On-board Unit Research Articles

Formula omitted]: Privacy-preserving localized hybrid authentication scheme for large scale vehicular ad hoc networks

Existing authentication schemes for vehicular ad hoc networks (VANETs) are not scalable to high-density and safety-critical VANETs. These schemes ignore the very important and unique VANET characteristics such as frequent path disconnections due to high-mobility, bandwidth-limited channel, applications entailing ultra-low latency, high channel-error rate, etc., in their design. Specifically, their approach of entrusting the job of issuing signing key material to remote trusted authorities introduces centralized dependency and high latency for vehicles requesting the key material. Furthermore, acquiring or frequently updating the key material demands substantial network resources including high bandwidth as well as longer and frequent connections to remote authorities. In this paper, we propose a novel and efficient privacy-preserving localized hybrid authentication scheme (PLHAS) modeled on two public key cryptosystems: PKI and CL-PKC. We distribute the key management task among multiple PKI-certified local semi-trusted road-side units (RSUs) to minimize centralized dependency, and entrust identity management to a central Transport Registration Authority (TRA) to ensure role separation. We further reduce the dependency on RSUs and TRA by offloading their tasks of supplying the signing key material. Instead, vehicle's on-board unit (OBU) derives it locally from minimal long-term secret keys acquired from TRA and a local RSU. Besides, vehicles utilize a novel efficient certificate-less signature (CLS) scheme to authenticate their outgoing messages. To protect the long-term secret keys from possible side-channel leakage attacks on OBU, we fashion our critical cryptographic operations in such a manner that bounds and randomizes the leakage of secret keys. Considering the multiple semi-trusted authority setup, we also propose a modified adversarial model for CLS scheme, and show that PLHAS is provably secure, in random oracle model, against modified Type-I/II forgery attacks, certificate forgery attack and vehicle impersonation attack. The results obtained from extensive performance analysis also confirm its efficient nature as compared to the related authentication schemes.

Computation Offloading in the Internet of Connected Vehicles: A Systematic Literature Survey

Nowadays, there is a rapid development in vehicles world. Vehicles are equipped with smart systems as well as infotainment applications. But such systems consume vehicles’ computation or storage capacity. However, when the vehicle encounters a computation and/storage hungery applications or near real time applications that need high Quality of experience (QoE), it must offload it, either partially or entirely, to a more powerful and resourceful entity. At the beginnings this entity was a remote cloud. Although clouds are powerful in terms of computation and storage capacities, the process of task offloading to a remote cloud consumes the network bandwidth, which is not suitable to delay sensitive applications. As a solution, researchers propose to use cloudlets as third entity closer to the network edge. This will make the offloading much faster, but unfortunately due to the fact that cloudlets less computation and storage capacity than clouds, offloading will cause resource starvation. These factors motivate the appearance of Vehicular Cloud Computing (VCC). VCC proposes collecting the on-board units of multiple vehicles to form an on-ground cloud. This allows vehicles to offload their computational task to other vehicles in the vicinity. In this paper, we first provide a summery on concepts that are related to edge computing and task offloading process, and then we review a set of papers that use different approaches to execute computation offloading and scheduling.

Assessing Trust Level of a Driverless Car Using Deep Learning

The increasing adoption of driverless cars already providing a shift to move away from traditional transportation systems to automated ones in many industrial and commercial applications. Recent research has justified that driverless vehicles will considerably reduce traffic congestions, accidents, carbon emissions, and enhance the accessibility of driving to wider cross-section of people and lifestyle choices. However, at present, people's main concerns are about its privacy and security. Since traditional protocol layers based security mechanisms are not so effective for a distributed system, trust value-based security mechanisms, a type of pervasive security, are appearing as popular and promising techniques. A few statistical non-learning based models for measuring the trust level of a driverless are available in the current literature. These are not so effective because of not being able to capture the extremely distributed, dynamic, and complex nature of the traffic systems. To bridge this research gap, in this paper, for the first time, we propose two deep learning-based models that measure the trustworthiness of a driverless car and its major On-Board Unit (OBU) components. The second model also determines its OBU components that were breached during the driving operation. Results produced using real and simulated traffic data demonstrate that our proposed DNN based deep learning models outperform other machine learning models in assessing the trustworthiness of individual car as well as its OBU components. The average precision of detection accuracies for the car, LiDAR, camera, and radar are 0.99, 0.96, 0.81, and 0.83, respectively, which indicates the potential real-life application of our models in assessing the trust level of a driverless car.

