Vehicular Applications Research Articles

A Distributed Mix-Context-Based Method for Location Privacy in Road Networks

Preserving location privacy is increasingly an essential concern in Vehicular Adhoc Networks (VANETs). Vehicles broadcast beacon messages in an open form that contains information including vehicle identity, speed, location, and other headings. An adversary may track the various locations visited by a vehicle using sensitive information transmitted in beacons such as vehicle identity and location. By matching the vehicle identity used in beacon messages at various locations, an adversary learns the location history of a vehicle. This compromises the privacy of the vehicle driver. In existing research work, pseudonyms are used in place of the actual vehicle identity in the beacons. Pseudonyms should be changed regularly to safeguard the location privacy of vehicles. However, applying simple change in pseudonyms does not always provide location privacy. Existing schemes based on mix zones operate efficiently in higher traffic environments but fail to provide privacy in lower vehicle traffic densities. In this paper, we take the problem of location privacy in diverse vehicle traffic densities. We propose a new Crowd-based Mix Context (CMC) privacy scheme that provides location privacy as well as identity protection in various vehicle traffic densities. The pseudonym changing process utilizes context information of road such as speed, direction and the number of neighbors in transmission range for the anonymisation of vehicles, adaptively updating pseudonyms based on the number of a vehicle neighbors in the vicinity. We conduct formal modeling and specification of the proposed scheme using High-Level Petri Nets (HPLN). Simulation results validate the effectiveness of CMC in terms of location anonymisation, the probability of vehicle traceability, computation time (cost) and effect on vehicular applications.

Positioning and Sensing for Vehicular Safety Applications in 5G and Beyond

This article presents a shared vision among stakeholders across the value chain on the use of radio positioning and sensing for road safety in the 5G ecosystem. The key enabling technologies and architectural functionalities are explored, focusing on the extremely stringent localization and communication requirements. A case study for joint radar and communication using experimental data showcases the potential of the new enablers that are paving the way toward enhanced road safety in beyond 5G scenarios.

Positioning and Sensing for Vehicular Safety Applications in 5G and Beyond

Positioning and Sensing for Vehicular Safety Applications in 5G and Beyond

Artificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles

Abstract Internet of Vehicles (IoV) is a network of vehicles communicating with each other by exchanging road traffic information via radio access technologies. Two potential technologies of V2X that have gained attention over the past years are DSRC and cellular networks such as 4G LTE and 5G. DSRC is suitable for low latency communications, however provides a shorter coverage range whereas, 4G LTE offers a wide coverage range but has high transmission time intervals. In contrast, 5G offers higher data rates, low latencies but prone to blockages. Single technology might not fully accommodate the requirements of vehicular communications. Hence, it is required to interwork with more than one radio access network to satisfy the requirements of safety vehicular applications. One issue identified when working with multiple radio access networks is the selection of the most appropriate network for vertical handover. Usually, in the previous works, the network is selected directly or will be connected to the available network due to which the handover had to take place frequently resulting in unnecessary handovers. Hence, in the existing state-of-the-art, the need for handover is not validated. In this paper, we have proposed a dynamic Q-learning algorithm to validate the need for handover, and then, appropriate selection of network would take place by using a fuzzy convolutional neural network. Besides, a modified jellyfish optimization algorithm is proposed to select the shortest paths by forming V2V pairs that take into account channel metrics, vehicle metrics, and vehicle performance metrics. The proposed algorithms are then evaluated using OMNET++ and compared with the existing state-of-the-art concerning mean handover, HO failure, throughput, delay, and packet loss as the performance metrics.

The Utility of DSRC and V2X in Road Safety Applications and Intelligent Parking: Similarities, Differences, and the Future of Vehicular Communication.

As the technological advancement in the automotive field increases and the complexity of vehicle and infrastructure applications is extremely high, new directions and approaches are needed in this field. Supporting and developing vehicular applications dedicated to road safety by analyzing the current behavior of existing networks in various forms is imperative. This paper studies and implements a DSRC-type communications infrastructure that receives a set of controllable and adjustable indicators, which can provide messages to network drivers in a timely manner. The implementation is based on the 802.11p protocol and initially addresses pedestrian infrastructure or pedestrian safety, controlled areas, and perimeters that allow intelligent communications. The design and setting of the communication parameters in the lower layer of the DSRC stack for vehicle applications are part of this work, aspects that are also relevant in the case of autonomous vehicles.

