Scalable Crowd-Sourcing of Video from Mobile Devices
Scalable Crowd-Sourcing of Video from Mobile Devices Pieter Simoens†, Yu Xiao‡, Padmanabhan Pillai•, Zhuo Chen, Kiryong Ha, Mahadev Satyanarayanan December 2012 CMU-CS-12-147 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 †Carnegie Mellon University and Ghent University ‡Carnegie Mellon University and Aalto University •Intel Labs Abstract We propose a scalable Internet system for continuous collection of crowd-sourced video from devices such as Google Glass. Our hybrid cloud architecture for this system is effectively a CDN in reverse. It achieves scalability by decentralizing the cloud computing infrastructure using VM-based cloudlets. 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 Virtual Machine (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 and provide valuable insight on how parameters such as frame rate and resolution impact the system scalability. Copyright 2012 Carnegie Mellon University This research was supported by the National Science Foundation (NSF) under grant numbers CNS-0833882 and IIS-1065336, by an Intel Science and Technology Center grant, by the Department of Defense (DoD) under Contract No. FA8721-05-C-0003 for the operation of the Software Engineering Institute (SEI), a federally funded research and development center, and by the Academy of Finland under grant number 253860. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily represent the views of the NSF, Intel, DoD, SEI, Academy of Finland, University of Ghent, Aalto University, or Carnegie Mellon University.
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