Paper Info
Title
MediaScope: selective on-demand media retrieval from mobile devices
Abstract
Motivated by an availability gap for visual media, where images and videos are uploaded from mobile devices well after they are generated, we explore the selective, timely retrieval of media content from a collection of mobile devices. We envision this capability being driven by similarity-based queries posed to a cloud search front-end, which in turn dynamically retrieves media objects from mobile devices that best match the respective queries within a given time limit. Building upon a crowd-sensing framework, we have designed and implemented a system called MediaScope that provides this capability. MediaScope is an extensible framework that supports nearest-neighbor and other geometric queries on the feature space (e.g. clusters, spanners), and contains novel retrieval algorithms that attempt to maximize the retrieval of relevant information. From experiments on a prototype, MediaScope is shown to achieve near-optimal query completeness and low to moderate overhead on mobile devices.
Year
DOI
Venue
2013
10.1145/2461381.2461416
IPSN
Keywords
Field
DocType
availability gap,extensible framework,cloud search front-end,dynamically retrieves media object,crowd-sensing framework,visual media,mobile device,selective on-demand media retrieval,timely retrieval,novel retrieval algorithm,media content,wireless sensor networks,image sensors,bandwidth,feature extraction,wireless communication,image retrieval,media,mobile devices,availability
Mobile computing,Feature vector,Mobile search,Information retrieval,Computer science,Upload,Image retrieval,Mobile database,Real-time computing,Mobile device,Multimedia,Cloud computing
Conference
Citations 
PageRank 
References 
21
0.82
21
Authors
6
Name
Order
Citations
PageRank
Yurong Jiang112611.36
Xing Xu2867.35
Peter Terlecky3626.17
Tarek Abdelzaher410179729.36
Amotz Bar-Noy52986400.08
ramesh govindan6154302144.86