Internet Of Connected Vehicles Research Articles

B-SAFE: Blockchain-Enabled Security Architecture for Connected Vehicle Fog Environment.

Vehicles are no longer stand-alone mechanical entities due to the advancements in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication-centric Internet of Connected Vehicles (IoV) frameworks. However, the advancement in connected vehicles leads to another serious security threat, online vehicle hijacking, where the steering control of vehicles can be hacked online. The feasibility of traditional security solutions in IoV environments is very limited, considering the intermittent network connectivity to cloud servers and vehicle-centric computing capability constraints. In this context, this paper presents a Blockchain-enabled Security Architecture for a connected vehicular Fog networking Environment (B-SAFE). Firstly, blockchain security and vehicular fog networking are introduced as preliminaries of the framework. Secondly, a three-layer architecture of B-SAFE is presented, focusing on vehicular communication, blockchain at fog nodes, and the cloud as trust and reward management for vehicles. Thirdly, details of the blockchain implementation at fog nodes is presented, along with a flowchart and algorithm. The performance of the evaluation of the proposed framework B-SAFE attests to the benefits in terms of trust, reward points, and threshold calculation.

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

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

Three Layered Architecture for Driver Behavior Analysis and Personalized Assistance with Alert Message Dissemination in 5G Envisioned Fog-IoCV

The Internet of connected vehicles (IoCV) has made people more comfortable and safer while driving vehicles. This technology has made it possible to reduce road casualties; however, increased traffic and uncertainties in environments seem to be limitations to improving the safety of environments. In this paper, driver behavior is analyzed to provide personalized assistance and to alert surrounding vehicles in case of emergencies. The processes involved in this research are as follows. (i) Initially, the vehicles in an environment are clustered to reduce the complexity in analyzing a large number of vehicles. Multi-criterion-based hierarchical correlation clustering (MCB-HCC) is performed to dynamically cluster vehicles. Vehicular motion is detected by edge-assisted road side units (E-RSUs) by using an attention-based residual neural network (AttResNet). (ii) Driver behavior is analyzed based on the physiological parameters of drivers, vehicle on-board parameters, and environmental parameters, and driver behavior is classified into different classes by implementing a refined asynchronous advantage actor critic (RA3C) algorithm for assistance generation. (iii) If the driver’s current state is found to be an emergency state, an alert message is disseminated to the surrounding vehicles in that area and to the neighboring areas based on traffic flow by using jelly fish search optimization (JSO). If a neighboring area does not have a fog node, a virtual fog node is deployed by executing a constraint-based quantum entropy function to disseminate alert messages at ultra-low latency. (iv) Personalized assistance is provided to the driver based on behavior analysis to assist the driver by using a multi-attribute utility model, thereby preventing road accidents. The proposed driver behavior analysis and personalized assistance model are experimented on with the Network Simulator 3.26 tool, and performance was evaluated in terms of prediction error, number of alerts, number of risk maneuvers, accuracy, latency, energy consumption, false alarm rate, safety score, and alert-message dissemination efficiency.

Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning

The emerging technology of internet of connected vehicles (IoCV) introduced many new solutions for accident prevention and traffic safety by monitoring the behavior of drivers. In addition, monitoring drivers’ behavior to reduce accidents has attracted considerable attention from industry and academic researchers in recent years. However, there are still many issues that have not been addressed due to the lack of feature extraction. To this end, in this paper, we propose the multimodal driver analysis internet of connected vehicles (MODAL-IoCV) approach for analyzing drivers’ behavior using a deep learning method. This approach includes three consecutive phases. In the first phase, the hidden Markov model (HMM) is proposed to predict vehicle motion and lane changes. In the second phase, SqueezeNet is proposed to perform feature extraction from these classes. Lastly, in the final phase, tri-agent-based soft actor critic (TA-SAC) is proposed for recommendation and route planning, in which each driver is precisely handled by an edge node for personalized assistance. Finally, detailed experimental results prove that our proposed MODAL-IoCV method can achieve high performance in terms of latency, accuracy, false alarm rate, and motion prediction error compared to existing works.

