Vehicular Cloud Research Articles

Traffic Road Congestion System Using by the Internet of Vehicles (IoV): A Systematic Literature Review

Traffic problems have increased in modern life due to the significant number of vehicles, urbanization, and non-compliance with traffic regulations. Vehicular ad hoc networks (VANETs) have made improvements to the traffic system in the past, playing a crucial role in establishing efficient traffic control systems in large cities. However, VANETs alone cannot effectively address certain problems under specific conditions. Presently, the development of new Internet of Things (IoT) technologies has enabled collaborative and efficient task execution. This technology has been implemented in the transportation system, transforming it into an intelligent transportation system (ITS), known as the Internet of Vehicles (IoV). This study investigates traffic issues within the traditional system and explores the benefits, enhancements, and reasons for improving IoV through a comprehensive Systematic Literature Review (SLR). The SLR approach involved targeted searches using multiple search phrases, including 25 articles published between 2016 and 2023. Furthermore, discussion on the necessary IoV technologies and tools required to establish IoV and address specific traffic challenges. Simulation of Urban Mobility (SUMO) is employed for the design and simulation of road traffic and we aim to contribute to the development of an optimal traffic control system. This paper analyzes two vehicular congestion control models, selects the most optimized and efficient model, and provides evidence for its effectiveness through a Systematic Literature Review (SLR)-based investigation based on its efficient features, in the end, we propose IoV based on vehicular clouds as a superior model, surpassing the capabilities of the traditional model and enhancing the network system.

Efficient message authentication scheme with forward and backward security in Vehicle Cloud Network towards sustainable smart city

The Vehicular Cloud Network (VCN) serves as a crucial component of Intelligent Transportation Systems (ITS), playing an instrumental role in the development of sustainable smart cities. It merges the benefits of both Vehicular Ad Hoc Networks (VANET) and cloud computing. However, VCN faces significant security challenges due to the complexity of communication, the dynamic nature of the environment, and the privacy concerns of data. To address the issues of message security and privacy preservation in VCN, this paper introduces an identity-based key-insulated ring signature (ID-KIRS) scheme for VCN message authentication. Our scheme integrates a secure key update function to address key exposure issues, providing both forward and backward security. The security analysis confirms that our scheme ensures message integrity, unconditional anonymity, and unforgeability. The performance evaluation clearly demonstrates our scheme is efficient, and applicable to vehicle congestion situations, especially car accident scenarios.

Enhancing privacy-preserving in vehicular cloud through an incentive-based strategy

The literature has extensively explored vehicle ad hoc networks (VANETs) and vehicular clouds, with a common assumption in these studies being incorporating onboard units (OBUs) in vehicles. OBUs are used to collect and disseminate information between vehicles. Furthermore, numerous studies assume the presence of road infrastructure for communication. Implementing a vehicular cloud can play a vital role in aggregating data on events such as weather conditions, traffic information and accidents. This information is distributed to other vehicles, allowing drivers to make informed decisions and ensure safe driving practices. To protect privacy within the vehicular cloud, pseudonyms serve as a means of communication between vehicles and roadside units (RSU). Numerous existing approaches suggest more frequent updates to vehicle pseudonyms to reduce the likelihood of linking transmitted messages by vehicles. However, some of these strategies overlook situations where vehicle density is low, and vehicles have limited willingness to engage in the pseudonym-changing process. This article introduces an architecture that encourages vehicles to participate in the pseudonym-changing process to enhance vehicle privacy. This is achieved by issuing rewards to vehicles that can be used to access cloud services.

GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling Over Dynamic Vehicular Clouds

GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling Over Dynamic Vehicular Clouds

A dual hashing-based authentication and secure data transmission scheme for vehicular cloud environment using MECC with optimal resource allocation mechanism

A dual hashing-based authentication and secure data transmission scheme for vehicular cloud environment using MECC with optimal resource allocation mechanism

Task offloading scheme of vehicular cloud edge computing based on Digital Twin and improved A3C

