ehicular networks are in the progress of merging with the Internet to constitute a fundamental information platform, which is an indispensable part of an intelligent transport system (ITS) [1] . This will eventually evolve into all vehicles connected in the era of the Internet of Things (IoT) [2] . By supporting traffic-related data gathering and processing, vehicular networks are able to notably improve transport safety, relieve traffic congestion, reduce air pollution, and enhance driving comfortability [3] . It has been reported that, in Western Europe, deaths due to car accidents could be reduced 25 percent by deploying safety warning systems at highway intersections [4] . Another example is that real-time traffic information could be collected and transmitted to data centers for processing, and in return, information could be broadcast to drivers for route planning. City traffic congestion would be alleviated and travel time reduced, leading to greener cities. A variety of information technologies have been developed for intelligent vehicles, roads, and traffic infrastructures such that all vehicles are connected. Smart sensors and actuators are deployed in vehicles and roadside infrastructures for data acquisition and decisions. Advanced communication technologies are used to interconnect vehicles and roadside infrastructures, and eventually access to the Internet. For instance, dedicated short-range communications (DSRC) is specifically designed for vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) communications. IEEE 802.11p, called Wireless Access in Vehicular Environments (WAVE) [5] , is currently a popular standard for DSRC. Besides, the Long Term Evolution (LTE), LTE-Advanced, and cognitive radio (CR) [6, 7] are all fairly competitive technologies for vehicular networking [8, 9] . Despite the well developed information technologies, there is a significant challenge that hinders the rapid development of vehicular networks. Vehicles are normally constrained by resources, including computation, storage, and radio spectrum bandwidth. Due to the requirements of small-size and lowcost hardware systems, a single vehicle has limited computation and storage resources, which may result in low data processing capability. On the other hand, many emerging applications demand complex computation and large storage, including in-vehicle multimedia entertainment, vehicular social networking, and location-based services. It is becoming increasingly difficult for an individual vehicle to efficiently support these applications. A very promising solution is to share the computation and storage resources among all vehicles or physically nearby vehicles. This motivates us to study the new paradigm of cloud-based vehicular networks. Recently, a few research projects have been reported that study the combination of cloud computing and vehicular networks. In [10] , the concept of autonomous vehicular clouds (AVCs) is proposed to exploit the underutilized resources in vehicular ad hoc networks (VANETs). A platform as a service (PaaS) model is designed in [11] to support cloud services for mobile vehicles. The work in [12] proposes architectures of vehicular clouds (VCs), vehicles using clouds (VuCs), and hybrid clouds (HCs). Vehicles act as cloud service providers and clients in VCs and VuCs, respectively, and as both in HCs. In this article, we propose a hierarchical cloud architecture for vehicular networks. Our work is different from previous research in three main aspects. First, we aim to create a pervasive cloud environment for mobile vehicles by integrating redundant physical resources in ITS infrastructures, including data centers, roadside units, and vehicles. The aggregation of these sporadic physical resources potentially compose massive and powerful cloud resources for vehicles. Second, we propose a three-layered architecture to organize the cloud resources. The layered structure allows vehicles to select their V 48 IEEE Network • 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.
Because it is challenging for the connected vehicles to collect and store large amount of traffic data, cloud computing is combined with IoVs in REF , by which vehicles are able to share the resources of computation, storage and bandwidth.
Toward cloud-based vehicular networks with efficient resource management
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Computer Science