Internet of Things (IoT), 5th generation cellular network technology (5G) and many others.

Demonstrating Transit Schedule Benefits with a Dedicated Short-Range Communication-Based Connected Vehicle System

A vehicle-to-infrastructure (V2I) connected vehicle system was installed along Redwood Road in Salt Lake City, Utah, United States, in November 2017 using dedicated short-range communication (DSRC) radios to connect transit buses to traffic signals. One of the goals of this system was to improve the schedule reliability of the bus by providing signal priority at traffic signals when the bus is behind its published schedule by a certain threshold. Data for the analysis were obtained from the DSRC communications, the Automated Traffic Signal Performance Measures (ATSPM) system, and the transit operations system. The robust data available from these three systems allow for detailed analysis of priority requests made, requests served, and bus on-time performance in a way that is not possible without these data sets. By comparing actual schedules of the four DSRC-equipped buses over a 4-month period from April to July 2018 with buses which do not have the ability to request signal priority, it has been determined that the equipped buses meet their published schedule about 2% to 6% more frequently, depending on direction and time of day, with the most significant improvement of 6% in the southbound PM peak.

Encrypted Vehicular Communication Using Wireless Controller Area Network

In this paper, we focus on ensuring encrypted vehicular communication using wireless controller area network performance at high node densities, by means of Dedicated Short-Range Communication (DSRC) algorithms. We analyses the effect of the vehicular communication parameters, message-rate, data-rate, transmission power and carrier sensing threshold, on the application performance. After a state-of-the-art analysis, we propose a data-rate DSRC algorithm. Simulation studies show that DSRC performs better than other decentralized vehicular communication algorithms for a wide range of application requirements and densities. Vehicular communication plays one of the most important roles for future autonomous vehicle. We have systematically investigated the impact of vehicular communication using the MATLAB© application platform and achieved an accuracy of 93.74% after encrypting all the communications between the vehicles and securing them by applying the encryption on V2V communication in comparison with the existing system of Sensor Networks which stands at 92.97%. The transmission time for the encryption is 165 seconds while the rate of encryption is as low as 120 Mbps for the proposed awareness range of vehicles to vehicle using DSRC algorithm in Wireless-Controller Area Network for communication. Experimental results show that our proposed method performs 3% better than the recently developed algorithms.

Characterizing Urban Vehicle-to-Vehicle Communications for Reliable Safety Applications

The IEEE 802.11p-based dedicated short range communication (DSRC) is essential to enhance driving safety and improve road efficiency by enabling rapid cooperative message exchanging. However, there is a lack of good understanding on the DSRC performance in urban environments for vehicle-to-vehicle (V2V) communications, which impedes its reliable and efficient application. In this paper, we first conduct intensive data analytics on V2V performance, based on a large amount of real-world DSRC communications trace collected in Shanghai city, and obtain several key insights as follows. First, among many context factors, the non-line-of-sight (NLoS) link condition is the major factor degrading V2V performance. Second, the durations of line-of-sight (LoS) and NLoS transmission conditions follow power law distributions, which indicate that the probability of experiencing long LoS/NLoS conditions both could be high. Third, the packet inter-reception (PIR) time distribution follows an exponential distribution in the LoS conditions but a power law in the NLoS conditions, which means that the consecutive packet reception failures rarely appear in the LoS conditions but can constantly appear in the NLoS conditions. Based on these findings, we propose a context-aware reliable beaconing scheme, called CoBe , to enhance the broadcast reliability for safety applications. The CoBe is a fully distributed scheme, in which a vehicle first detects the link condition with each of its neighbors by machine learning algorithms, then exchanges such link condition information with its neighbors, and finally selects the minimal number of helper vehicles to rebroadcast its beacons to those neighbors in bad link condition. To analyze and evaluate the CoBe performance, a two-state Markov chain is devised to model beaconing behaviors. The extensive trace-driven simulations are conducted to demonstrate the efficacy of CoBe .