Design, modeling and testing of a new compressive amplifier structure for piezoelectric harvester

In recent years, harnessing electrical energy from mechanical vibration by using a piezoelectric energy harvester (PEH) has attracted much attention from researchers. This sustainable energy harvester is useful for wireless sensor network, where a replacement or replenishment of an energy source such as a battery is impractical. From previous studies, the amount of energy generated by the PEH is very limited even in a high force environment. To solve this issue, mechanical amplifier structure such as Cymbal structure is implemented to amplify the tensile loading force towards the PEH. In terms of the material strength perspective, this performance can be further enhanced by using a compressive-type mechanical amplifier structure, as the compressive yield strength of piezoelectric material is much higher than its tensile yield strength. In this study, a compressive structural design which is named as Hull structure is proposed. Several techniques included analytical model analysis, finite element analysis (FEA), and experimental testing have been used to evaluate its performance. It shows a force amplification factor of 9.72 at 6° through the analytical model. From the FEA result, the proposed Hull structure shows great potential in enhancing the power output of 11.34 mW, which is 3.08 times larger than the benchmarking Tensile Cymbal structure. It also shows 5.28 times greater output voltage than the benchmark case in the experiment. Besides, it has a great advantage of providing a wider area for excitation loading force which increases the PEH’s load capacity and suitable for the vehicular excitation application.

Intelligent and resizable control plane for software defined vehicular network: a deep reinforcement learning approach

Software-defined networking (SDN) has become one of the most promising paradigms to manage large scale networks. Distributing the SDN control proved its performance in terms of resiliency and scalability. However, the choice of the number of controllers to use remains problematic. A large number of controllers may be oversized inducing an overhead in the investment cost and the synchronization cost in terms of delay and traffic load. However, a small number of controllers may be insufficient to achieve the objective of the distributed approach. So, the number of used controllers should be tuned in function of the traffic charge and application requirements. In this paper, we present an intelligent and resizable control plane for software defined vehicular network architecture, where SDN capabilities coupled with deep reinforcement learning (DRL) allow achieving better QoS for vehicular applications. Interacting with SDVN, DRL agent decides the optimal number of distributed controllers to deploy according to the network environment (number of vehicles, load, speed etc.). To the best of our knowledge, this is the first work that adjusts the number of controllers by learning from the vehicular environment dynamicity. Experimental results proved that our proposed system outperforms static distributed SDVN architecture in terms of end-to-end delay and packet loss.

Study of Ruthenium Contamination Effect on Oxygen Reduction Activity of Platinum-Based PEMFC and DMFC Cathode Catalyst

Proton exchange membrane fuel cells (PEMFC) and PEM-based direct methanol fuel cells (DMFC) are promising energy sources for vehicular, stationary and portable applications. While PEMFC-based power systems have already reached a limited commercialization level, durability remains to be one of the main obstacles on the path towards their broad commercialization. One of the degradation mechanisms reducing durability of reformate-based PEMFC and DMFC is ruthenium contamination of the cathode catalyst [i] following ruthenium dissolution from the anode PtRu catalyst and its cross-over to the cathode [ii].The aim of our research is to quantitatively measure the effect of ruthenium contamination on the oxygen reduction reaction (ORR) activity of a typical cathode catalyst. For this purpose, various and precisely known amounts of ruthenium (equivalent to 0.15-4 ruthenium monolayers) were deposited on a commercial 50%Pt/C catalyst (Johnson Matthey HiSPEC8000) using electroless reduction method. The obtained Ru-contaminated Pt/C catalysts were examined using TGA, SEM-EDS, XPS, HR-TEM and STEM-EDS to determine their composition, particles size distribution and structure. Cyclic-voltammetry with rotating–disk–electrode was used to quantitatively determine the effect of ruthenium contamination on the ORR specific activity.It was found that ORR activity exhibits a pseudo-exponential decay with increased ruthenium coverage. At a contamination level of 0.15 equivalent ruthenium monolayer (EDS %at Ru3Pt97, XPS %at Ru7Pt93) a massive, ~45%, decrease in ORR specific activity was measured. At a contamination level of 1 equivalent ruthenium monolayer (EDS %at Ru27Pt73, XPS %at Pt50Ru50) almost a complete inhibition of ORR activity was observed. Ruthenium deposition on platinum in the form of bi-layer islands and 3d-clusters and deactivation of platinum atoms surrounding ruthenium deposition sites were proposed as a possible explanation for the pseudo-exponential decay of the ORR activity. The obtained results allowed us to estimate the decrease in power density of reformate-based PEMFC and DMFC due to ruthenium contamination by 0.15 equivalent monolayer at roughly 10%.Assuming that the decrease in ORR activity is originating from the strong O and OH binding to ruthenium, DFT-calculated O and OH binding energies to ruthenium and platinum were used to estimate ORR activity on model Run/Pt(111) surfaces that represent Ru-contaminated Pt/C catalyst. The estimated ORR activities have shown a dependence on ruthenium contamination that was qualitatively similar to experimental results, indicating that the strong O and OH binding to ruthenium have indeed a negative effect on the ORR activity.[i] L. Gancs, B. N. Hult, N. Hakim, S. Mukerjee, Electrochem. Solid-State Lett., 2007, 10, 9, B150[ii] P. Piela, C. Eickes, E. Brosha, F. Garzon, P. Zelenay, Journal of The Electrochemical Society, 2004, 151 (12), A2053–A2059

