Due to the road-constrained data delivery and highly dynamic topology of vehicle nodes in a Vehicular Ad Hoc Network (VANET), it is better to construct routing based on the road-to-road pattern than the traditional no...Due to the road-constrained data delivery and highly dynamic topology of vehicle nodes in a Vehicular Ad Hoc Network (VANET), it is better to construct routing based on the road-to-road pattern than the traditional node-to-node routing pattern in MANETs. However, the challenging issue is the opportunistic forwarding at intersections. Therefore, we propose a novel routing scheme, called Buffer and Switch (BAS). In BAS, each road buffers the data packets with multiple duplicates propagation in order to provide more opportunities for packet switching at intersections. Different from conventional protocols in VANETs, the propagation of duplicates in BAS is bidirectional along the routing path. Moreover, BAS's cost is much lower than other flooding-based protocols due to its spatio-temporally controlled duplicates propagation. Different from recent researches, BAS can deliver packets not only to a stationary node, but also to the stationary or mobile nodes in a specified area. We conduct the extensive simulations to evaluate the performance of BAS based on the road map of a real city collected from Google Earth. The simulation results show that BAS can outperform the existing protocols, especially when the network resources are limited.展开更多
HWANG Jenq-Neng received his Ph.D. degree from the University of Southern California, USA. In the summer of 1989, Dr. HWANG joined the De- partment of Electrical Engineering of the Universi- ty of Washington in Seattl...HWANG Jenq-Neng received his Ph.D. degree from the University of Southern California, USA. In the summer of 1989, Dr. HWANG joined the De- partment of Electrical Engineering of the Universi- ty of Washington in Seattle, USA, where he has been promoted to Full Professor since 1999. He served as the Associate Chair for Research fi＇om 2003 to 2005, and from 2011-2015. He is current- ly the Associate Chair for Global Affairs and Inter- national Development in the EE Depamnent. Hehas written more than 330 journal papers, conference papers and book chapters in the areas of machine learning, muhimedia signal processing, and muhimedia system integration and networking, including an au- thored textbook on ＂Multimedia Networking： from Theory to Practice,＂ published by Cambridge University Press. Dr. HWANG has close work- ing relationship with the industry on muhimedia signal processing and nmltimedia networking.展开更多
Nowadays, media cloud and machine learning have become two hot research domains. On the one hand, the increasing user de- mand on multimedia services has triggered the emergence of media cloud, which uses cloud comput...Nowadays, media cloud and machine learning have become two hot research domains. On the one hand, the increasing user de- mand on multimedia services has triggered the emergence of media cloud, which uses cloud computing to better host media servic- es. On the other hand, machine learning techniques have been successfully applied in a variety of multimedia applications as well as a list of infrastructure and platform services. In this article, we present a tutorial survey on the way of using machine learning techniques to address the emerging challenges in the infrastructure and platform layer of media cloud. Specifically, we begin with a review on the basic concepts of various machine learning techniques. Then, we examine the system architecture of media cloud, focusing on the functionalities in the infrastructure and platform layer. For each of these function and its corresponding challenge, we further illustrate the adoptable machine learning based approaches. Finally, we present an outlook on the open issues in this intersectional domain. The objective of this article is to provide a quick reference to inspire the researchers from either machine learning or media cloud area.展开更多
基金supported by the National Natural Science Foundation of China under Grants No. 60903155,No. 60903156,No.60903158,No. 61003229the Fundamental Research Funds for the Central Universities under Grants No. ZYGX2009J063, No.ZYGX2010J074
文摘Due to the road-constrained data delivery and highly dynamic topology of vehicle nodes in a Vehicular Ad Hoc Network (VANET), it is better to construct routing based on the road-to-road pattern than the traditional node-to-node routing pattern in MANETs. However, the challenging issue is the opportunistic forwarding at intersections. Therefore, we propose a novel routing scheme, called Buffer and Switch (BAS). In BAS, each road buffers the data packets with multiple duplicates propagation in order to provide more opportunities for packet switching at intersections. Different from conventional protocols in VANETs, the propagation of duplicates in BAS is bidirectional along the routing path. Moreover, BAS's cost is much lower than other flooding-based protocols due to its spatio-temporally controlled duplicates propagation. Different from recent researches, BAS can deliver packets not only to a stationary node, but also to the stationary or mobile nodes in a specified area. We conduct the extensive simulations to evaluate the performance of BAS based on the road map of a real city collected from Google Earth. The simulation results show that BAS can outperform the existing protocols, especially when the network resources are limited.
文摘HWANG Jenq-Neng received his Ph.D. degree from the University of Southern California, USA. In the summer of 1989, Dr. HWANG joined the De- partment of Electrical Engineering of the Universi- ty of Washington in Seattle, USA, where he has been promoted to Full Professor since 1999. He served as the Associate Chair for Research fi＇om 2003 to 2005, and from 2011-2015. He is current- ly the Associate Chair for Global Affairs and Inter- national Development in the EE Depamnent. Hehas written more than 330 journal papers, conference papers and book chapters in the areas of machine learning, muhimedia signal processing, and muhimedia system integration and networking, including an au- thored textbook on ＂Multimedia Networking： from Theory to Practice,＂ published by Cambridge University Press. Dr. HWANG has close work- ing relationship with the industry on muhimedia signal processing and nmltimedia networking.
文摘Nowadays, media cloud and machine learning have become two hot research domains. On the one hand, the increasing user de- mand on multimedia services has triggered the emergence of media cloud, which uses cloud computing to better host media servic- es. On the other hand, machine learning techniques have been successfully applied in a variety of multimedia applications as well as a list of infrastructure and platform services. In this article, we present a tutorial survey on the way of using machine learning techniques to address the emerging challenges in the infrastructure and platform layer of media cloud. Specifically, we begin with a review on the basic concepts of various machine learning techniques. Then, we examine the system architecture of media cloud, focusing on the functionalities in the infrastructure and platform layer. For each of these function and its corresponding challenge, we further illustrate the adoptable machine learning based approaches. Finally, we present an outlook on the open issues in this intersectional domain. The objective of this article is to provide a quick reference to inspire the researchers from either machine learning or media cloud area.