In traditional vehicle edge computing research, the limited computing resources of edge servers are not fully considered. At the same time, when using deep reinforcement learning (DRL) to solve vehicle edge computing problems, it is not fully considered that DRL requires a large amount of real-time data and is prone to gradient explosion or disappearance, over-fitting and local optimal solution problems. Therefore, this article proposes a digital twin (DT)-assisted vehicular cloud edge computing offloading solution. To solve the problem of limited edge server computing resources, this article proposes a vehicle, cloud, and edge server collaboration solution. In response to the problem that DRL requires a large amount of real-time data, this paper proposes a real-time data acquisition method based on DT technology. Aiming at the gradient explosion or disappearance, overfitting, and local optimal solution problems of the asynchronous dominant actor–critic (A3C) algorithm, this paper proposes an improved algorithm of A3C, which introduces the ɛ-greedy strategy and dynamic baseline to increase the possibility of exploration and increase the generalization ability of the model through Dropout technology. During the training process, this article also uses the stochastic gradient descent (SGD) algorithm to accelerate the training process and reduce the computational complexity. The simulation results show that compared with other solutions, the improved algorithm proposed in this paper can effectively reduce the delay and energy consumption of offloading tasks at the vehicular edge cloud.

Resource Cluster-Based Resource Search and Allocation Scheme for Vehicular Clouds in Vehicular Ad Hoc Networks.

Vehicular clouds represent an appealing approach, leveraging vehicles' resources to generate value-added services. Thus, efficiently searching for and allocating resources is a challenge for the successful construction of vehicular clouds. Many recent schemes have relied on hierarchical network architectures using clusters to address this challenge. These clusters are typically constructed based on vehicle proximity, such as being on the same road or within the same region. However, this approach struggles to rapidly search for and consistently allocate resources, especially considering the diverse resource types and varying mobility of vehicles. To address these limitations, we propose the Resource Cluster-based Resource Search and Allocation (RCSA) scheme. RCSA constructs resource clusters based on resource types rather than vehicle proximity. This allows for more efficient resource searching and allocation. Within these resource clusters, RCSA supports both intra-resource cluster search for the same resource type and inter-resource cluster search for different resource types. In RCSA, vehicles with longer connection times and larger resource capacities are allocated in vehicular clouds to minimize cloud breakdowns and communication traffic. To handle the reconstruction of resource clusters due to vehicle mobility, RCSA implements mechanisms for replacing Resource Cluster Heads (RCHs) and managing Resource Cluster Members (RCMs). Simulation results validate the effectiveness of RCSA, demonstrating its superiority over existing schemes in terms of resource utilization, allocation efficiency, and overall performance.

A Sanitizable Access Control With Policy-Protection for Vehicular Social Networks

As an emerging field of communication, Vehicular Social Networks (VSNs) can reduce traffic congestion while enhancing road safety by sharing data among groups of commuters. In VSNs, Vehicular Cloud Server (VCS) based data sharing technology with encrypted primitives allows local users to outsource encrypted data for reducing the storage burden on the user side and sharing data without location restrictions. However, existing data encryption solutions that have been applied in VSNs environments still encounter weaknesses in efficiency, security, or privacy due to the following problems: (1) lack of effective access policies for flexible authorizing ciphertext to multiple data users; (2) data breaches caused by malicious data publishers; (3) necessity in hiding the private information of receivers. To date, no such solution has been available that securely enables one-to-many user authorization with privacy protection, while greatly resisting malicious data publishers. We propose a Sanitizable Access Control System with Policy-protection (SASP) for VSNs in this paper. Our SASP enables a sanitizer to test and sanitize encrypted data to defend against malicious data publishers, ensuring that the plaintext can only be recovered if an authorized user has a valid key. Furthermore, in our SASP system, the access policy is separated into attribute names and attribute values. Wherein, the attribute values contain a lot of private information, which is hidden in the ciphertext to guarantee data users’ privacy. Rigorous security analysis and performance evaluations demonstrate the practicality of SASP for VSNs.

Fuzzy logic‐based trusted routing protocol using vehicular cloud networks for smart cities

AbstractDue to the characteristics of vehicular ad hoc networks, the increased mobility of nodes and the inconsistency of wireless communication connections pose significant challenges for routing. As a result, researchers find it to be a fascinating topic to study. Furthermore, since these networks are vulnerable to various assaults, providing an authentication method between the source and destination nodes is crucial. How to route in such networks more efficiently, taking into account node mobility characteristics and accompanying massive historical data, is still a matter of discussion. Fuzzy logic‐based Trusted Routing Protocol for vehicular cloud networks (FTRP) is proposed in this study that determines the secure path for data dissemination. Fuzzy Logic determines the node candidacy value and selects or rejects a path accordingly. The cloud assigns a confidence score to each vehicle based on the data it collects from nodes after each interaction. Our study identifies the secure path on the basis of trust along with factors such as speed, closeness to other nodes, signal strength and distance from the neighbouring nodes. Simulations of the novel protocol demonstrate that it can keep the packet delivery ratio high with little overhead and low delay. FTRP has significant implications for deploying Vehicular Cloud Networks using electric vehicle technologies in smart cities. The routing data is collected with the help of Internet of Technology (IOT) sensors. The information is transmitted between vehicles using IOT gateways.

