Vehicular Communication Environment Research Articles

Design of a compact quad-port wideband printed MIMO antenna for vehicular communication environment

ABSTRACT A wideband quad-port Multiple Input Multiple Output (MIMO) antenna with efficient isolation characteristics for 5 G New Radio (5 G-NR) applications is presented. The proposed antenna works in the span of 3–6 GHz frequency covering the n77/n78/n79 bands also covering the 5 G-V2X band (3300–5000 MHz), LTE-46 band (5150–5925 MHz) and Dedicated Short Range Communication (DSRC) at 5.9 GHz band. The single element of the MIMO antenna structure is modelled by an elliptical structure stacked on a rectangular patch along with the feedline on the conducting plane. The ground plane is modelled as a rhombic design providing a Defected Ground Structure (DGS) to provide the resonance at the desired frequency band. Later, the antenna is evolved as a four-element MIMO system by arranging the antenna elements orthogonal to each other with a distance of separation between the adjacent antenna elements considered to be 10 mm. The all-inclusive dimension of the proposed antenna is 65 × 65 mm2. The simulated MIMO structure is fabricated and measured for antenna parameters and MIMO parameters such as isolation characteristics and MIMO diversity performances. The simulated values are obtained at acceptable limits with the measured values for the proposed wideband frequency range of operation. The compact, wideband antenna is found to be suitable for various 5 G-NR applications, especially Wi-Fi-assisted Vehicular Communication in the sub-6 GHz frequency range.

A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks

Efficient spectrum sharing is essential for maximizing data communication performance in Vehicular Networks (VNs). In this article, we propose a novel hybrid framework that leverages Multi-Agent Reinforcement Learning (MARL), thereby combining both centralized and decentralized learning approaches. This framework addresses scenarios where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. We introduce the QMIX technique with the Deep Q Networks (DQNs) algorithm to facilitate collaborative learning and efficient spectrum management. The DQN technique uses a neural network to approximate the Q value function in high-dimensional state spaces, thus mapping input states to (action, Q value) tables that facilitate self-learning across diverse scenarios. Similarly, the QMIX is a value-based technique for multi-agent environments. In the proposed model, each V2V agent having its own DQN observes the environment, receives observation, and obtains a common reward. The QMIX network receives Q values from all agents considering individual benefits and collective objectives. This mechanism leads to collective learning while V2V agents dynamically adapt to real-time conditions, thus improving VNs performance. Our research finding highlights the potential of hybrid MARL models for dynamic spectrum sharing in VNs and paves the way for advanced cooperative learning strategies in vehicular communication environments. Furthermore, we conducted an in-depth exploration of the simulation environment and performance evaluation criteria, thus concluding in a comprehensive comparative analysis with cutting-edge solutions in the field. Simulation results show that the proposed framework efficiently performs against the benchmark architecture in terms of V2V transmission probability and V2I peak data transfer.

SECMACE+: Upscaling Pseudonymous Authentication for Large Mobile Systems

The central building block of secure and privacy-preserving Vehicular Communication (VC) systems is a Vehicular Public Key Infrastructure (VPKI), which provides vehicles with multiple anonymized credentials, termed pseudonyms. These pseudonyms are used to ensure VC message authenticity and integrity while preserving vehicle (thus passenger) privacy. In the light of emerging large-scale multi-domain VC environments, the efficiency of the VPKI and, more broadly, its scalability are paramount. By the same token, preventing misuse of the credentials, in particular, Sybil-based misbehavior, and managing “honest-but-curious” VPKI entities are other facets of a challenging problem. In this paper, we leverage the state-of-the-art VPKI system and enhance its functionality towards a highly-available, dynamically-scalable, and resilient design; this ensures that the system remains operational in the presence of benign failures or resource depletion attacks, and that it dynamically scales out, or possibly scales in, according to request arrival rates. Our full-blown implementation on the Google Cloud Platform shows that deploying large-scale and efficient VPKI can be cost-effective: the processing latency to issue 100 pseudonyms is approximately 56 ms. More so, our experiments show that our VPKI system dynamically scales out or scales in according to the rate of pseudonyms requests. We formally assess the achieved security and privacy properties for the credential acquisition process. Overall, our scheme is a comprehensive solution that complements standards and can catalyze the deployment of secure and privacy-protecting VC systems.