On the Design of Efficient Hierarchic Architecture for Software Defined Vehicular Networks.

Modern vehicles are equipped with various sensors, onboard units, and devices such as Application Unit (AU) that support routing and communication. In VANETs, traffic management and Quality of Service (QoS) are the main research dimensions to be considered while designing VANETs architectures. To cope with the issues of QoS faced by the VANETs, we design an efficient SDN-based architecture where we focus on the QoS of VANETs. In this paper, QoS is achieved by a priority-based scheduling algorithm in which we prioritize traffic flow messages in the safety queue and non-safety queue. In the safety queue, the messages are prioritized based on deadline and size using the New Deadline and Size of data method (NDS) with constrained location and deadline. In contrast, the non-safety queue is prioritized based on First Come First Serve (FCFS) method. For the simulation of our proposed scheduling algorithm, we use a well-known cloud computing framework CloudSim toolkit. The simulation results of safety messages show better performance than non-safety messages in terms of execution time.

Real-Time Vehicle Trajectory Estimation Based on Lane Change Detection using Smartphone Sensors

As technology is moving rapidly toward automation and connectivity, it is of paramount importance to predict vehicle trajectories ahead of time. This not only enhances safety but also ensures mobility in a connected and automated environment. Previous studies have shown that, given the previous trajectory, the future trajectory can be estimated. But this method suffers from considerable drawbacks in the case of intersections as it cannot predict turning movements. It also requires advanced sensors that are not readily available in most vehicles. A smartphone device can also be used in such scenarios, bringing partial automation to vehicles without these sensors. This paper presents an integrated method of estimating vehicle trajectories for both general roadway segments and intersections by using a smartphone. A lane change detection system is taken as an indicator of intersection turning movement estimation and corresponding vehicle trajectories are estimated accordingly. The system can achieve high penetration rates and can be used to replicate onboard units. Sensor readings are taken periodically which are first filtered with a low-pass filter to zero out any high-frequency noise and then fed into a machine learning model to detect lane changes. The model can successfully capture lane changes with smartphone data with high accuracy (95%). Finally, vehicle trajectory is estimated using Chebyshev’s polynomial. This type of estimation system can find applications in collision prediction at intersections between a turning vehicle and a pedestrian on a crosswalk.

Highly Reliable MAC Protocol Based on Associative Acknowledgement for Vehicular Network

IEEE 1609/802.11p standard obligates each vehicle to broadcast a periodic basic safety message (BSM). The BSM message comprises a positional and kinematic information of a transmitting vehicle. It also contains emergency information that is to be delivered to all the target receivers. In broadcast communication, however, existing carrier sense multiple access (CSMA) medium access control (MAC) protocol cannot guarantee a high reliability as it suffers from two chronic problems, namely, access collision and hidden terminal interference. To resolve these problems of CSMA MAC, we propose a novel enhancement algorithm called a neighbor association-based MAC (NA-MAC) protocol. NA-MAC utilizes a time division multiple access (TDMA) to distribute channel resource into short time-intervals called slots. Each slot is further divided into three parts to conduct channel sensing, slot acquisition, and data transmission. To avoid a duplicate slot allocation among multiple vehicles, NA-MAC introduces a three-way handshake process during slot acquisition. Our simulation results revealed that NA-MAC improved packet reception ratio (PRR) by 19% and successful transmission by 30% over the reference protocols. In addition, NA-MAC reduced the packet collisions by a factor of 4. Using the real on-board units (OBUs), we conducted an experiment where our protocol outperformed in terms of PRR and average transmission interval by 82% and 49%, respectively.