UAV Enabled Content Distribution for Internet of Connected Vehicles in 5G Heterogeneous Networks

The increasing development of Internet of Things (IoT) has led to the emergence of Internet of connected vehicles (IoCVs). These vehicles with various functionalities have the potential prospects for improving the quality of experience (QoE) of vehicle users. Moreover, the use of unmanned aerial vehicles (UAVs) in flying networks extends the connectivity and universality of IoT, and these UAVs with caching and communication capacities can support various services. However, due to the heterogeneity of vehicular networks and flying networks, the communication performance and content distribution between UAVs and IoCVs expose new challenges in heterogeneous networks (HetNets). Therefore, in this paper, a novel content distribution mechanism between UAVs and IoCVs is proposed to improve the QoE of vehicle users. Specifically, we first develop a novel content distribution architecture for UAVs and IoCVs in HetNets, where the content is distributed by UAV content providers to IoCVs. Next, we establish an optimization problem of content distribution between UAVs and IoCVs to minimize the transmission delay. In order to stimulate UAVs and IoCVs to join content distribution, the utilities of UAVs and IoCVs are formulated, respectively. Moreover, we design a coalition game between UAVs and IoCVs to determine the optimal strategy of content distribution. Finally, simulation results demonstrate that the proposed mechanism can significantly improve the performance of content distribution compared with the conventional mechanisms.

Adaptive Computation Offloading With Edge for 5G-Envisioned Internet of Connected Vehicles

Nowadays, the applications related to Internet of connected vehicles (IoCV) have been greatly promoted by the roadside units (RSUs). To improve the transmission efficiency by the RSUs, 5G is introduced to the IoCV scenario for offering sufficient communication bandwidth. Generally, the traditional offloading destinations of the computing tasks in IoCV are the distant cloud servers, which consequently increases the response time of the tasks. Edge servers, placed together with macro base stations (MABSs) in 5G and RSUs, offer alternatives to host the tasks. However, the complicated locations of MABSs and RSUs make it difficult to distinguish the offloading destinations of the computing tasks in IoCV. In view of this, an adaptive computation offloading method, named ACOM, is devised for edge computing in 5G-envisioned IoCV to optimize the task offloading delay and resource utilization of the edge system. More specifically, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is fully leveraged to generate the available solutions. Then, the optimal offloading solution is obtained by utility evaluation. Eventually, the experimental results demonstrate the effectiveness of ACOM.

Guest Editorial Special Issue on Intent-Based Networking for 5G-Envisioned Internet of Connected Vehicles

With the recent advances in wireless communications, the automotive industry is leading to evolution. To succeed in this emerging era of technology, the Internet of Connected Vehicles (IoCV) has emerged as one of the potential applications of the Internet of Things (IoT). It refers to the dynamic mobile communication systems that communicate between vehicles and public networks to enhance the connectivity between cars via technology. By offering a wide variety of infotainment services, fleet operations, and in-vehicle applications, IoCV has gained the tremendous capacity to provide a safer and sustainable transportation system to the society. According to Gartner Inc., “the connected car is already a reality, and in-vehicle wireless connectivity is expanding rapidly.” As a result, the evolution of cars into the IoT will keep on accelerating the global market which is expected to grow by 270% by 2022. Furthermore, the increasing deployment of sensors and ever-evolving cognitive technology opens up new opportunities for IoCV. Due to these significant developments, connected vehicles are receiving widespread attention from the major automotive giants such as Tesla, BMW, Waymo (Google), Uber, Volvo, and so on. Despite all the opportunities offered by the IoCV, their highly dynamic topology and the increasing number of vehicles pose challenges regarding delivering low-latency vehicle-to-everything (V2X) communications.