Artificial intelligence support for 5G/6G-enabled Internet of Vehicles networks: An overview

Improving transportation efficiency and on-road safety using Intelligent Transportation Systems (ITSs) has become crucial as road congestion and vehicle complexity increase coupled with ongoing rapid development and deployment of electric vehicles across the globe. Recent advances in computer systems and wireless communications have ushered in more possibilities for smart solutions to road traffic safety, congestion reduction, convenience, and overall efficiency. The evolution and deployment of 5G have opened up new technologies and features that can provide the much needed high-mobility wireless networks for the emerging Internet of Vehicles (IoV). The application of AI consisting of Deep Learning (DL), Machine Learning (ML) and Swarm Intelligence (SI) techniques have emerged in both conventional and vehicular wireless networks with strong promises towards enhancing traditional data-centric methods. Particularly, in the application domains of IoV, big data is frequently generated from various sources within the vehicular communication environment. The collected big data is usually processed and used for both safety and infotainment services including routing, broadening drivers' awareness, traffic mobility prediction for hazardous situation avoidance to improve overall safety and passenger comfort, and general quality of road experience. Applying data-driven methods enables AI to address high mobility and dynamic vehicular communications and network issues facing traditional solutions and approaches like network optimization techniques and conventional control loop design. This study provides a concise review of DL, ML and SI techniques and applications that are currently being explored by different research efforts within the application area of vehicular networks. The paper further discusses the strengths and weaknesses of the proposed AI-based solutions for the IoV networks.

Enhancing OLSR protocol in VANETs with multi-objective particle swarm optimization

The Optimized Link State Routing Protocol (OLSR) is envisioned as one of the key components to the operation of future vehicular ad hoc networks (VANETs). Its performance is however fairly constrained in the vehicular communication environment due to the limited coverage of wireless transmission and high-speed movement of nodes. In this brief, we propose a multi-objective particle swarm optimization (MOPSO) framework to enhance the performance of OLSR in VANETs. Specifically, we formulate a multi-objective optimization problem (MOP) considering not only the quality of service (QoS), including throughput, delay and packet loss rate, but also the cost of service, i.e., routing load. We solve this MOP with MOPSO and obtain the Pareto front corresponding to the optimal equilibrium of performance and cost. In the experiment, our optimization framework is used in a general setting of VANETs to obtain the optimal parameters of OLSR, i.e., Hello and TC intervals, and these parameters are further validated in realistic VANET scenarios. Our optimization framework can be naturally extend to other dynamic routing protocols in networks.

Deep learning-based channel quality indicators prediction for vehicular communication

Vehicular communication shares essential information for safety and convenience. Vehicular communication must guarantee a high transmission rate with stable communication. In vehicular communication environments, the channel characteristic frequently varies due to the high-speed movement of the vehicles. Understanding the channel conditions is essential to maintain stable communication. We propose an optimal channel quality indicator (CQI) prediction model for expecting channel characteristics. Our prediction model defines the CQI from the received signal strength indication (RSSI) and is applied to the IEEE 802.11p wireless access in vehicular environments (WAVE) standard. The prediction part applies robust long-short term memory (LSTM) network to sequential data. The CQI prediction model is trained and evaluated using vehicular communication data collected by an IEEE 802.11p WAVE device. We compare the prediction performance of the proposed model with the auto-regressive integrated moving average, support vector regression, and multilayer perception models.

Service-Centric Heterogeneous Vehicular Network Modeling for Connected Traffic Environments.