Robust control of connected vehicles via V2V communication

AbstractThis paper introduces a robust controller for connected vehicles which are communicating through dedicated short‐range communication (DSRC) protocol to avoid collision and improve safety. By a comprehensive analysis on both longitudinal and lateral dynamics of the vehicles, the controller covers a wide range of collision avoidance scenarios, such as: Intersection Movement Assistant (IMA), Automatic Emergency Brake (AEB) and Lane Change Warning (LCW). By implementing an advanced Sliding Mode Controller (SMC), the effect of uncertainties will be mitigated for a variety of path plans. It turns out that the proposed control scheme can robustly improve the safety. Finally, we optimize the performance of vehicles in tracking the paths by an MPC controller.

Vehicle‐localization‐based and DSRC‐based autonomous vehicle rear‐end collision avoidance concerning measurement uncertainties

Rear‐end collision is one of the most serious accidents for vehicles, some rear end collision avoidance systems have been developed to protect vehicles from rear‐end collisions. Current rear‐end collision avoidance systems mainly utilize vehicle‐relative measurement sensors to acquire information on surrounding vehicles, considering limited detection ranges and impaired performance in bad weathers of these vehicle sensors, currently used collision avoidance systems cannot guarantee vehicle safety in all environments. Vehicle‐localization‐based and intervehicle communication‐based rear‐end collision avoidance approaches can be possible solutions to improve vehicle safety compared with present vehicle sensor‐based collision avoidance systems. Dedicated Short‐Range Communication (DSRC) is introduced to transfer information on surrounding vehicles instead of using vehicle‐relative measurement sensors. Vehicle dynamic model and brake system dynamics are used to generate precise vehicle states that would be used in rear‐end collision avoidance. Required safe distance is used as the safety index when using different vehicle‐localization methods with a predefined safety probability of 99.99%. Monte Carlo simulations are executed to evaluate the influence of measurement uncertainties. Simulation results show that vehicle‐localization‐based and DSRC‐based rear‐end collision avoidance methods can guarantee vehicles free from rear‐end collision and expand the service capabilities of velocity compared with the traditional vehicle radar‐based rear‐end collision avoidance method. Evaluation outcomes encourage us to adopt a more precise absolute vehicle‐localization approach in rear‐end collision avoidance, because required safe distance and safe distance margin can be decreased when using a high‐precision vehicle‐localization method compared with standalone GNSS‐based localization. Some further aspects are discussed in this work that need to be tackled with to make vehicle‐localization‐based and intervehicle communication‐based rear‐end collision avoidance systems into practice, including real‐time brake system dynamics acquirement, benefit of DSRC to curve roads, and so on. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Distributed Edge Computing to Assist Ultra-Low-Latency VANET Applications

Vehicular ad hoc networks (VANETs) are a recent class of peer-to-peer wireless networks that are used to organize the communication and interaction between cars (V2V), between cars and infrastructure (V2I), and between cars and other types of nodes (V2X). These networks are based on the dedicated short-range communication (DSRC) IEEE 802.11 standards and are mainly intended to organize the exchange of various types of messages, mainly emergency ones, to prevent road accidents, alert when a road accident occurs, or control the priority of the roadway. Initially, it was assumed that cars would only interact with each other, but later, with the advent of the concept of the Internet of things (IoT), interactions with surrounding devices became a demand. However, there are many challenges associated with the interaction of vehicles and the interaction with the road infrastructure. Among the main challenge is the high density and the dramatic increase of the vehicles’ traffic. To this end, this work provides a novel system based on mobile edge computing (MEC) to solve the problem of high traffic density and provides and offloading path to vehicle’s traffic. The proposed system also reduces the total latency of data communicated between vehicles and stationary roadside units (RSUs). Moreover, a latency-aware offloading algorithm is developed for managing and controlling data offloading from vehicles to edge servers. The system was simulated over a reliable environment for performance evaluation, and a real experiment was conducted to validate the proposed system and the developed offloading method.