Security Enhancement for Real-Time Parallel In-Vehicle Applications by CAN FD Message Authentication

Controller Area Network with Flexible Data-rate (CAN FD) is beneficial for the in-vehicle communication of Internet of Connected Vehicles (IoCVs) because of its high bandwidth and data field length. However, CAN FD lacks a security authentication mechanism, making it extremely vulnerable to masquerade attacks. This study proposes the security enhancement for a real-time parallel in-vehicle application adopting a two-stage method. The first stage obtains the lower bound of an in-vehicle application by quickly abandoning most of sequences, while the second stage enhances security by adding Message Authentication Codes (MACs) to messages taking advantage of the laxity interval from the lower bound to the deadline. Experiments with an example and the adaptive cruise control in-vehicle application show the advantage of the proposed two-stage method in increasing the total byte size of MACs.

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.

An Enhanced Multi-Hop Intersection-Based Geographical Routing Protocol for the Internet of Connected Vehicles Network

Internet of connected vehicles (IoCV) is one of the popular subclasses of vehicle ad hoc networks and an up-rising form based on new Internet, 5G, cloud, and edge computing features. The vehicle nodes in such networks can exchange the information with other nodes with the help of infrastructure or without prior infrastructure. For efficient data communication among the vehicle nodes, the routing protocols play a significant role and able to handle the different network characteristics including high mobility, dynamic topologies, and disconnected links. To address the existing routing protocol issues including data delay, disconnection, interference, scalability, and overhead, this paper presents the Intersection Gateway and Connectivity based Routing (IGCR) protocol for IoCV networks. The proposed protocol uses important traffic-aware routing metrics, including traffic density and direction of nodes towards the destination for route and next forwarder node selection. The experimental results indicated that the proposed protocol well-behaved and showing better performance compared to the state of the art routing protocols in terms of data delivery, data delay, and data throughput.

Enabling Intelligent IoCV Services at the Edge for 5G Networks and Beyond

The Fifth Generation (5G) communication technology has paved the way for intelligent and diversified Internet of Connected Vehicles (IoCV) services that meet stringent Quality of Service (QoS) requirements. Both Artificial Intelligence (AI) and Blockchain are playing and will continue to play an imperative role in providing secure and decentralized resource sharing to solve complex and time-sensitive problems at the edge. The integration of both those techniques will enhance the performance of smart vehicular services, especially in beyond 5G (B5G) networks. Ensuring secure transactions in complex autonomous network architectures is an immense challenge. This article addresses computational, storage, connectivity and intelligence concerns using a collaborative approach to engage multiple Internet of Things (IoT) nodes such as connected- vehicles, drones and mobile devices for the provisioning of QoS-optimal complex service compositions in autonomous mobile networks. Continuous and fast compositions emerge using decentralized decisions and interactions with diversified neighboring nodes with the aid of reinforcement learning. Blockchain is used to ensure that nodes interact with each other verifiably and record transactions without the need for trusted intermediaries. We assess whether having an AI-enabled blockchain collaborative composition solution improves service availability and delivery of smart city vehicular services.

Uncertain-Driven Analytics of Sequence Data in IoCV Environments

As the increasing availability and use of dynamic mobile communications, information from an Internet of Things (IoT) subset of devices, known as Internet of Connected Vehicles (IoCV), is collected with a level of uncertainty. To bridge this gap of data analytics, some studies take two factors individually to mine knowledge or information, such as uncertainty and utility as two exemplary factors. However, this approach may cause actual loss of knowledge integrity. In this work, our first result is a knowledge called High Expected Utility Sequential Patterns (HEUSPs) that is both novel and also provides an alternative option for knowledge discovery regarding utility and uncertainty factors by a single threshold in IoCV environments. Furthermore, two PUL-Chain and EUL-Chain structures with six pruning methodologies are respectively developed to maintain information that is necessary and reduce the search space for improving mining performance. Our experimental results show both efficiency and strength of the designed algorithm compared to HUS-Span which is considered to be the current standard in utility-oriented sequential pattern mining.