Amalgamating Vehicular Networks With Vehicular Clouds, AI, and Big Data for Next-Generation ITS Services

Advances in the connected vehicle and cloud computing technologies, Big data, and artificial intelligence techniques have opened new research opportunities. We can integrate them to work out the issues originating from transportation complexities and offer improved services. In this work, we present a seamless multi-module multi-layer vehicular cloud computing system developed using resources of parked vehicles, cloud computing facilities, and vehicular networking technologies. It can offer transportation-specific AI and Big data-empowered services to on-road vehicles. As use cases, we present two innovative and improved services, vehicular Big data mining and vehicular route optimization. A physical testbed is formed to show the feasibility of this work. Results analysis shows that the systems perform better than the standalone systems and servers under different scenarios. Relevant fundamental challenges and future outlooks are also highlighted in this work.

SLA-Based Service Provisioning Optimization in Vehicular Cloud Networks Using Fuzzy Logic

SLA-Based Service Provisioning Optimization in Vehicular Cloud Networks Using Fuzzy Logic

Unleashing the Potential of Stage-Wise Decision-Making in Scheduling of Graph-Structured Tasks over Mobile Vehicular Clouds

Unleashing the Potential of Stage-Wise Decision-Making in Scheduling of Graph-Structured Tasks over Mobile Vehicular Clouds

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

Secure Channel Establishment Scheme for Task Delivery in Vehicular Cloud Computing

Secure Channel Establishment Scheme for Task Delivery in Vehicular Cloud Computing

Smart dynamic evacuation planning and online management using vehicular communication system

AbstractDuring disasters, swiftly and efficiently evacuating populations in hazardous situations is crucial to minimize losses. This study proposes a novel framework to address dynamic population evacuation (DPE) problems, which includes planning and online evacuation management phases facilitated by vehicular communication. In the planning phase, a shelter allocation problem (SAP) is solved dynamically for destination choice and a dynamic traffic assignment (DTA) for path choice toward the chosen destinations to obtain an initial evacuation plan. The initial plan is then enhanced by employing a vehicular ad hoc network (VANET) within the vehicular edge computing (VEC) architecture. This enhancement enables communication among evacuees, allowing them to revise their vehicle's route choice and planned destination. These revisions take into account the changing risk and traffic conditions. The proposed online DPE framework is applied to the real evacuation scenario of Mill Valley City, CA. The proposed model is evaluated with different VANET architectures, including vehicular cloud computing (VCC) and VEC. The results show that the VEC framework outperforms other configurations and improves the evacuation process compared to the scenario with an initial plan by more than 30% in network clearance time. Additionally, a performance analysis is carried out for evacuation scenarios with different penetration rates of connected vehicles in VANET.

Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques

The increasing number of vehicles on the road has made traffic regulations challenging to manage, particularly in large and crowded cities. Real-time traffic monitoring systems are one of the most important factors that enable efficient traffic flow and enhanced mobility. Therefore, vehicles and drivers have always needed reliable and accurate real-time traffic information. Recently, various solutions have been proposed to solve the problems and concerns in traffic situations. One alternative solution is vehicular cloud computing (VCC). Additionally, an IoT-aided robotic (IoRT) model has been developed with a modern architecture that integrates IoT sensor nodes and cameras to gather real-time traffic data. The main contributions of this research work are to implement two deep learning techniques based on modified LeNet-5 for real-time traffic sign recognition and the transfer learning-based Inception-V3 model for detecting and recognizing traffic lights. Furthermore, optimal distance was found between the ultrasonic sensors and the obstacles using ultrasonics’ waves time and speed to reduce road accidents. The data, which is collected by sensors and cameras, is processed using various image processing algorithms and it is sent to the cloud to be available for drivers and commuters through a mobile application. Test results indicate that the proposed models have significant improvements in terms of accuracy. The modified LeNet-5 achieved accuracy rates of 99.12% and 99.78% on the German Traffic Sign Recognition Benchmark (GTSRB) and extended GTSRB (EGTSRB) datasets, respectively, whereas the second model, trained on Laboratory for the Intelligent and Safe Automobiles (LISA) dataset, attained a 98.6% accuracy rate. Compared to the related traffic monitoring systems, the findings of this study outperform other works by 3.78% for traffic sign recognition and by 1.02% for traffic light detection and recognition.