Super-resolution for doubly-dispersive channel estimation

In this work we consider the problem of identification and reconstruction of doubly-dispersive channel operators which are given by finite linear combinations of time-frequency shifts. Such operators arise as time-varying linear systems for example in radar and wireless communications. In particular, for information transmission in highly non-stationary environments the channel needs to be estimated quickly with identification signals of short duration and for vehicular application simultaneous high-resolution radar is desired as well. We consider the time-continuous setting and prove an exact resampling reformulation of the involved channel operator when applied to a trigonometric polynomial as identifier in terms of sparse linear combinations of real-valued atoms. Motivated by recent works of Heckel et al. we present an exact approach for off-the-grid super-resolution which allows to perform the identification with realizable signals having compact support. Then we show how an alternating descent conditional gradient algorithm can be adapted to solve the reformulated problem. Numerical examples demonstrate the performance of this algorithm, in particular in comparison with a simple adaptive grid refinement strategy and an orthogonal matching pursuit algorithm.

ToA-Based Localization of Far-Away Targets: Equi-DOP Surfaces, Asymptotic Bounds, and Dimension Adaptation

This paper studies the Dilution of Precision (DOP) in the Time-of-arrival (ToA)-based localization of targets outside the anchors’ convex hull. In the far-away target regime, we derive a closed-form expression of the DOP that reveals a linear asymptotic scaling law. We characterize the asymptotic DOP bounds, and equi-DOP surfaces/contours in 3D/2D localization scenarios, which quantifies the reliability of location estimates on the target's trajectory. Motivated by vehicular applications, we propose a range-aided dimension adaptation scheme. Here the localization dimension is adapted in real-time using a single range measurement such that the maximum/root-mean-square (rms) DOP does not exceed a threshold. Since high-accuracy localization of far-away targets is infeasible due to linear DOP scaling with distance, this scheme prioritizes high-dimensional tracking of nearby targets while monitoring far-away targets with range-only measurements.

Agent-Based Model Predictive Controller (AMPC) for Flexible and Efficient Vehicular Control

The rapid development of advanced vehicular mechatronic systems help to bring vehicle performances to a higher level. To fully unleash the power of such systems, an integrated vehicular control framework has been widely adopted. All actuators, along with their impact on vehicle dynamics are considered in a holistic model, and control performances based on such models are highly optimized thanks to a centralized distribution process. However, the strong reliance on a complex model with details for all subsystems limits the scalability and re-usability of resultant controllers. Any changes from vehicle topology to actuator details will lead to a controller redesign. In some cases, execution logic of actuators might even be proprietary and cannot be incorporated into a centralized coordination scheme. A flat architecture is proposed in our previous research to address such limitations, and a distributed multi-agent control scheme is suggested for a specialized modular vehicle platform. This paper generalizes such approach, and focuses on addressing model scalability and re-usability in complex vehicular active safety applications. When properly formulated, the proposed agent-based cooperative controllers are shown to give no compromise to control performances as their integrated-designed counterparts. The proposed framework also features greater flexibility in handling vehicle configuration changes as well as actuator modelling granularities. Incorporation of proprietary subsystems as “black-box” agent modules expands the coverage of the proposed architecture for advanced vehicular mechatronic systems.

Research on a Task Offloading Strategy for the Internet of Vehicles Based on Reinforcement Learning.