Self-Adaptive Clustering and Load-Bandwidth Management for Uplink Enhancement in Heterogeneous Vehicular Networks

Due to the diversity of traffic scenes and high mobility of vehicles, the propagation environment between moving vehicles and road side access points can be highly dynamic, which causes unstable uplink connectivity and time-varying uplink data rates in vehicular networks. In this paper, we study the uplink performance of heterogenous vehicular networks, which integrate dedicated short-range communications (DSRCs) and Long Term Evolution vehicle-to-everything (LTE V2X) into a single vehicular network to provide high reliability, low latency, and wide area coverage. Here, we adopt a cluster-based approach, in which DSRC and LTE are utilized to provide vehicle-to-vehicle communications between vehicles within a cluster and vehicle-to-infrastructure communications between vehicles and access points, respectively. Specifically, a self-adaptive clustering method is proposed based on the iterative self-organizing data analysis technique algorithm, in which the number of clusters can automatically adjust to the optimal value according to the mobility information. Also, a joint load-bandwidth management scheme is proposed to distribute traffic load and bandwidth resources between DSRC and LTE. Simulation results show that the proposed algorithm outperforms the traditional section-based and ${K}$ -means clustering methods, and a tradeoff between average uplink data rate and signaling overhead can be achieved.

Electromagnetic Wave Absorber Sheet for 940 MHz Dedicated Short Range Communication Frequency Bands Using Fe Based Alloy Soft Magnetic Metal Powder

The recent development of information and communication technologies brings new changes to automobile traffic systems. The most typical example is the advancement of dedicated short range communication(DSRC). DSRC mainly consists of an intelligent transportation system(ITS), an electronic toll collection system(ETCS) and an advanced traveler information system(ATIS). These wireless communications often cause unnecessary electromagnetic waves, and these electromagnetic waves, in turn, cause frequent system malfunction. To solve this problem, an absorber of electromagnetic waves is suggested. In this research, various materials, such as powdered metal and iron oxides, are used to test the possibility for an effective absorption of the unnecessary electromagnetic waves. The various metal powders are made into a thin sheet form by compositing through processing. The electromagnetic characteristics(complex permittivity, complex permeability) of the fabricated sheet are measured. As a result, we achieve –6.5 dB at 940 MHz(77.6 % absorption rate) with a 1.0 mm-thickness electromagnet wave absorber, and –9.5 dB at 940 MHz(88.8 % absorption rate) with a 2.0 mm-thickness absorber.

Privacy-aware query processing in vehicular ad-hoc networks

Recently there is a research trend to integrate the cloud and the vehicular ad-hoc networks to enhance each other on the cooperative urban sensing applications. However, it remains a challenging problem on how to forward queries to search the results and then to route back the query results, as the vehicular nodes dynamically move on the road. Also, the dark side of vehicular cloud is often ignored. Getting the owner’s identity or locations of a given vehicle, or getting the queries submitted by vehicles would put the data privacy at risk. In this paper we propose a privacy preserving query processing scheme for vehicular sensor networks. The scheme consists of two phases: 1) at the index mapping phase, sensed data are stored locally at road side units (RSU), and a global index is maintained at the cloud; 2) at the query processing phase, queries are transformed based on a mapping function, and then are routed to the cloud. The cloud computes the set of RSUs that have the query result based on the mapping index, and then directs the query to the RSUs. Then the result forwarding is transformed into a query result forwarding problem at the RSU, where the data of query results are routed back at their best paths either through the 4G channel or through the DSRC (Dedicated Short Range Communication) channel. Also, the query results are encrypted and diffused to a set of RSUs that the query requester would travel along, so the requester could fetch the results before the query is outdated. Extensive experiments demonstrate the effectiveness of the proposed algorithm in vehicular sensor networks. The ratio of successful query delivery is higher than existing query schemes, while at the same time preserving the privacy of query requesters and the data owner in vehicular sensor networks.

