Scalable crowd-sourcing of video from mobile devices
Scalable crowd-sourcing of video from mobile devices
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DOI：
10.1145/2462456.2464440
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
P. Simoens;Yu Xiao;Padmanabhan Pillai;Zhuo Chen;Kiryong Ha;M. Satyanarayanan
中科院分区：
文献类型：
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作者：
P. Simoens;Yu Xiao;Padmanabhan Pillai;Zhuo Chen;Kiryong Ha;M. Satyanarayanan
我们提出了一个可扩展的互联网系统，用于从Google Glass等设备连续收集众包视频。我们的混合云架构GigaSight实际上是一个反向的内容交付网络（CDN）。它通过使用基于虚拟机~（VM）的cloudlets分散收集基础设施来实现可扩展性。根据时间、位置和内容，隐私敏感信息会自动从视频中删除。这个过程，我们称之为变性，在cloudlet上的用户特定VM中执行。用户可以对变性视频的总目录执行基于内容的搜索。我们的实验揭示了视频上传，变性，索引和基于内容的搜索的瓶颈。它们还提供了有关帧速率和分辨率等参数如何影响可扩展性的见解。
We propose a scalable Internet system for continuous collection of crowd-sourced video from devices such as Google Glass. Our hybrid cloud architecture, GigaSight, is effectively a Content Delivery Network (CDN) in reverse. It achieves scalability by decentralizing the collection infrastructure using cloudlets based on virtual machines~(VMs). Based on time, location, and content, privacy sensitive information is automatically removed from the video. This process, which we refer to as denaturing, is executed in a user-specific VM on the cloudlet. Users can perform content-based searches on the total catalog of denatured videos. Our experiments reveal the bottlenecks for video upload, denaturing, indexing, and content-based search. They also provide insight on how parameters such as frame rate and resolution impact scalability.