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Trajectory-Based Data Forwarding Schemes for Vehicular Networks 认领 引用
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作者 Jaehoon Jeong Tian He David H.C.Du 《ZTE Communications》 2014年第1期17-25,共9页
This paper explains trajectory-based data forwarding schemes for multihop data delivery in vehicular networks where the trajectory is the GPS navigation path for driving in a road network. Nowadays, GPS-based navigati... This paper explains trajectory-based data forwarding schemes for multihop data delivery in vehicular networks where the trajectory is the GPS navigation path for driving in a road network. Nowadays, GPS-based navigation is popular with drivers either for efficient driv- ing in unfamiliar road networks or for a better route, even in familiar road networks with heavy traffic. In this paper, we describe how to take advantage of vehicle trajectories in order to design data-forwarding schemes for information exchange in vehicular networks. The design of data-forwarding schemes takes into account not only the macro-scoped mobility of vehicular traffic statistics in road net- works, but also the micro-scoped mobility of individual vehicle trajectories. This paper addresses the importance of vehicle trajectory in the design of multihop vehicle-to-infrastructure, infrastructure-to-vehicle, and vehicle-to-vehicle data forwarding schemes. First, we explain the modeling of packet delivery delay and vehicle travel delay in both a road segment and an end-to-end path in a road net- work. Second, we describe a state-of-the-art data forwarding scheme using vehicular traffic statistics for the estimation of the end-to- end delivery delay as a forwarding metric. Last, we describe two data forwarding schemes based on both vehicle trajectory and vehicu- lar traffic statistics in a privacy-preserving manner. 展开更多
关键词 VANET DSRC vehicular networks data forwarding vehicle trajectory
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GPU-Driven Scalable Parser for OBJ Models 认领 引用 被引量：1
2
作者 Sunghun Jo Yuna Jeong Sungkil Lee 《Journal of Computer Science & Technology》 SCIE EI CSCD 2018年第2期417-428,共12页
This paper presents a scalable parser framework using graphics processing units （GPUs） for massive text-based files. Specifically, our solution is designed to efficiently parse Wavefront OBJ models texts of which sp... This paper presents a scalable parser framework using graphics processing units （GPUs） for massive text-based files. Specifically, our solution is designed to efficiently parse Wavefront OBJ models texts of which specify 3D geometries and their topology. Our work bases its scalability and efficiency on chunk-based processing. The entire parsing problem is subdivided into subproblems the chunk of which can be processed independently and merged seamlessly. The within-chunk processing is made highly parallel, leveraged by GPUs. Our approach thereby overcomes the bottlenecks of the existing OBJ parsers. Experiments performed to assess the performance of our system showed that our solutions significantly outperform the existing CPU-based solutions and GPU-based solutions as well. 展开更多
关键词 3D model Wavefront OBJ parser GPU
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