Small-size Unmanned Control Algorithm of an Underwater Complex

Purpose of research: The creation of water bodies monitoring systems makes it possible to evaluate environmental situation in various points of monitoring area quickly. One of the main stages of water quality research is sampling, which is currently carried out at stationary posts, which makes it impossible to ensure operational control in various areas of an observation site. The problem can be solved by using mobile robotic platforms. The purpose of this study is to create mathematical model and algorithm for controlling autonomous movement of an underwater robotic device for collecting water samples in a reservoir. Methods. The following problems were solved for this purpose: the structure of the device was developed, which consists of a power frame unit, units of screw electric drives, units of depth and direction rudders. On-board power supply unit, sensor unit providing interaction of underwater vehicle with environment were also developed. Control tasks are formulated. A scheme of device movement in a reservoir has been developed for this purpose. Results. Modular method of trajectory planning is proposed. It is based on a single motion cycle concept, which consists of 2 turns and 2 straight-line stages of 2R2P. A model of control algorithm is also proposed and reactions of apparatus to external disturbances are studied. General dynamics theorems, method of algorithm synthesizing according to inverse dynamics problem were applied when solving problems. Conclusion: As a result of conducted studies, the system response to disturbances acting in longitudinal direction is described, disturbance diagrams are also described, random type with normal distribution law and mass center deviation s from the given position are given.

ALICIA: Applied Intelligence in blockchain based VANET: Accident Validation as a Case Study

In a connected vehicle application, the driver heavily depends on the messages, such as accident notification, collision warning, brake warning, etc., generated by the vehicle. These messages, which are generated by On-Board Units (OBU) can be used by an attacker to distract the driver or change the driving behavior to fulfill the attacker’s intentions. These generations of false messages are termed under illusion attacks. These types of attacks can be deterred by using blockchain-based architecture, which requires consensus to validate the generated messages. While the implementation of blockchain technology in connected vehicles is increasing, it is facing several vulnerabilities and threats from malicious nodes, anomalous data, and imperfect consensus mechanisms. These vulnerabilities in the system are majorly caused by malicious nodes. The generated false data can be injected at any section of the network, and not only by connected vehicles but also by endorsing Road Side Units (RSUs). These vulnerabilities can be checked to a great extent through the Miner Node Selection (MNS) process in blockchain-based systems. The MNS process selects a specific set of RSUs from all RSUs in a vehicular network (VANET) to run the consensus process. To overcome that goal, we propose ALICIA (AppLied Intelligence in bloCkchaIn vAnet) where we have used the Artificial Neural Network (ANN) to select when and which node to exclude during the consensus process. Similarly, we have also proposed our Accident detection and validation system where we detect and validate an accident, and send the data to ALICIA to perform MNS. We have used Hyperledger Fabric (HF), a blockchain platform developed as a part of the Hyperledger Project, to implement our architecture, which uses the Practical Byzantine Fault Tolerance (PBFT) method to reach consensus for it does not require high computation power to add transactions to the blockchain.

Secure and Authenticate Communication by using SoftSIM for Intelligent Transportation System in Smart Cities

Intelligent Transportation System is a field which is daily paving way for new technologies. Communication in vehicular devices has paved way for security breaches. Vehicle-to-everything connects each and every device to our vehicle just like Internet of Things connects everything to the internet. Everything which is connected to the internet, is prone to security attack. It is very difficult to track the source of the information. Authentication is a big issue and has to be solved. This paper has provided a cognitive approach to address this problem effectively in ITS. A softSIM- similar to the hardware SIM has been proposed. An architecture of the softSIM is designed. A novel algorithm for authenticating the on-board softSIM with the operator has been developed. The performance analysis of softSIM is compared with regular device users. The paper also incorporates future scope as well as business case of the study. The soft SIM will be part of on-board unit and shall come programmed from manufacturer side. This has provided cost effective solution and saved the space that was needed for the hardware SIM.

