Browsing by Author "Chen, Huihui"

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    CrowdPic: A Multi-coverage Picture Collection Framework for Mobile Crowd Photographing
    (IEEE, 2016-07-21) Chen, Huihui; Guo, Bin; Yu, Zhiwen; Chen, Liming
    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.
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    A Generic Framework for Constraint-Driven Data Selection in Mobile Crowd Photographing
    (IEEE Internet of Things Journal, 2017-01-05) Chen, Huihui; Guo, Bin; Yu, Zhiwen; Chen, Liming; Ma, Xiaojuan
    Mobile crowd photographing (MCP) is an emerging area of interest for researchers as the built-in cameras of mobile devices are becoming one of the commonly used visual logging approaches in our daily lives. In order to meet diverse MCP application requirements and constraints of sensing targets, a multifacet task model should be defined for a generic MCP data collection framework. Furthermore, MCP collects pictures in a distributed way in which a large number of contributors upload pictures whenever and wherever it is suitable. This inevitably leads to evolving picture streams. This paper investigates the multiconstraint-driven data selection problem in MCP picture aggregation and proposes a pyramid-tree (PTree) model which can efficiently select an optimal subset from the evolving picture streams based on varied coverage needs of MCP tasks. By utilizing the PTree model in a generic MCP data collection framework, which is called CrowdPic, we test and evaluate the effectiveness, efficiency, and flexibility of the proposed framework through crowdsourcing-based and simulation-based experiments. Both the theoretical analysis and simulation results indicate that the PTree-based framework can effectively select a subset with high utility coverage and low redundancy ratio from the streaming data. The overall framework is also proved flexible and applicable to a wide range of MCP task scenarios.