Toward a Generic Vehicular Cloud Network Architecture: A Case of Virtual Vehicle as a Service

In recent years, cloud computing has gained more and more popularity. The motivation towards implementing cloud computing in vehicular networks is due to the availability of communication, storage, and computing resources represented by communication, vehicles, roadside units (RSUs), and central servers. These resources can be utilized and provided to vehicles, drivers on the road, travellers, and customers on the internet. Intelligent Transportation System (ITS) applications can utilize vehicular cloud computing to provide efficient real-time services, as well as to improve transportation safety, mobility, and comfort levels for drivers. In this paper, all possible vehicular cloud models are presented. Each vehicular cloud model offers different services. Integrating all vehicular cloud models into one integrated system will provide all services and serve internet users, passengers, and vehicles. Therefore, a generic vehicular cloud model is proposed. After that, a new service called Virtual Vehicle is proposed in vehicular cloud computing. The virtual vehicle is a virtual machine that migrates from one physical vehicle to another. It provides the same services as the physical vehicle according to the consumer's requirements.

Mobility and Deadline-Aware Task Scheduling Mechanism for Vehicular Edge Computing

Vehicular Edge Computing (VEC) is a promising paradigm that provides cloud computing services closer to vehicular users. In VEC, vehicles and communication infrastructures can form pools with computational resources to meet vehicular services with low-latency constraints. These resource pools are known as Vehicular Cloud (VC). The usage of VC resources requires a task scheduling process. In this case, depending on its complexity, a vehicular service can be divided into different tasks. An efficient task scheduling needs to orchestrate where and for how long such tasks will run, considering the available pools, the mobility of nodes, and the tasks deadline constraints. Thus, this article proposes an efficient VC task scheduler based on an approximation heuristic and resources prediction to select the best VC for each task, called MARINA. MARINA aims to analyze the behavior of vehicles that share their computational resources with the VC and make scheduling decisions based on the mobility (VC availability) of these vehicles. Simulation results under a realistic scenario demonstrate the efficiency of MARINA compared to existing state-of-the-art mechanisms in terms of the number of tasks scheduled, monetary cost, system latency, and Central Processing Unit (CPU) utilization.

Toward Information-Centric Vehicular Cloud: Technologies, Challenges, and Opportunities

At present, road-safety problems such as traffic accidents have become increasingly serious. In the next ten years, road safety problems will lead to 13 million deaths if no effective measures are taken. Rapid retrieval of road information can prevent most traffic accidents, so it is becoming an important measure to solve road-safety problems. However, with increasing complexity and diversification of road information, an individual vehicle can no longer produce the entire traffic and road information due to limitations of visual angles and resources. In this circumstance, the concept of Vehicular Cloud (VC) is proposed to enable resource sharing and collaboration among cloud members as a solution for overcoming resource limitations. In this article, we analyze the features of VC and the advantages of the information-centric model, and propose that the information-centric model could be an ideal solution for achieving VC. Then, we discuss the open challenges encountered in enabling VC using the information-centric model, and some possible solutions.

TCaS‐VN: A novel V2V communication scheme to improve traffic control and safety in vehicular cloud networks

SummaryRoad accidents and traffic congestion are the unavoidable consequences of the growing number of vehicles on the road. Conventional management systems are not effective in mitigating these issues. However, a range of technologies, including sensors, processors, vehicle‐based resources, and mobile cloud computing, can help solve traffic and safety problems. Vehicular cloud networks, a cutting‐edge technology for the development of intelligent transportation systems, face significant challenges, such as ensuring reliable service providers that maintain stable connections between vehicles. Roadside units (RSUs) play a crucial role in these networks, but they come with several drawbacks, including high costs, imbalanced load distribution, and unreliable connections. To address these issues, we propose the Traffic Control and Safety in Vehicular Networks (TCaS‐VN) algorithm, a cloud‐based sharing services framework for vehicles that eliminates the need for RSUs. This approach offers several advantages, including a twofold improvement in the lifetime of clusters, the elimination of noncluster head nodes, a 54% reduction in overhead, a 58% reduction in service search time, and a success rate of over 95% in service provisioning.