Heterogeneous vehicular communication on the Internet of connected vehicle (IoV) environment is an emerging research theme toward achieving smart transportation. It is an evolution of the existing vehicular ad hoc network architecture due to the increasingly heterogeneous nature of the various existing networks in road traffic environments that need to be integrated. The existing literature on vehicular communication is lacking in the area of network optimization for heterogeneous network environments. In this context, this paper proposes a heterogeneous network model for IoV and service-oriented network optimization. The network model focuses on three key networking entities: vehicular cloud, heterogeneous communication, and smart use cases as clients. Most traffic-related data–oriented computations are performed at cloud servers for making intelligent decisions. The connection component enables handoff-centric network communication in heterogeneous vehicular environments. The use-case-oriented smart traffic services are implemented as clients for the network model. The model is tested for various service-oriented metrics in heterogeneous vehicular communication environments with the aim of affirming several service benefits. Future challenges and issues in heterogeneous IoV environments are also highlighted.

Analysis and design of fractal antenna for efficient communication network in vehicular model

Vehicular communication plays a dominant role in providing better safety and security with high rate of data communication in next generation networking infrastructure, and one of the prominent areas for research as well. As the growth in vehicular communication has seen a prominent improvement in vehicle to vehicle communication, vehicle to infrastructure and vehicular to pedestrian communication, to establish a proper communication there is an immediate need of antennas which serves the purpose. This article reveals a comprehensive review and design of vehicular communication antenna and its impact with the help efficient archives for efficient solutions for providing better communication between the devices connected. As the technology is advancing towards higher frequencies, in-turn demands a better performing antenna which provides more energy-efficient networking for smart vehicular communication environments. We have provided a design of antenna which could be used for frequencies ranging from 10GHz to 15GHz, and the performance of the antenna measured in the form of Return losses, Voltage Standing Wave Ratio, and Gain. Three different substrate materials are used in the paper to test and provide a better solution, which are FR4_epoxy, Rogers RT/Duroid 5880tm, and Rogers RO4003. Eventually, the design made ready with altering/recessed ground, and its impact on the performance of the antenna for all the three different substrates is depicted which could be used for vehicle networks. The size of the patch selected as 40mm X 30 mm with an impedance of 50Ω and a lumped port is used to provide the feed.

A Blockchain-Based Searchable Public-Key Encryption With Forward and Backward Privacy for Cloud-Assisted Vehicular Social Networks

As the integration of the Internet of Vehicles and social networks, vehicular social networks (VSN) not only improves the efficiency and reliability of vehicular communication environment, but also provide more comprehensive social services for users. However, with the emergence of advanced communication and computing technologies, more and more data can be fast and conveniently collected from heterogeneous devices, and VSN has to meet new security challenges such as data security and privacy protection. Searchable encryption (SE) as a promising cryptographic primitive is devoted to data confidentiality without sacrificing data searchability. However, most existing schemes are vulnerable to the adaptive leakage-exploiting attacks or can not meet the efficiency requirements of practical applications, especially the searchable public-key encryption schemes (SPE). To achieve secure and efficient keyword search in VSN, we design a new blockchain-based searchable public-key encryption scheme with forward and backward privacy (BSPEFB). BSPEFB is a decentralized searchable public-key encryption scheme since the central search cloud server is replaced by the smart contract. Meanwhile, BSPEFB supports forward and backward privacy to achieve privacy protection. Finally, we implement a prototype of our basic construction and demonstrate the practicability of the proposed scheme in applications.

Dynamic Scheduling Algorithm for Delay-Sensitive Vehicular Safety Applications in Cellular Network

The vehicular safety applications disseminate the burst messages during an emergency scenario, but effort to reduce delay of communication are hampered by wireless access technology. As conventional VANET (Vehicular ad-hoc network) connected intermittently, the LTE-based framework has been established for the vehicular communication environment. However, resource allocation which affected by many factors, such as power, PRB (physical resource block), channel quality, are challenging to guarantee the safety services QoS in LTE downlink for OFDM. In order to solve the problem of safety message dissemination in LTE vehicular network, we proposed a delay-aware control policy by leveraging cross-layer approach to maximize the system throughput. First, we model the resource allocation problem using the queuing theory based on the MISO. Second, the method casts the problem of throughput and latency for dynamic communication system into a stochastic network optimization problem, and then makes tradeoffs between them by Lyapunov optimization technique. Finally, we use the improved the branch and bound algorithm to search optimal solution in system capacity region for these decomposed subproblems. The simulation results show that our algorithm can guarantee the delay while maximum system throughput