Composite fault diagnosis methodology for urban vehicular ad hoc network

Vehicular Ad-hoc NETwork (VANET) is a meteoric growing research area due to the technological advancement in sensing, computation, communication, and radio wireless technology. The demand for VANET is increasing day by day due to the numerous diverse applications. Applications of VANET include safety applications like reduction in the road accident by broadcasting messages to the driver, convenience applications like automatic parking service, commercial applications like selling and buying of products through a network of wheels, productive applications like automatic environmental parameters monitoring, vehicular cloud service like secure toll transaction, etc. The aforementioned VANET applications will become non-operational if the communication unit of the vehicle becomes inoperative or malfunctioning. This paper proposes a composite fault diagnosis methodology by detecting and classifying the fault of the On Board Unit (OBU) of the vehicular ad hoc network. The fault detection includes the detection of two categories of the fault i.e. hard fault in which the communication unit is in the inactive state and not able to communicate with other communication units and soft fault in which the communication unit is in the active state but communicates incorrect data with other communication devices. The hard fault is detected by the repeated time out mechanism and by a statistical method called Kolmogorov–Smirnov test (K-S test). The detection of soft fault (permanent, intermittent, and transient) is performed by a statistical method called the Chi-square test. Apart from the detection of the soft fault, the classification of soft fault is carried out by ECOC-SVM. Finally, the result of the fault is transmitted to the faulty vehicle. The parameters such as Fault Detection Accuracy (FDA) and False Alarm Rate (FAR) are used to validate the proposed fault detection protocol. The parameter False classification rate (FCR) is used to measure the performance of the soft fault classification. Finally, the performance of the transmission of the result to the faulty vehicle is evaluated through the parameters end-to-end (E2E) delay, number of hops, and network gaps by comparing the proposed work with existing routing protocols of VANET like GSR, A-STAR, Bhoi et al.

Novel Battery-Less Sustainable Energy Harvester Scheme for On-board Electronic Units

With the rapid development in smart vehicles, the on-board unit is established for communication between the vehicle and toll collection booth for toll collection. However, providing a continuous power supply to such units has been a challenge. While features like compactness, low cost and simplicity are to be maintained, the accuracy and performance of the device are to be maintained intact. This paper proposes a battery-less energy harvester scheme as a solution to this power issue. Maximum energy transmission Several researches are conducted on obtaining maximum energy transmission through performance optimization in this domain. This paper provides maximum performance while minimizing the energy harvester transponder dimensions. The energy transmitted to the transponder is maximized considering the optimal source impedance. This provides power to the battery-less structure. The radio frequency to DC rectifier is provided with an optimal source impedance with the help of a small patch antenna. Utilization of a lossy matching network can be avoided through this technique. Inter-stage matching network is used for comparison of the function. The energy efficiency using the proposed scheme is increased by a factor of 10% when compared to the existing schemes.

OODIDA: On-Board/Off-Board Distributed Real-Time Data Analytics for Connected Vehicles

A fleet of connected vehicles easily produces many gigabytes of data per hour, making centralized (off-board) data processing impractical. In addition, there is the issue of distributing tasks to on-board units in vehicles and processing them efficiently. Our solution to this problem is On-board/Off-board Distributed Data Analytics (OODIDA), which is a platform that tackles both task distribution to connected vehicles as well as concurrent execution of tasks on arbitrary subsets of edge clients. Its message-passing infrastructure has been implemented in Erlang/OTP, while the end points use a language-independent JSON interface. Computations can be carried out in arbitrary programming languages. The message-passing infrastructure of OODIDA is highly scalable, facilitating the execution of large numbers of concurrent tasks.

An Efficient and Provably Secure ECC-Based Conditional Privacy-Preserving Authentication for Vehicle-to-Vehicle Communication in VANETs

In vehicular ad-hoc networks (VANETs), safety messages are exchanged among vehicles and between vehicles and infrastructure to ensure passengers’ safety and efficiency in traffic. The source authentication as well as integrity checking of these messages are very necessary for a receiver. Based on certificateless cryptography (CLC), some state-of-the-art signature schemes have been proposed to address these. Although they fulfill the requirements of authentication and privacy, they are not efficient with respect to performance. Bilinear pairings and map-to-point hash functions are used in these schemes. These require a huge amount of time to process. The computational power and storing capacity of an on-board unit (OBU) in each vehicle are limited. Therefore, computational overhead is induced on vehicles that need to authenticate messages in areas of high traffic density. In this paper, a provably secure and efficient certificateless short signature-based conditional privacy-preserving authentication (CLSS-CPPA) scheme for V2V communication is designed. This scheme does not use bilinear parings and is based on the elliptic curve cryptosystem (ECC). In addition, instead of map-to-point hash functions, general hash functions are used. Furthermore, the CLSS-CPPA scheme supports the batch signature verification method which allows multiple signatures to be verified simultaneously and efficiently. The CLSS-CPPA scheme ensures security against type-I and type-II attackers with respect to existential unforgeability against adaptively chosen message attacks (EUF-CMA) under a hardness assumption of the elliptic curve discrete logarithm problem (ECDLP) in the random oracle model (ROM). The proposed scheme significantly improves performance in terms of computational and communication costs in comparison with state-of-the-art schemes.

