Mobility Aware Vehicular Cloud Systems based on Edge computing

2013

Cloud computing technique is gaining more and more popularity recently. It can be applied to the vehicle applications to ensure real time performance as well as to improve accuracy and comfort degree for drivers. In this paper, we propose our novel vehicle cloud architecture which includes device level, communication level and service level. Each of these levels is explained in further detail with flow chart and taxonomy definition. Some innovative and real time vehicle cloud services are introduced to show the wide potential applications of vehicles and some discussion about research challenges, context classification is also provided.

—Traditionally, the vehicle has been the extension of the man's ambulatory system, docile to the driver's commands. Recent advances in communications, controls and embedded systems have changed this model, paving the way to the Intelligent Vehicle Grid. The car is now a formidable sensor platform, absorbing information from the environment (and from other cars) and feeding it to drivers and infrastructure to assist in safe navigation, pollution control and traffic management. The next step in this evolution is just around the corner: the Internet of Autonomous Vehicles. Pioneered by the Google car, the Internet of Vehicles will be a distributed transport fabric capable to make its own decisions about driving customers to their destinations. Like other important instantiations of the Internet of Things (e.g., the smart building), the Internet of Vehicles will have communications, storage, intelligence, and learning capabilities to anticipate the customers' intentions. The concept that will help transition to the Internet of Vehicles is the Vehicular Cloud, the equivalent of Internet cloud for vehicles, providing all the services required by the autonomous vehicles. In this article, we discuss the evolution from Intelligent Vehicle Grid to Autonomous, Internet-connected Vehicles, and Vehicular Cloud.

Wireless Communications and Mobile Computing

A new networking paradigm, Vehicular Edge Computing (VEC), has been introduced in recent years to the vehicular network to augment its computing capacity. The ultimate challenge to fulfill the requirements of both communication and computation is increasingly prominent, with the advent of ever-growing modern vehicular applications. With the breakthrough of VEC, service providers directly host services in close proximity to smart vehicles for reducing latency and improving quality of service (QoS). This paper illustrates the VEC architecture, coupled with the concept of the smart vehicle, its services, communication, and applications. Moreover, we categorized all the technical issues in the VEC architecture and reviewed all the relevant and latest solutions. We also shed some light and pinpoint future research challenges. This article not only enables naive readers to get a better understanding of this latest research field but also gives new directions in the field of VEC to the oth...

Journal of Network and Computer Applications, 2013

Vehicular networking has become a significant research area due to its specific features and applications such as standardization, efficient traffic management, road safety and infotainment. Vehicles are expected to carry relatively more communication systems, on board computing facilities, storage and increased sensing power. Hence, several technologies have been deployed to maintain and promote Intelligent Transportation Systems (ITS). Recently, a number of solutions were proposed to address the challenges and issues of vehicular networks. Vehicular Cloud Computing (VCC) is one of the solutions. VCC is a new hybrid technology that has a remarkable impact on traffic management and road safety by instantly using vehicular resources, such as computing, storage and internet for decision making. This paper presents the state-of-theart survey of vehicular cloud computing. Moreover, we present a taxonomy for vehicular cloud in which special attention has been devoted to the extensive applications, cloud formations, key management, inter cloud communication systems, and broad aspects of privacy and security issues. Through an extensive review of the literature, we design an architecture for VCC, itemize the properties required in vehicular cloud that support this model. We compare this mechanism with normal Cloud Computing (CC) and discuss open research issues and future directions. By reviewing and analyzing literature, we found that VCC is a technologically feasible and economically viable technological shifting paradigm for converging intelligent vehicular networks towards autonomous traffic, vehicle control and perception systems. Systems (ITS) (Al-Sultan et al. , 2013, Hartenstein and. The promise of vehicular networking has led to a fast convergence with ITS and to the advent of Intelligent Vehicular Networks , which are anticipated to transform driving styles by creating a secure, safe and healthy environment that will ultimately encompass our busy city streets and highways. Thus, the intelligent vehicular networks will provide infotainment and will enable a new versatile system that enhances transportation efficiency and safety . Although many efforts have been made to reach these objectives, VANET has several drawbacks, such as the high cost of the service constrained communications due to the high mobility of the vehicle .