Beacon inter-reception time based adaptive beaconing (BIRA) for congestion-aware vehicular networks

The vehicular networks heavily rely on beaconing for traffic safety and congestion awareness. These periodic broadcasted safety messages may lead to heavy data traffic, resulting in excessive inaccuracy and failure of applications based on vehicular communication environments. With the help of data congestion control schemes,Vehicular networks can be made more reliable and effective. However, these approaches use time-average metrics while not consideringbeacon receptions temporal behavior. In order to resolve the congestion problem, we present a Beacon Inter-Reception time based Adaptive beaconing (BIRA) in this article. The comparative simulation results show that the proposed scheme BIRA is effective in congestion control in comparison with the similar class scheme.

COOPERATIVE SPECTRUM DECISION IN COGNITIVE VEHICULAR NETWORK BASED ON SUPPORT VECTOR MACHINE

Spectrum sensing is one of the core functionality of a cognitive radio system. Accurate identification of primary system signal can prevent interference and provide maximum protection to licensed users. However, many challenges associated with spectrum sensing still exist. Examples include shadowing, multipath fading of the primary user signal, hidden primary user problem and many more. To circumvent the challenges faced by sensing device, the cooperative decision from participating devices has been proposed in the literature. In this paper, we suggest a cooperative decision based on support vector machine (SVM) for spectrum sensing in vehicular communication environment to mitigate shadowing and multipath fading. Spectrum sensing can be formulated as apattern recognition problem with sensing results grouped into two distinct classes corresponding to the presence or absence of a primary user signal. Therefore, we employ SVM at the fusion centre to classify the primary signal correctly. The roadside units along the highway are selected to act as the fusion centre where sensing decision is made. The performance of the proposed scheme is evaluated using receiver operation characteristic curves in MATLAB. We compare the probability of detection for the proposed cooperative scheme to hard fusion rule. The simulation result shows that SVM based cooperative decision performed better than hard fusion rule.

Optical-RoI-Signaling for Vehicular Communications

This paper introduces a new hybrid waveform for optical wireless communication (OWC)/optical camera communication (OCC) systems and discusses the technical considerations of these systems. In a challenged vehicular communication environment, which requires high-speed, high-mobility, and long-distance communication support, implementing a hybrid waveform guarantees high-speed data transmission while reducing the cost of the OWC/OCC systems. The application of a hybrid waveform in the OWC/OCC systems is known as the region-of-interest (RoI)-signaling technique. This technique allows the OWC/OCC systems to simultaneously transmit low-rate and high-rate data streams. The low-rate data stream is used to detect and track the RoI of light sources for setting up the communication link, whereas the high-rate data stream is used for high-speed data transmission. Selection of proper modulation schemes for two simultaneous data streams is also discussed in this paper. A new modulation scheme, such as spatial-2-phase-shift-keying (S2-PSK), is proposed for the low-rate data stream. This scheme has been used to modify the IEEE 802.15.7-2018 standard, which is the revised version of the IEEE 802.15.7-2011 standard. For the high-rate data stream, single-carrier modulation or multiple-carrier modulation, such as the proposed hybrid-spatial-phase-shift-keying (HS-PSK) or variable pulse-position modulation (VPPM), can serve as viable solutions. Technical considerations for the modulation schemes of each type of data stream are analyzed to determine the feasibility of the proposed schemes. Finally, the experimental results and numerical parameters of the intended system are presented.