Today, vehicles are increasingly being connected to the Internet of Things, which enables them to obtain high-quality services. However, the numerous vehicular applications and time-varying network status make it challenging for onboard terminals to achieve efficient computing. Therefore, based on a three-stage model of local-edge clouds and reinforcement learning, we propose a task offloading algorithm for the Internet of Vehicles (IoV). First, we establish communication methods between vehicles and their cost functions. In addition, according to the real-time state of vehicles, we analyze their computing requirements and the price function. Finally, we propose an experience-driven offloading strategy based on multi-agent reinforcement learning. The simulation results show that the algorithm increases the probability of success for the task and achieves a balance between the task vehicle delay, expenditure, task vehicle utility and service vehicle utility under various constraints.

Sustainable and Trustworthy Edge Machine Learning

Nowadays, our world is driven by complex, large scale, yet tactile information systems requiring various degrees of trustworthiness. Trustworthiness of the systems always comes with costs. The traditional and rather costly way to understand the behavior of large scale systems is to develop powerful mathematical abstractions that allow us to condense these behaviors and to reason about them at a very abstract level. In our FWF funded project Rucon, we introduce an orthogonal, data driven, and probabilistic concept to model and reason uncertainty of the systems. In Rucon, deliberated system failures are tolerated due to the benefits of the costs and sustainability. Rucon's approach targets large scale near real-time systems like live video analytics, streaming, vehicular applications, and smart city information systems.

A cooperative heterogeneous vehicular clustering framework for efficiency improvement

Heterogeneous vehicular clustering integrates multiple types of communication networks to work efficiently for various vehicular applications. One popular form of heterogeneous network is the integration of long-term evolution (LTE) and dedicated short-range communication. The heterogeneity of such a network infrastructure and the non-cooperation involved in sharing cost/data are potential problems to solve. A vehicular clustering framework is one solution to these problems, but the framework should be formally verified and validated before being deployed in the real world. To solve these issues, first, we present a heterogeneous framework, named destination and interest-aware clustering, for vehicular clustering that integrates vehicular ad hoc networks with the LTE network for improving road traffic efficiency. Then, we specify a model system of the proposed framework. The model is formally verified to evaluate its performance at the functional level using a model checking technique. To evaluate the performance of the proposed framework at the micro-level, a heterogeneous simulation environment is created by integrating state-of-the-art tools. The comparison of the simulation results with those of other known approaches shows that our proposed framework performs better.

High-Density Platooning in Cellular Vehicle-To-Everything Systems: On the Importance of Communication-Aware Networked Control Design

Fifth-generation (5G) cellular networks enable many vehicular communication applications by providing wireless interfaces for the exchange of messages among vehicles [vehicle to vehicle (V2V)] and among vehicles and pedestrians, infrastructure components, and application servers [vehicle to everything (V2X)]. Platoon networked control systems to maintain intervehicle distance (IVD) represent one of the most demanding use cases in V2X. Closed-loop feedback controllers introduced decades ago have been demonstrated to work with string stability and IVDs of only a few meters at highway speeds. However, the effects of temporally and spatially correlated packet losses, as experienced at the cell edge and during handovers, have not been sufficiently analyzed.

Optimal Charging Profile Design for Attaining Desired State of Charge in Symmetric Electrochemical Capacitor With Efficiency Analysis

The optimal utilization of ultracapacitor (symmetric electrochemical capacitor) for vehicular applications demands continuous monitoring and maintenance of its state of charge (SOC). The online estimation of SOC plays a vital role in the effective utilization of the energy storage capacity of ultracapacitors. In addition to monitoring SOC, high reliability in vehicle operation requires a predefined SOC level, as per the dynamic load requirement. For the ultracapacitor undergoing series of charge-discharge cycles, proper regulation of the SOC in accordance with the vehicle drive profile, increases the ultracapacitor life by avoiding possible stress due to undercharging/overcharging. Achieving the desired SOC in real-time warrants a mechanism for the optimal charging of the ultracapacitor. In this regard, the present article proposes an optimal charging scheme to attain a predefined SOC from any initial value, within a given time-frame. The proposed noniterative fractional calculus-based approach allows for analytically representing the charging current as a function of the desired SOC, charging duration and ultracapacitor model parameters. The scheme has been extensively validated experimentally for its effectiveness in attaining the desired SOC. Further, the appropriateness of the derived charging profiles for different operating scenarios has also been analyzed in terms of maintaining a tradeoff between faster charging and high charging efficiency.

