Cloud-based Vehicular Networks Research Articles

PAU: Privacy Assessment method with Uncertainty consideration for cloud-based vehicular networks

With the rapid progress of wireless communication and big data, the traditional Vehicular Ad-hoc Networks (VANETs) gradually evolve into the new Heterogeneous Vehicular Networks (HetVNets). Meanwhile, with the combination of multiple forms of communication modes, it initiates the Vehicle to Everything(V2X) communication model providing more efficient services. V2X communication generates much more private data than traditional VANETs, but the concerns over privacy breaches are increasing. these big data burdens the concerns about. To protect the privacy in these cloud-based vehicular networks is remained unsolved. In this paper, we propose Privacy Assessment method with Uncertainty consideration (PAU) to estimate the nodes’ capability in protecting privacy, and then choose the vehicular nodes with high priority calculated by PAU to improve the whole network’s privacy protection level. PAU expands subjective logic based on two-tuple to triad and keeps uncertainty as a constituent element. It evaluates the nodes by using the historical data from the vehicular cloud and the real-time data from V2V communications. The experiments and analysis show that the improvement of privacy-preserving capability achieved when applied PAU in Mix-zone scenarios.

Exploring Analytical Model to Performance Optimization for Mobile Application Using End-to-End Network Slicing in Cloud-Based Vehicular Networks

Today, many large usages of cloud-based vehicular networks and applications have rapidly increased. This rapid increase causes the requirement of systems to be reliable to share their resources without delay in order to ensure a better quality of service (QoS) for mobile users. Hence, network slicing is considered one of the key concepts to enhance QoS in 5G networks. At present, new architectures attempt to provide support for end-to-end server quality mechanisms. A key mechanism of network slicing supported by such modern architectures is able to either handover to better network or migrate services closer to the users as they move around. This can be done by advanced handover and server localization techniques. These sorts of advanced handover and server localization help to maintain the QoS for mobile application in heterogeneous environments. In order to obtain QoS measurements and get the network conditions in a specific area, a cloud-based vehicular network slicing management framework is proposed using an analytical modeling approach. The analytical model results obtained considering real scenarios from a Middlesex University VANET testbed. Using this framework, the mobile users will make a decision on which situation is better suited to obtain the service based on the latencies as well as queuing capacities of the networks.

Correction to: Cloud-based vehicular networks: a taxonomy, survey, and conceptual hybrid architecture

The original version of this article unfortunately contained a mistake in the title. The correct title has been published with this erratum.

Could-based vehicular networks: a taxonomy, survey, and conceptual hybrid architecture

In recent times, vehicular network research has attracted the attention of both researchers and the industry partly due to its potential applications in efficient traffic management, road safety, entertainment, etc. Resources such as communication, on-board unit, storage and computing units, and battery are generally installed in the vehicles participating in intelligent transportation systems. The need to maximize the utilization of these resources has also resulted in interest in cloud based vehicular networks (CVNs), an area of active research. This paper survey the CVNs literature published between 2010 and 2016. In addition, a taxonomy based on three main CVN categories, namely vehicular cloud computing (VCC), vehicle using cloud (VuC) and hybrid cloud (HC), is presented. In the taxonomy, we focus on related systems, architectures, applications and services. Although VCC has been extensively discussed in the literature, a comprehensive survey on the two other categories is lacking. Hence, this motivates our research. Through an extensive comparison of common characteristics among cloud computing, mobile cloud computing, VCC, VuC and HC and overview of the existing architectures, we present a conceptual HC architecture. Finally, we conclude the paper with open issues and challenges.

Mobile-Edge Computing for Vehicular Networks: A Promising Network Paradigm with Predictive Off-Loading

Cloud-based vehicular networks are a promising paradigm to improve vehicular services through distributing computation tasks between remote clouds and local vehicular terminals. To further reduce the latency and the transmission cost of the computation off-loading, we propose a cloud-based mobileedge computing (MEC) off-loading framework in vehicular networks. In this framework, we study the effectiveness of the computation transfer strategies with vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication modes. Considering the time consumption of the computation task execution and the mobility of the vehicles, we present an efficient predictive combination-mode relegation scheme, where the tasks are adaptively off-loaded to the MEC servers through direct uploading or predictive relay transmissions. Illustrative results indicate that our proposed scheme greatly reduces the cost of computation and improves task transmission efficiency.