Harnessing Vehicular Broadcast Communications: DSRC-Actuated Traffic Control

Traffic congestion in major cities around the globe is a daunting problem that is getting worse as the speed of urbanization keeps increasing. The role of traffic lights (TLs) at intersections in regulating flows cannot be underestimated. Even though there are a variety of actuated traffic signals based on cameras or loop detectors, the vast majority of existing TLs in the world employ a timer-based decision logic which is clearly not very effective. In this paper, we present a new scheme for controlling traffic at intersections, which is known as dedicated short-range communications (DSRCs)-actuated traffic control. The proposed approach leverages the presence of DSRC radios in vehicles and gives priority (by displaying green light) to approaches (roads) that include DSRC-equipped vehicles (such as 10% or 20% of vehicles having DSRC radios). Using this priority mechanism, it is shown that the average waiting time at each TL can be significantly reduced. This, in turn, can reduce the average commute time of urban workers during rush hours substantially. One of the great advantages of the presented approach is that it can function well with even a low percentage of DSRC-equipped vehicles. Given that many industry forecasts predict a gradual penetration rate for the DSRC technology, this is a very attractive feature. It is also shown that the proposed new approach leads to a cost-effective solution for urban traffic control since the hardware and software platforms needed for its implementation are low cost.

Radiometric identification using variational mode decomposition

Radiometric Identification (RAI) is the identification of wireless devices through their Radio Frequency (RF) emissions. In recent years, the research community has investigated it applying different methods and sets of statistical features extracted from the digitized RF emissions. In this paper, the authors investigate the application of Variational Mode Decomposition (VMD), recently introduced as an improvement to Empirical Mode Decomposition (EMD). VMD is applied to two sets of RF emissions from: wireless devices supporting Dedicated Short Range Communications (DSRC) at 5.9 GHz and Internet of Things wireless devices transmitting in the Industrial, Scientific and Medical (ISM) band at 2.4 GHz. Various machine learning algorithms have been used for classification and results are compared. Performances of VMD are evaluated against other approaches used in literature in Line of Sight (LOS) conditions, with Additive White Gaussian Noise (AWGN) and fading effects. Results show that VMD significantly outperforms other approaches.

Positioning Information Privacy in Intelligent Transportation Systems: An Overview and Future Perspective.

Today, the Intelligent Transportation Systems (ITS) are already in deep integration phase all over the world. One of the most significant enablers for ITS are vehicle positioning and tracking techniques. Worldwide integration of ITS employing Dedicated Short Range Communications (DSRC) and European standard for vehicular communication, known as ETSI ITS-G5, brings a variety of options to improve the positioning in areas where GPS connectivity is lacking precision. Utilization of the ready infrastructure, next-generation cellular 5G networks, and surrounding electronic devices together with conventional positioning techniques could become the solution to improve the overall ITS operation in vehicle-to-everything (V2X) communication scenario. Nonetheless, effective and secure communication protocols between the vehicle and roadside units should be both analyzed and improved in terms of potential attacks on the transmitted positioning-related data. In particular, said information might be misused or stolen at the infrastructure side conventionally assumed to be trusted. In this paper, we first survey different methods of vehicle positioning, which is followed by an overview of potential attacks on ITS systems. Next, we propose potential improvements allowing mutual authentication between the vehicle and infrastructure aiming at improving positioning data privacy. Finally, we propose a vision on the development and standardization aspects of such systems.