IEEE Access

With the proliferation of mobile devices and a wealth of rich application services, the Internet of vehicles (IoV) has struggled to handle computationally intensive and delay-sensitive computing tasks. To substantially reduce the latency and the energy consumption, application work is offloaded from a mobile device to a remote cloud or a nearby mobile edge cloud for processing. Compared with remote clouds, mobile edge clouds are located at the edge of the network. Therefore, mobile edge computing (MEC) has the advantages of effectively utilizing idle computing and storage resources at the edge of the network and reducing the network transmission delay. In addition, mobile devices are increasingly moving toward intelligence. To satisfy the service experience and service quality requirements of mobile users, the vehicle Internet is transforming into the intelligent vehicle Internet. Artificial intelligence (AI) technology can adapt to rapidly changing dynamic environments to provide multiple task requirements for resource allocation, computational task scheduling, and vehicle trajectory prediction. On this basis, combined with MEC technology and AI technology, computing and storage resources are placed on the edge of the network to provide real-time data processing while providing more efficient and intelligent services. This article introduces IoV from three aspects, namely, MEC, AI and the advantages of combining the two, and analyzes the corresponding architecture and implementation technology. The application of MEC and AI in IoV is analyzed and compared with current approaches. Finally, several promising future directions in the field of IoV are discussed. INDEX TERMS Internet of Vehicles (IoV), mobile edge computing (MEC), artificial intelligence (AI).

Chinese Journal of Mechanical Engineering, 2021

The electrification of vehicle helps to improve its operation efficiency and safety. Due to fast development of network, sensors, as well as computing technology, it becomes realizable to have vehicles driving autonomously. To achieve autonomous driving, several steps, including environment perception, path-planning, and dynamic control, need to be done. However, vehicles equipped with on-board sensors still have limitations in acquiring necessary environmental data for optimal driving decisions. Intelligent and connected vehicles (ICV) cloud control system (CCS) has been introduced as a new concept as it is a potentially synthetic solution for high level automated driving to improve safety and optimize traffic flow in intelligent transportation. This paper systematically investigated the concept of cloud control system from cloud related applications on ICVs, and cloud control system architecture design, as well as its core technologies development. Based on the analysis, the chall...

IEEE Access, 2016

2020 IEEE 9th International Conference on Cloud Networking (CloudNet), 2020

As most vehicles spend over 95% of their time in the parking lots, the powerful computing resources of parked vehicles (PVs) are underutilized, that can be considered as available computing nodes to run tasks as well as an extension of the existing infrastructure. In this paper, we propose EdgePV, a collaborative computing paradigm to efficiently improve online heterogeneous task scheduling. To guarantee service reliability, a container orchestration (e.g. Kubernetes) is advocated to be integrated into this proposed architecture due to its notable advanced features such as load-balancing, auto-healing, resource isolation, security, etc,. Kubernetes coordinates PVs to run sufficient numbers of task replicas, providing high service availability against possible failure caused by the mobility of PVs. We investigate how efficient PVs can handle the online computational tasks during peak hours. We also present the dual cost and utility-aware heuristic algorithm, compared with a set of heuristics to solve the problem of task scheduling, that can be devised for replacing the default scheduler in Kubernetes platform. Extensive simulation results show that our proposed design improves the task acceptance ratios and average costs at least 23% and 64%, respectively, at lowest task arrival rate compared to the cooperated cloudedge architecture. Furthermore, owners of PVs can significantly benefit from incentives received by sharing the idle resources of their PVs.

2019

Autonomous Vehicles (AVs) generated a plethora of data prior to support various vehicle applications. Thus, a big storage and high computation platform is necessary, and this is possible with the presence of Cloud Computing (CC). However, the computation for vehicular networks at the cloud computing suffers from several drawbacks, such as latency and cost, due to the proximity issue. As a solution, the computing capability has been recently proposed at the edge of vehicular networks, which is known as Vehicle Edge Computing (VEC). This leads to other open problems for vehicles to offload and compute data at edge nodes, and also how data is cached in edge nodes and then disseminated to other vehicles. In this paper, we initially present an overview of VEC architectures including types of layers, fog nodes, communication technologies and also vehicle applications, which are used in data offloading and dissemination scenarios. Since the mobility is critical on the VEC performance, the ...

Effective Big Data Management and Opportunities for Implementation, 2000

The population of humanity has become more than seven billion. Daily used devices, machines, and equipment, are also increasing quicker than the human population. The number of mobile devices in use like phones, tablets and IoT devices already passed the two billion barrier and even more than one billion as vehicles are also on the roads. Combining these two will make the one of the biggest Big Data Environment about the daily life of human beings after the use of internet and social applications. For the newly manufactured vehicles, internet operated entertainment and information Systems are becoming a standard equipment delivering such an information to the manufacturers but most of the current vehicles do not have a system like that. This chapter explains the combined version of IoT and vehicles to create a V2C vehicle to cloud system that will create the big data for environmental sustainability, energy and traffic management by different technical and political views and aspects.