Internet of Vehicles is a specific application of Internet of Things technology in intelligent transportation systems, and has attracted attention of relevant research institutions, automobile manufacturers and communication technology suppliers all over the world. The US government has spent hundreds of millions of dollars on DSRC development, which is a technology that can help achieve V2V and V2I communications, and enable vehicles to communicate with intelligent traffic lights to mitigate traffic congestion and accidents or bad weather on the road. Automakers such as GM and Toyota have well deployed Wi-Fi-based DSRC technology. But with the continuous development of 5G and D2D communications, cellular technology tends to be a strong candidate for V2X communications..Although 5G communication technology does not yet have a unified standard, its related concepts and trends have been recognized by industry area. Automobile manufacturers represented by Ford and BMW expect to achieve a leap in automotive networking through 5G. In the face of the 5G era, the dispute between DSRC and LTE standards has become a hot topic in the field of car networking. DSRC, a dedicated short-range communication developed and tested by automotive technology suppliers is under threat, and the next generation of 5G technology might be the final answer. Based on this point, this paper firstly introduces the development history of the Internet of Vehicles communication standard, and analyzes the advantages and disadvantages of DSRC and cellular network communication technology. Then, the three core elements of the Internet of Vehicles (Node Performance, local Network, and Internet of Things) are discussed for the development trend of vehicle networking communication technology in the context of 5G. Finally, this chapter explains the impact of the development of 5G communication technology on the future development of vehicle networking, and proposes the possible development directions of future vehicle networking technologies. INDEX TERMS Intelligent transportation systems, vehicular and wireless technologies, Internet of Things, 5G, DSRC, SDN, mobile cloud computing, mobile edge computing. NOMENCLATURE Abbreviations Full name 3G magnetic flux 4G 3rd Generation 5G fifth-generation 5G-VANET 5G enabled vehicular ad hoc network AB Automotive Brain AHS Advanced Highway System AHS Automated Highway System The associate editor coordinating the review of this manuscript and approving it for publication was Zhen Ling.
Green communications are playing critical roles in vehicular ad hoc networks (VANETs), while the deployment of a power efficient VANET is quite challenging in practice. To add more greens into such kind of complicated and time-varying mobile network, we specifically investigate the throughput and transmission delay performances for real-time and delay sensitive services through a repeated game theoretic solution. This paper has employed Nash Equilibrium in the noncooperative game model and analyzes its efficiency. Simulation results have shown an obvious improvement on power efficiency through such efforts.
With the explosive growth of web-based cameras and mobile devices, billions of photographs are uploaded to the internet. We can trivially collect a huge number of photo streams for various goals, such as 3D scene reconstruction and other big data applications. However, this is not an easy task due to the fact the retrieved photos are neither aligned nor calibrated. Furthermore, with the occlusion of unexpected foreground objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct realistic scenes. In this paper, we propose a structurebased image completion algorithm for object removal that produces visually plausible content with consistent structure and scene texture. We use an edge matching technique to infer the potential structure of the unknown region. Driven by the estimated structure, texture synthesis is performed automatically along the estimated curves. We evaluate the proposed method on different types of images: from highly structured indoor environment to the natural scenes. Our experimental results demonstrate satisfactory performance that can be potentially used for subsequent big data processing: 3D scene reconstruction and location recognition.
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