Pseudonymous authentication for secure V2I services in cloud-based vehicular networks

Vehicular cloud computing is a technological paradigm shifting which takes advantage of cloud computing to provide vehicles with useful computing resources and services on the roads. The advancement in smart vehicles and information technologies motivate researchers and industries to pay attention to the combination of vehicular network with cloud computing in recent. In order to make the emerging vehicular cloud computing viable, security issues must be considered. Especially, privacy is one of the critical security issues in vehicular cloud services as well as vehicular communications since a third-party entity may be involved in cloud management and operations. In this paper, we propose an anonymous vehicle-to-infrastructure cloud access management system in which identity and location privacy of service requesting vehicles are prevented from not only a global eavesdropper but also any single system management entity. We devise pseudonymous service access tokens for vehicles and RSU-local revocation mechanism to reduce the size of revocation list containing revoked pseudonyms in the proposed system.

Multimedia Services in Cloud-Based Vehicular Networks

Research into the requirements for mobile services has seen a growing interest in the fields of cloud technology and vehicular applications. Integrating cloud computing and storage with vehicles is a way to increase accessibility to multimedia services, and inspire myriad potential applications and research topics. This paper presents an overview of the characteristics of cloud computing, and introduces the basic concepts of vehicular networks. An architecture for multimedia cloud computing is proposed to suit subscription service mechanisms. The tendency to equip vehicles with advanced and embedded devices such as diverse sensors increases the capabilities of vehicles to provide computation and collection of multimedia content in the form of the vehicular network. Then, the taxonomy of cloud-based vehicular networks is addressed from the standpoint of the service relationship between the cloud computing and vehicular networks. In this paper, we identify the main considerations and challenges for cloud based vehicular networks regarding multimedia services, and propose potential research directions to make multimedia services achievable. More specifically, we quantitatively evaluate the performance metrics of these researches. For example, in the proposed broadcast storm mitigation scheme for vehicular networks, the packet delivery ratio and the normalized throughput can both achieve about 90%, making the proposed scheme a useful candidate for multimedia data exchange. Moreover, in the video uplinking scenarios, the proposed scheme is favorably compared with two well-known schedulers, M-LWDF and EXP, with the performance much closer to the optimum.

Video Capture Service in the Intelligent Transportation System based on Cloud Computing

networking has significant potential to enable many different applications related to traffic safety and traffic efficiency within Intelligent Transportation System. In this paper, cloud-based vehicular network architecture is proposed such that the vehicles can share computation resources, storage resources and bandwidth resources. The proposed architecture includes four types of cloud: a vehicular cloud, a roadside cloud, a cellular cloud and a central cloud. Then, a video capture service based on the proposed architecture is presented. Vehicular cloud is used to provide a video capture service of specific location, moving vehicle or route. It sends the captured video to a customer and the other available cloud to be used for further investigation. Resource allocation and management algorithms are proposed to handle the continuity of video capture with minimum service disruption. The proposed service is implemented using a test bed.

Toward cloud-based vehicular networks with efficient resource management

In the era of the Internet of Things, all components in intelligent transportation systems will be connected to improve transport safety, relieve traffic congestion, reduce air pollution, and enhance the comfort of driving. The vision of all vehicles connected poses a significant challenge to the collection and storage of large amounts of traffic-related data. In this article, we propose to integrate cloud computing into vehicular networks such that the vehicles can share computation resources, storage resources, and bandwidth resources. The proposed architecture includes a vehicular cloud, a roadside cloud, and a central cloud. Then we study cloud resource allocation and virtual machine migration for effective resource management in this cloud-based vehicular network. A game-theoretical approach is presented to optimally allocate cloud resources. Virtual machine migration due to vehicle mobility is solved based on a resource reservation scheme.