An Intelligent Terminal Based Privacy-Preserving Multi-Modal Implicit Authentication Protocol for Internet of Connected Vehicles

The Internet of connected Vehicles (IOV) can collect, process, compute and release the information of intelligent transportation systems. IOV is an integrated service system that can support the applications for automatic driving, intelligent transport and information services. As the number of incidents on IOV has been on the rise in the past few years, IOV security is becoming increasingly important in the IOV architecture. One of the most notable risks of IOV faces is intelligent terminal security. The vehicle's intelligent terminal can be used to launch for further attacks on the on-board operating system to penetrate into the internal network of connected vehicle, and consequently threaten the safety of the vehicle. Thus, it is of paramount importance that we protect the security of the intelligent terminal. We propose two intelligent terminal based privacy-preserving multi-modal implicit authentication protocols to protect the security of the intelligent terminal in IOV. The proposed protocols use the password and the vehicle owner's behavior features as the authentication factors to protect the security of the intelligent terminal. Since the vehicle owner's behavior features are sensitive and the privacy information of the user must be protected, we also consider the privacy protection of the behavior features. Our protocols do not reveal any information about the vehicle owner's behavior features to the authentication server and the adversary except the ciphertext size of the feature vector. We analyze the security of our proposed protocol and compare them with other related protocols in terms of computation and communications costs. Our results demonstrate that our proposed protocols yield better security and efficiency.

Clustering-Learning-Based Long-Term Predictive Localization in 5G-Envisioned Internet of Connected Vehicles

Localization services play an important role in Internet of Connected Vehicles (IoCV) and vehicle predictive localization information can greatly improve traffic efficiency and reduce accidents. However, a huge amount of computing and communication overhead is required to obtain such information by traditional methods. In this work, we propose a Behavior-based Clustering Method (BCM) to analyze the behavioral correlation between vehicles and classify them into different clusters. Based on BCM results coupled with a deep learning model, we further propose a Clustering-learning-based Long-term Predictive Localization (CLPL) algorithm to predict vehicles' future location distribution. In the proposed CLPL algorithm, all the traffic roads are divided into consecutive small segments in order to pinpoint vehicles' precise current locations and to obtain long-term predictions. Extensive simulations, notably involving real dataset, have been carried out to evaluate BCM and CLPL in terms of several performance criteria including matching rates. The analysis of the results validated how the designed methods can predict vehicle location much more accurately than existing algorithms.

Optimization of Classified Municipal Waste Collection Based on the Internet of Connected Vehicles

The development of 5G technology has brought the information revolution of the Internet of Things (IoT). With the emergence of intelligent products, such as the Internet of connected vehicles (IoCV) and wireless sensor nodes, the classification and disposal of municipal waste is now more intelligent and efficient. How to arrange waste collection vehicles reasonably and optimize their service routes in real time based on signals sent by intelligent waste bins to meet the daily needs of residents has become an urgent problem. An improved multiobjective model for the split-delivery vehicle routing problem (SDVRP) is proposed based on the multiple cleaning services for the same collection point in a certain period of time according to the real-time waste volume. The process of classified waste disposal in the Tianjin Wudadao area is analyzed as an example. The experimental results show that the Pareto-optimal solutions obtained by the algorithm can effectively arrange the vehicle service routes, meet the needs of classified waste collection in the area, and provide a scientific theoretical basis for the further development of waste classification.

LocJury: An IBN-Based Location Privacy Preserving Scheme for IoCV

Stemming from the recent evolutionary progress of the 5G wireless communication and Internet of Things (IoT) relevant technologies, the vision of the Internet of Connected Vehicles (IoCV) has become more apparent. On the basis of the state-of-the-art IoCV conceptual implementations, the location of the vehicles is one of the essential driven data of IoCV, and the location privacy issue needs to be taken into account. However, when scrutinizing into IoCV, noticeable challenges of location-aware scenario has raised. For IoCV, location is more than just query criteria like in Location-based Services (LBSs) of mobile Internet. It is also the underpinning data of various types of IoCV underlying mechanisms and functions. This difference makes preserving location privacy in IoCV quite different from the traditional privacy scenarios. In this paper, an overall analysis of end-user location privacy in IoCV was performed. To solve the location privacy dilemma, we proposed an intent prediction-based approach named LocJury, which benefits from the emerging concept of Intent-based Networking (IBN). LocJury provides location privacy by learning and estimate the intent of location access and will penalize those malicious location accesses. By simulating the conceptual IBN-based IoCV application scenario, which relies on the location accesses, the performance of LocJury is evaluated under various circumstances. The simulation result verified the effectiveness of our proposed method.