Cooperative Position Prediction: Beyond Vehicle-to-Vehicle Relative Positioning

Reliable and accurate relative position prediction techniques are vital to the integrity of cooperative intelligent transportation systems (C-ITS) because most C-ITS safety applications should constantly access a (relative-) localization mechanism that satisfies not only a certain (relative-) position accuracy range but also a minimal computational complexity. A global navigation satellite system (GNSS) coupled with dedicated short-range communications (DSRC) links is a candidate for the localization system required for vehicular relative positioning. The vehicle-to-vehicle (V2V) exchange of safety-related data such as raw GNSS measurements, as accurate and frequent as possible, via DSRC links allows the vehicular localization mechanism to maintain real-time relative positioning (RRP) vectors. However, V2V DSRC safety messages may include inaccurate positioning data or may fast become outdated, and/or DSRC links may occasionally fail due to various adverse scenarios including network overheads that drop the bandwidth of vehicular ad-hoc networks. Hence, each DSRC-enabled vehicle may not necessarily know the latest positioning data of neighboring vehicles. This calls upon reliable and accurate relative position prediction mechanisms to cover the DSRC-related deficiencies of relative positioning. This paper, at first, studies the benefits of a terrestrial communications system complementing DSRC-based V2V RRP and then considers position prediction techniques suitable for V2V RRP and examines a few existing solutions and makes a detailed comparison based on a set of required positioning performance parameters for vehicle safety application.

Sustainable Adoption of Connected Vehicles in the Brazilian Landscape: Policies, Technical Specifications and Challenges

This review addresses the intervehicular communication in Connected Vehicles (CV) by emphasizing V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) communications in terms of evolution, current standards, state-of-the-art studies, embedded devices, simulation, trends, challenges, and relevant legislation. This review is based on studies conducted from 2009 to 2019, government reports about the sustainable deployment of these technologies and their adoption in the Brazilian automotive market. Moreover, WAVE (Wireless Access in Vehicular Environment) and DSRC (Dedicated Short-range Communication) standards, the performance analysis of communication parameters and intervehicular available at the market are also described. The current status of ITS (Intelligent Transportation System) development in Brazil was reviewed, as well as the research institutes and governmental actions focused on introducing the concept of connected vehicles into the society. The Brazilian outlook for technological adoption concerning CVs was also discussed. Moreover, challenges related to technical aspects, safety and environmental issues, and the standardization for vehicle communication are also described. Finally, this review highlights the challenges and proposals from available technologies devoted to the roads and vehicular infrastructure communication, their evolution and upcoming trends.

High-gain PDMS-magnetite zero refractive index metamaterial antenna for Vehicle-to-Vehicle communications

This paper presents the simulation design of a high-gain antenna using zero refractive index fishnet metamaterial (MTM) perforated on PDMS-Magnetite substrate for Vehicle-to-Vehicle (V2V) communications. In order to design the MTM, magnetite nanoparticles, 10-nm iron oxide (Fe3O4) are dispersed into polydimethylsiloxane (PDMS) matrix. Subsequently, the unit cell is designed by removing the circular hole with radius of 3.69 mm on the PDMS-Magnetite substrate layer and arranged in 5×5 array fishnet configuration. This optimized MTM is inserted between the antenna design and pure PDMS substrate to improve the gain. The characteristic of the respective unit-cell is investigated to operate at 5.9 GHz and the effectiveness of MTM is performed by comparing the antenna performance with and without MTM. The unique characteristics of zero refractive index transform the diverging wave into plane wave for perfectly parallel wave impact on the design to improve the directivity and gain of the antenna. The proposed MTM into design improves the antenna gain to 7.36 dB without having to compromise other antenna parameters of return loss, Voltage Standing Wave Ratio (VSWR), gain, directivity, efficiency, current distribution, radiation pattern and bandwidth. These advantages has made proposed antenna as a suitable candidate for V2V in Dedicated Short Range Communication (DSRC) application since high-gain directional antenna is required to increase the sensitivity towards signals coming from certain direction.

