CrowdPic: A Multi-coverage Picture Collection Framework for Mobile Crowd Photographing

Date

2016-07-21

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

Abstract

This paper proposes a generic task-driven data collection framework, named as Crowd Pic, for Mobile Crowd Photographing (MCP) - a widely used technique in crowd sensing. In order to meet diverse MCP application requirements (e.g. Spatio-temporal contexts, single or multiple shooting angles to a sensing target), a multifaceted task model with collection constraints is provided in Crowd Pic. Meanwhile, a pre-selection process is necessary to prevent mobile clients from uploading redundant pictures so as to reduce the overhead traffic and maintain the sensing quality. To address this issue, we developed a pyramid-tree (PTree) model which can select maximum diversified subset from the evolving picture streams based on multiple coverage requirements and constraints defined in MCP tasks by data requesters. Crowd sourcing-based and simulation-based methods are both used to evaluate the effectiveness, efficiency and flexibility of the proposed framework. The experimental results indicate that the PTree method can efficiently assess redundant pictures and effectively select minimal subset with high coverage from the streaming picture according to various coverage needs, and the whole framework is applicable to a wide range of use scenarios.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

mobile crowd sensing, picture collection, pyramid tree

Citation

Chen, H. et al. (2016) CrowdPic: A Multi-coverage Picture Collection Framework for Mobile Crowd Photographing. Proceeding of UIC-ATC-ScalCom-CBDCom-IoP 2015, pp.68-76

Rights

Research Institute

Cyber Technology Institute (CTI)