A Photo-Based Mobile Crowdsourcing Framework for Event Reporting

Aymen Hamrouni, Hakim Ghazzai, Mounir Frikha, Yehia Massoud

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

9 Scopus citations

Abstract

Mobile Crowdsourcing (MCS) photo-based is an arising field of interest and a trending topic in the domain of ubiquitous computing. It has recently drawn substantial attention of the smart cities and urban computing communities. In fact, the built-in cameras of mobile devices are becoming the most common way for visual logging techniques in our daily lives. MCS photo-based frameworks collect photos in a distributed way in which a large number of contributors upload photos whenever and wherever it is suitable. This inevitably leads to evolving picture streams which possibly contain misleading and redundant information that affects the task result. In order to overcome these issues, we develop, in this paper, a solution for selecting highly relevant data from an evolving picture stream and ensuring correct submission. The proposed photo-based MCS framework for event reporting incorporates (i) a deep learning model to eliminate false submissions and ensure photos credibility and (ii) an A-Tree shape data structure model for clustering streaming pictures to reduce information redundancy and provide maximum event coverage. Simulation results indicate that the implemented framework can effectively reduce false submissions and select a subset with high utility coverage with low redundancy ratio from the streaming data.
Original languageEnglish (US)
Title of host publicationMidwest Symposium on Circuits and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages198-202
Number of pages5
ISBN (Print)9781728127880
DOIs
StatePublished - Aug 1 2019
Externally publishedYes

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