V2V System Congestion Control Validation and Performance

Major international automakers have considered the deployment of the 5.9 GHz dedicated short-range communications (DSRC) on their vehicle fleets for wireless connectivity. DSRC-enabled vehicle-to-vehicle (V2V) communication through broadcast of basic safety messages enables safety applications for crash warning and avoidance. However, in dense traffic conditions as the V2V deployment scales up, the resultant channel load increases and leads to channel congestion and may adversely affect the performance of the safety applications. The Society of Automotive Engineers J2945/1 standard that builds atop Institute of Electrical and Electronics Engineers (IEEE) 802.11p and IEEE 1609 standards provides the minimum performance requirements for V2V safety communications. Specifically, it provides a congestion control protocol for transmission rate and power adaptations to achieve robust performance in dense vehicular networks. The primary contribution of this paper is that using a congestion generation testbed that emulates channel congestion including a large number of remote vehicles, we can validate and test any V2V equipped vehicle for compliance with the J2945/1 standard. Our paper also demonstrates that under heavy congestion, even with 600 ms of inter-transmit time, a moving vehicle can be tracked to a lane-level accuracy.

Millimetre‐wave massive MIMO for cellular vehicle‐to‐infrastructure communication

Autonomous driving is delightedly an innovative and revolutionary paradigm for future intelligent transport systems. To be fully functional and efficient, vehicles will use hundreds of sensors and generate terabytes of data that will be used and shared for safety, infotainment and allied services. Communication among vehicles or between vehicle and infrastructure thus requires data rate, latency and reliability far beyond what the legacy dedicated short-range communication (DSRC) and long-term evolution-advanced (LTE-A) systems can support. In this work, the authors motivate the use of millimetre-wave (mmWave) massive multiple-input multiple-output (MIMO) technology to facilitate gigabits-per-second (Gbps) communication for cellular vehicle-to-infrastructure scenarios. As a fundamental component, the authors characterise the mmWave massive MIMO vehicular channel using metrics such as path loss, root-mean-square delay spread, Rician K -factor, cluster and ray distribution, power delay profile, channel rank and condition number as well as data rate. They compare the mmWave performance with the DSRC and LTE-A capabilities, and offer useful insights on vehicular channels. The results show that mmWave massive MIMO can deliver Gbps data rates for next-generation vehicular networks.

Multi-Layer Problems and Solutions in VANETs: A Review

The Dedicated Short Range Communication (DSRC) technology supports the vehicular communications through Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) Communication, by operating at 5.9 GHz band (U.S. Standard). The Physical (PHY) and Medium Access Control (MAC) Layer are defined by the IEEE 802.11p, while the IEEE 1609 family of standards define the Wireless Access in Vehicular Environment (WAVE); a suite of communication and security standards in the Vehicular Area Networks (VANETs). There has been a lot of research regarding several challenges in VANETs, from spectrum utilization to multichannel operation and from routing to security issues. The aim of all is to improve the performance of the network and support scalability in VANETs; which is defined as the ability of the network to handle the addition of vehicles (nodes) without suffering noticeable degradation of performance or administrative overhead. In this paper, we aim to highlight multilayer challenges concerning the performance of the VANETs, the already proposed solutions, and the possible future work.

GPS-free vehicular localization system using roadside units with directional antennas

The success of dedicated short-range communications (DSRC) applications depends on an accurate knowledge of the positions of vehicles within the network. At present, vehicle localization is generally performed using some form of vehicle-mounted global positioning system (GPS). However, GPS signals may not be always available. Various GPS-free vehicle localization techniques using the ranging information, the prior knowledge of the vehicle's position, or the special hardware have been proposed for enhancing the performance of traditional GPS-based methods. Different from the previous approaches, we propose a GPS-free vehicle localization system using roadside units (RSUs) with directional antennas without specific hardware support on the vehicle and the assumption of the prior knowledge of the vehicle. In the proposed approach, each vehicle determines its position using the information contained within beacon messages transmitted by neighboring RSUs deployed along the road only. The performance of the proposed localization scheme is evaluated by ns-2 simulations and is compared with those of recent GPS-free and GPS-assisted localization systems. The simulation results show that the proposed localization scheme achieves a higher positioning accuracy than the existing GPS-free and GPS-assisted schemes. The feasibility of the proposed system for practical applications is further investigated experimentally. The experimental results for the positioning accuracy are consistent with those obtained from the ns-2 simulations.