Efficient Maintenance of AODV Routes in the Vehicular Communication Environment with Sparsely Placed Road Side Units

Thanks to the vehicular communication network, vehicles on the road can communicate with other vehicles or nodes in the global Internet. In this study, we propose an enhanced routing mechanism based on AODV so that road side units (RSUs) can provide continuous services such as video streaming services to vehicles which may be intermittently located outside of the coverage areas of RSUs. In the highway environment with sparsely placed RSUs, the communications between RSUs and vehicles are frequently disconnected due to high vehicular speeds. To resolve this problem, both V2I and V2V communications are utilized. In order to reduce the route recovery time and the number of route failures in the sparsely placed RSU environment, backup routes are established through the vehicles with longer direct communication duration with the RSU. The backup route substitutes the main route upon route disconnection. Also, for the efficient handover to the next RSU, the route shortening mechanism is proposed. For the performance evaluation of the proposed mechanism, we carried out the NS-3-based simulations.

Performance of Generalized Frequency Division Multiplexing Based Physical Layer in Vehicular Communications

Vehicular communications enable information exchange among vehicles including the roadside infrastructure. This has led to applications to primarily increase road safety and traffic efficiency. Standardization efforts on vehicular communications are underway. The physical layer (PHY) is defined based on the IEEE 802.11 family of WiFi standards operating at the 5.9 GHz frequency band and on extensions of long-term evolution (LTE); in both cases orthogonal frequency division multiplexing (OFDM) is the waveform of choice. Since the typical environment of WiFi deployment is very different to the vehicular communication environment, it is a challenging task to adapt the WiFi-based PHY for providing reliable and real-time communications under highly time- and frequency-selective fading channels. In this paper, the employment of an alternative waveform termed generalized frequency division multiplexing (GFDM) for vehicular communication is investigated. Specifically, a GFDM-based packet design is proposed on the basis of the standard-compliant OFDM-based PHY configuration. On the receiver side, this paper focuses on developing synchronization, channel estimation, and equalization algorithms. The performance of the resulting GFDM-based PHY is verified and compared with the OFDM-based one by means of simulation. The obtained results demonstrate that the proposed GFDM-based PHY can utilize the time and frequency resources more efficiently, and outperform particularly under challenging channel conditions. Additionally, the low out-of-band emission of GFDM is a desirable feature for future multichannel operation (MCO) in vehicular communications.

Efficient data dissemination in cooperative multi-RSU Vehicular Ad Hoc Networks (VANETs)

Many safety and non-safety related applications have been envisioned in VANETs. However, efficient data dissemination considering the mobility of vehicle is must for the success of these applications. Although the Road Side Unit (RSU) is a stationary unit, both RSU and vehicle have limited transmission range that restricts to shorter connection time. This endures a higher request drop rate especially at the overloaded RSUs. A cooperative load balancing (CLB) among the RSUs to use their residual bandwidth can be an effective solution to reduce the request drop rate. In this paper, we investigate that considering the remaining delay tolerance of submitted requests and the knowledge of fixed road layout, the performance of the cooperative load balancing system can be further improved significantly. We show that this performance gain comes from serving the requests based on the urgency and the efficient load balancing among the junction-RSUs and edge-RSUs. Based on the observations, we propose an Enhanced CLB (ECLB) approach in this paper. To demonstrate the efficiency of the ECLB approach a number of well-known scheduling algorithms are integrated and an extensive simulation experiments are conducted in the vehicular communication environment that supports the superiority of ECLB over the existing approaches.

Performance analysis of hybrid WiMAX/DSRC scenarios for vehicular communication environment

Internet becomes a vital and important service being integrated in the current scenarios of competing technologies, therefore there is a need for higher speed broadband access. Emergence and evolutions of technologies from the first generation to 4G LTE then to the promising 5G and WiMAX. For achieving high data rates and services to multiple users, WiMAX is one of the competing technology, which was introduced by the Institute of Electrical and Electronic Engineers (IEEE) designated for fixed wireless and mobile wireless applications and Dedicated Short Range Communication (DSRC) is another technology which is a trend in the vehicular network in the current situations employed in the Intelligent Transportation System in the United States which supports high speed data rates. In this paper, the authors considered a hybrid scenario and compared the communication link taken place in the DSRC-to-DSRC traditional system with the hybrid DSRC-WiMAX system communication link namely DSRC-to-Fixed WiMAX and Mobile WiMAX-to-Fixed WiMAX, with evaluation of performance by considering certain parameters simulated in MATLAB.

