Photo crowdsourcing for area coverage in resource constrained environments

Yibo Wu, Yi Wang, Guohong Cao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

41 Scopus citations


Photos crowdsourced from mobile devices can be used in many applications such as disaster recovery to obtain information about a target area. However, such applications often have resource constraints in terms of bandwidth, storage, and processing capability, which limit the number of photos that can be crowdsourced. Thus, it is a challenge to use the limited resources to crowdsource photos that best cover the target area. In this paper, we leverage various geographical and geometrical information about photos, called metadata, to address this challenge. Metadata includes the location, orientation, field of view, and range of a camera. Based on metadata, we define photo utility to measure how well a target area is covered by a set of photos. We propose various techniques to analyze such coverage and calculate photo utility accurately and efficiently. We also study the problem of selecting photos with the largest utility under a resource budget, and propose an efficient algorithm that achieves constant approximation ratio. With our design, the crowdsourcing server can select photos based on metadata instead of real images, and thus use the limited resources to crowdsource the most useful photos. Both simulation and experimental results demonstrate the effectiveness of our design.

Original languageEnglish (US)
Title of host publicationINFOCOM 2017 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509053360
StatePublished - Oct 2 2017
Event2017 IEEE Conference on Computer Communications, INFOCOM 2017 - Atlanta, United States
Duration: May 1 2017May 4 2017

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Other2017 IEEE Conference on Computer Communications, INFOCOM 2017
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering


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