Extremum Seeking For Economic Driving as An OnLine Mobile Application

Economic driving or energy-efficient driving plays a crucial role at reducing pollution caused by mass-production cars and fuel consumption. Operating a technical system at its most efficient set point is well known. But economic driving in traffic scenarios are more difficult to traffic flow, road grades and engine characteristics. In this study, the scalar energy-efficient variable is defined in order to seek energy efficient set point of the engine dynamics producing driving torque as a response to the travelling tasks in the road infrastructure and traffic. As an alternative solution to the well presented nonlinear controllers, a rough modelling is used to enable mobile computing on a smart-phone as an on-board unit in the car measuring the vehicle controller area network, global positioning coordinate system information and acceleration information. Overall, from the nonlinear controller design including its stability proof to the human-machine interface design to be used in road tests is elaborated. A 11% energy saving is obtained during road tests by using the presented on-line mobile and portable solution.

Design of a Compact and Highly Efficient Energy Harvester System Suitable for Battery-Less Low Cost On-Board Unit Applications

This study addresses the general problem regarding the power supply in specific on-board unit (OBUs) solutions. In detail, this paper refers to a subset of the so-called electronic toll collection (ETC) applications such as assets control and vehicle identification, where simplicity, low costs, and maximum compactness represent the most important features. In this context, the next generation of OBUs, developed specifically with reference to such applications, will involve energy harvester-based battery-less techniques. Previous studies have mainly concentrated on performance optimization by achieving maximum energy transmission to the OBUs. This study discusses a technique suitable for both maximizing performance and minimizing the dimensions of transponder energy harvesters suitable for assets control and vehicle identification operating at 5.8 GHz. The technique assumes that an optimal source impedance exists that maximizes the energy transfer to the transponder, thus enabling its power supply in a battery-less configuration. We discuss a solution based on a compact patch antenna designed to exhibit this optimal source impedance to the RF-to-DC rectifier. This approach avoids the use of a lossy matching network. For the sake of comparison, the same function is compared with an equivalent development, which includes the interstage matching network between the antenna and the RF-to-DC rectifier. We introduce experimental results demonstrating that the ultracompact energy harvester optimized at −5 dBm of impinging power is capable of increasing both the charge current and energy efficiency from 340 to 450 μA and from 37% to 47%, respectively.

A Statistical Approach Toward Channel Modeling With Application to Large-Scale Censored Data

Connected vehicle safety and efficiency applications rely on vehicular wireless networks for information sharing. To study the performance of these networks, and numerous protocols designed for them, it is essential to be able to model the channel behavior. However, due to the complexity of the vehicular environment, such modeling has to rely on data collected from large scale field tests. A majority of data collection campaigns with a large number of vehicles utilize vehicular on-board units (transceivers) to collect data in the form of received signal strength (RSS) values. Such datasets are limited in information since only the RSS values of correctly received packets are recorded, leading to datasets that can be considered as censored; especially where packet losses are high. Therefore, deriving channel models that accurately represent the channel behavior at distances with considerable loss of packets is a challenge. In this paper, we propose Bernoulli Mapping Estimation, a novel approach for the estimation of distributions from censored samples. Additionally, we provide a general method and formulation for modeling the communication channel in vehicular environments, considering receiver characteristics, imperfections, and data sparsity. The proposed methods were applied to datasets collected from highway and intersection environments; and the derived models are shown to be accurate representations of the datasets. It is also shown that even with only a small fraction of the RSS values, the framework is able to produce fading models that are similar in characteristics to field data.