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Employing machine learning into 6G vehicular networks to support vehicular application services is being widely studied and a hot topic for the latest research works in the literature. This article provides a comprehensive review of research works that integrated reinforcement and deep reinforcement learning algorithms for vehicular networks management with an emphasis on vehicular telecommunications issues. Vehicular networks have become an important research area due to their specific features and applications such as standardization, efficient traffic management, road safety, and infotainment. In such networks, network entities need to make decisions to maximize network performance under uncertainty. To achieve this goal, Reinforcement Learning (RL) can effectively solve decision-making problems. However, the state and action spaces are massive and complex in large-scale wireless networks. Hence, RL may not be able to find the best strategy in a reasonable time. Therefore, Deep Reinforcement Learning (DRL) has been developed to combine RL with Deep Learning (DL) to overcome this issue. In this survey, we first present vehicular networks and give a brief overview of RL and DRL concepts. Then we review RL and especially DRL approaches to address emerging issues in 6G vehicular networks. We finally discuss and highlight some unresolved challenges for further study.

Vehicle positioning scheme based on visible light communication using a CMOS camera.

As an application of visible light communication (VLC), visible light positioning (VLP) technology has great potential for vehicle positioning due to its characteristics of no electromagnetic interference, low cost, and high positioning accuracy. In addition, the light emitting diode (LED) in street lights, traffic lights and vehicle lighting systems makes this positioning solution attractive for vehicular applications. However, the modulated LED signal will bring blooming effects on the images captured by a complementary metal oxide semiconductor (CMOS) camera. And it will decrease the positioning performance. Meanwhile, positioning errors will happen when the CMOS camera is tilted. In the paper, a vehicle positioning scheme based on VLC is proposed and experimentally demonstrated. It uses LED street light as a transmitter and the CMOS camera as a receiver. To mitigate the blooming effect in the CMOS camera based VLC, a bit length estimation (BLE) based sampling scheme is proposed to obtain the reference location information from the captured images. In addition, a novel angle compensation scheme combined with a particle filter is proposed to improve the accuracy of vehicle positioning when the CMOS camera is tilted. The experiments are performed under moving speeds of 40 to 80 cm/s and the measured distances of 80 to 115 cm. Assuming the performance of the proposed demonstrator is not modified when upscaling its size to a real scenario (such as speeds of 4 to 8 m/s and distances between the LED and camera of several meters), it can be concluded that as the speed of the moving vehicle is 8 m/s, the proposed vehicle positioning scheme based on VLC can achieve positioning accuracy of 0.128 m and 0.13 m for the tilt angles of 9° and 15.5°, respectively.

ESCOVE: Energy-SLA-Aware Edge–Cloud Computation Offloading in Vehicular Networks

The vehicular network is an emerging technology in the Intelligent Smart Transportation era. The network provides mechanisms for running different applications, such as accident prevention, publishing and consuming services, and traffic flow management. In such scenarios, edge and cloud computing come into the picture to offload computation from vehicles that have limited processing capabilities. Optimizing the energy consumption of the edge and cloud servers becomes crucial. However, existing research efforts focus on either vehicle or edge energy optimization, and do not account for vehicular applications’ quality of services. In this paper, we address this void by proposing a novel offloading algorithm, ESCOVE, which optimizes the energy of the edge–cloud computing platform. The proposed algorithm respects the Service level agreement (SLA) in terms of latency, processing and total execution times. The experimental results show that ESCOVE is a promising approach in energy savings while preserving SLAs compared to the state-of-the-art approach.

Joint Computing and Caching in 5G-Envisioned Internet of Vehicles: A Deep Reinforcement Learning-Based Traffic Control System

Recent developments of edge computing and content caching in wireless networks enable the Intelligent Transportation System (ITS) to provide high-quality services for vehicles. However, a variety of vehicular applications and time-varying network status make it challenging for ITS to allocate resources efficiently. Artificial intelligence algorithms, owning the cognitive capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), enable an intent-based networking for ITS to tackle the above-mentioned challenges. In this paper, we develop an intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly analyzing MNO's revenue and users' quality of experience, we define a profit function to calculate the MNO's profits. After that, we formulate a joint optimization problem to maximize MNO's profits, and develop an intelligent traffic control scheme by investigating DRL, which can improve system profits of the MNO and allocate network resources effectively. Experimental results based on real traffic data demonstrate our designed system is efficient and well-performed.