A survey of local differential privacy for securing internet of vehicles

Internet of connected vehicles (IoV) are expected to enable intelligent traffic management, intelligent dynamic information services, intelligent vehicle control, etc. However, vehicles’ data privacy is argued to be a major barrier toward the application and development of IoV, thus causing a wide range of attentions. Local differential privacy (LDP) is the relaxed version of the privacy standard, differential privacy, and it can protect users’ data privacy against the untrusted third party in the worst adversarial setting. Therefore, LDP is potential to protect vehicles’ data privacy in the practical scenario, IoV, although vehicles exhibit unique features, e.g., high mobility, short connection times, etc. To this end, in this paper, we first give an overview of the existing LDP techniques and present the thorough comparisons of these work in terms of advantages, disadvantages, and computation cost, in order to get the readers well acquainted with LDP. Thereafter, we investigate the potential applications of LDP in securing IoV in detail. Last, we direct several future research directions of LDP in IoV, to bridge the gaps between LDP researches and the privacy preservation in IoV. The originality of this survey is that it is the first work to summarize and compare the existing LDP research work and that it also does an pioneering work toward the in-depth analysis of the potential applications of LDP in privacy preservation in IoV.

Sampled-data internet of connected vehicles control with channel fading and time-varying delay

Abstract This paper is concerned with sampled-data internet of connected vehicles (IOCV) control systems with a communication channel fading and time-varying delay. A hybrid IOCV system model is established, where the communication channel fading and time-varying delay are given full considerations. Based on the model, by employing Lyapunov-Krosovskii functional, a new sampled-data controller method with variable sampling period is derived, which can robustly stabilize the IOCV system with a given level of disturbance attenuation. Furthermore, the obtained sampled-data controller is complemented by additional conditions established to guaranteeing string stability and zero steady state tracking errors. Finally, the effectiveness and superiority of the proposed control strategies are verified by both numerical simulation and experiment with four-laboratory scale cars.

Guest Editorial Special Issue on Toward Securing Internet of Connected Vehicles (IoV) From Virtual Vehicle Hijacking

Today’s vehicles are no longer stand-alone transportation means, due to the advancements on vehicle-tovehicle (V2V) and vehicle-to-infrastructure (V2I) communications enabled to access the Internet via recent technologies in mobile communications, including WiFi, Bluetooth, 4G, and even 5G networks. The Internet of vehicles was aimed toward sustainable developments in transportation by enhancing safety and efficiency. The sensor-enabled intelligent automation of vehicles’ mechanical operations enhances safety in on-road traveling, and cooperative traffic information sharing in vehicular networks improves traveling efficiency.

An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles

The Internet of connected vehicles (IoV) is employed to collect real-time traffic conditions for transportation control systems, and the computing tasks are available to be offloaded from the vehicles to the edge computing devices (ECDs) for implementation. Despite numerous benefits of IoV and ECDs, the wireless communication for computation offloading increases the risk of privacy leakage, which may consequently lead to tracking, identity tampering and virtual vehicle hijacking. Therefore, it remains a challenge to avoid privacy conflicts for computation offloading to the ECDs in IoV. To address this challenge, an edge computing-enabled computation offloading method, named ECO, with privacy preservation for IoV is proposed in this paper. Technically, the privacy conflicts of the computing tasks in IoV are analyzed in a formalized way. Then, vehicle-to-vehicle (V2V) communication-based routing for a vehicle is designed to acquire the routing vehicles from the origin vehicle where the computing task is located at the destination vehicle. NSGA-II (non-dominated sorting genetic algorithm II) is adopted to realize multi-objective optimization to reduce the execution time and energy consumption of ECDs and prevent privacy conflicts of the computing tasks. Finally, experimental evaluations are conducted to validate the efficiency and effectiveness of ECO.