Arquitectura de comunicación para la digitalización de la agricultura en torno a la maquinaria agrícola

Se presenta una arquitectura de comunicación para habilitar servicios en la nube en torno a la maquinaria agrícola en zonas rurales con infraestructura de conectividad limitada. La arquitectura propuesta se compone de tres módulos, Unidad a bordo, gateway y servicio en la nube, que extienden el alcance de la cobertura de comunicación y permiten el intercambio de datos desde la maquinaria agrícola a la nube. La arquitectura se implementa con hardware de bajo costo y librerías de código abierto que permiten una rápida implementación. Los resultados obtenidos, en términos de latencia de comunicación, indican que la solución es adecuada para aplicaciones de monitoreo en maquinarias agrícola que utilizan ISOBUS.

A Risk-Averse Adaptively Stochastic Optimization Method for Multi-Energy Ship Operation Under Diverse Uncertainties

In this paper, an optimal coordination method for energy dispatch and voyage scheduling is proposed for a renewable-energy-integrated hybrid AC/DC multi-energy ship (MES) microgrid under the continuous ship swinging. In the MES microgrid, all the onboard units are dispatched coordinately with higher flexibility for providing multiple energies. To guarantee the reliable ship operation, diverse uncertainties from solar irradiation, ship swinging angle, and onboard multi-energy demands are managed by an adaptive risk-averse stochastic programming approach to minimize the voyage cost and conditional value-at-risk. Besides, chance constraints are introduced to leverage the quality of thermal service given the thermal inertia. To speed up the solution process, the original nonlinear/nonconvex operation constraints are reformulated to a mixed-integer quadratically constrained programming form by linearization/convexification and scenario generation/reduction methods. Then the problem can be efficiently solved by commercial solvers. Finally, case studies are conducted on a test MES microgrid. The simulation results verify that the proposed method is effective in coordinating multi-energy dispatch and voyage scheduling, minimizing operating cost/risk, and immunizing against diverse uncertainties.

Proposing a Secure Self-Fining Vehicle Using Blockchain and Vehicular Edge Computing

In recent years driving violations have increased in big and crowded cities specially in developing countries. This has resulted in the escalation of traffic congestions and number of accidents, making them difficult to handle by a central managing authority. New technologies can be used in order to mitigate this problem. Hence, in this article, we propose a novel concept, called self-fining vehicle, using blockchain infrastructure on top of the vehicular edge computing. Self-fining vehicle is a vehicle that detects and records driving violations, issues tickets, and pays fines without the need for a centralized management system. Violations can be coded into on-board units and detected by vehicular communications. The conceptual architecture of secure self-fining vehicle is proposed and discussed in this article. The proposed architecture improves violation data security, processing speed, and communication delay using encryption, authentication, blockchain, and edge computing (EC), enhancing quality of service and consumers’ quality of experience. The performance analysis of the proposed architecture proves its efficiency in terms of latency in service delivery.

An Identity-Based and Revocable Data-Sharing Scheme in VANETs

Ensuring data confidentiality in a vehicular ad hoc network (VANET) is an increasingly important issue. Message confidentiality, user privacy and access control are the most important problems that affect services provided by VANETs. However, access control that addresses data downloads while preserving users' privacy remains an open problem. Based on a set of attributes, the ciphertext-policy attribute-based encryption (CP-ABE) algorithm proposes a party data encryption/decryption mechanism for shared data; consequently, the algorithm has become a popular solution for data-sharing access control. However, the current CP-ABE schemes are still infeasible for VANETs because these schemes use a single authority and inefficient encryption/decryption and ignore revocation mechanisms. Here, over CP-ABE with revocation, we introduce an identity-based scheme that achieves secure data sharing in VANETs. To reduce the computation load for in-vehicle on-board units (OBUs), we outsource computationally intensive encryption and decryption operations to cloud compute nodes. Attributes are decentralized and managed by application service providers that provide services to vehicles based on subscriptions. Comprehensive experimental results and security analysis show that our scheme achieves fine-grained access control while preserving user privacy. Through implementation, performance analysis demonstrates that our scheme is suitable for VANETs.