IEEE 802.11p에서 파일럿 부반송파를 이용한 채널추정 기법

이동 중의 차량에서 멀티 서비스를 지원받고자 하는 수요가 증가함에 따라 통신시스템과 차량산업을 융합하는 기술에 대한 관심이 증가하고 있다. 이러한 서비스가 성공적으로 수행되기 위해서는 신뢰성 있는 정보의 전달과 우수한 수신성공률이 보장되어야 한다. 따라서 정확한 채널 값을 추정 할 수 있는 기법이 필요하다. 이 논문은 고속주행 환경에서 동작하는 차량용 통신 규격인 IEEE 802.11p에서 시변 채널추정을 위한 PTAU(Pilot Tone Aided Update) 결정지향 기법을 제안한다. 기존의 기법들은 시간영역에서 오류전파가 발생하므로 높은 서비스의 질을 제공하기 어렵다. 이 논문에서는 이러한 문제점의 보완을 위해 파일럿 부반송파들을 사용하였고, 주파수 대역에서 일정한 순서에 따라 추정한 값들을 업데이트시킴을 제안한다. PTAU기법은 기존의 기법들에 비해 낮은 비트오류율을 가져 고속채널에서의 추적 성능을 향상시킴을 검증하였다. It has been grown interests of the convergence technologies about communication systems and vehicular industry. Vehicular communications enable a number of infotainment applications to serve cars under high mobility environments. To achieve this goal, a robust and accurate channel estimation scheme is of great importance. This paper proposes an enhanced Decision-Directed channel estimation scheme called PTAU(Pilot Tone Aided Update) for IEEE 802.11p in vehicular communication environment. Existing approaches which use subsequent data symbols to estimate channel response in time domain will lead to the error propagation. In this paper, we use the pilot subcarriers to get initial channel response, then utilize the correlation characteristics to update channel response in frequency domain. Finally, Analysis and simulation results reveal that the proposed scheme outperforms in bit error rate(BER), significantly improve the performance of the estimation.

Calibrating Communication Simulator for Connected Vehicle Applications

Research efforts focused on the connected vehicle (CV) technology applications typically assume perfect communications among the vehicles and between the vehicles and the roadside equipment. However, a few studies, including this one, pointed out that the wireless communications experience packet drops, which might lead to a serious downgrade of the safety critical CV applications. Thus, the wireless communication simulators used to emulate the communications performance need to be properly calibrated to replicate the real-world vehicular communication environments. This study calibrated an NCTUns simulator for the dedicated short range communications (DSRC) of CV technology using the DSRC field test data executed on an instrumented intersection at the Turner–Fairbank Highway Research Center. Physical layer parameters (e.g., data rate and transmission power) as well as channel models are calibrated. The calibration applied a Latin hypercube sampling technique to generate multiple combinations of parameter sets. The calibrated NCTUns simulator produced much more realistic outputs than the uncalibrated one. Then a signalized intersection was simulated in a case study to further investigate the packet drops of DSRC-based CV communications. The results indicated that there were significant packet drops, requiring further research before implementing safety critical CV applications.

Vehicular WiFi offloading: Challenges and solutions

WiFi offloading is envisioned as a promising solution to the mobile data explosion problem in cellular networks. WiFi offloading for moving vehicles, however, poses unique characteristics and challenges, due to high mobility, fluctuating mobile channels, etc. In this paper, we focus on the problem of WiFi offloading in vehicular communication environments. Specifically, we discuss the challenges and identify the research issues related to drive-thru Internet access and effectiveness of vehicular WiFi offloading. Moreover, we review the state-of-the-art offloading solutions, in which advanced vehicular communications can be employed. We also shed some lights on the path for future research on this topic.