research-article

CoSMiC: designing a mobile crowd-sourced collaborative application to find a missing child in situ

Authors:

Hojung ChaAuthors Info & Claims

MobileHCI '14: Proceedings of the 16th international conference on Human-computer interaction with mobile devices & services

Pages 389 - 398

https://doi.org/10.1145/2628363.2628400

Published: 23 September 2014 Publication History

Abstract

Finding a missing child is an important problem concerning not only parents but also our society. It is essential and natural to use serendipitous clues from neighbors for finding a missing child. In this paper, we explore a new architecture of crowd collaboration to expedite this mission-critical process and propose a crowd-sourced cooperative mobile application, CoSMiC. It helps parents find their missing child quickly on the spot before he or she completely disappears. A key idea lies in constructing the location history of a child via crowd participation, thereby leading parents to their child easily and quickly. We implement a prototype application and conduct extensive user studies to assess the design of the application and investigate its potential for practical use.

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Cited By

Rasikannan L Suganthi J Sasikumar R Reshma V. K. (2022)Region Proposal-Based Convolutional Neural Network for Missing Child DetectionInternational Journal of Sociotechnology and Knowledge Development10.4018/IJSKD.29905014:1(1-18)Online publication date: 1-Jan-2022
https://doi.org/10.4018/IJSKD.299050
Ding YJiang DYang YLiu YHe TZhang D(2022)P2-LocProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35172386:1(1-24)Online publication date: 29-Mar-2022
https://dl.acm.org/doi/10.1145/3517238
Ray AChowdhury CBhattacharya SRoy S(2022)A survey of mobile crowdsensing and crowdsourcing strategies for smart mobile device usersCCF Transactions on Pervasive Computing and Interaction10.1007/s42486-022-00110-95:1(98-123)Online publication date: 15-Jul-2022
https://doi.org/10.1007/s42486-022-00110-9
Show More Cited By

Index Terms

CoSMiC: designing a mobile crowd-sourced collaborative application to find a missing child in situ
1. Human-centered computing
  1. Collaborative and social computing

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cover image ACM Conferences

MobileHCI '14: Proceedings of the 16th international conference on Human-computer interaction with mobile devices & services

September 2014

664 pages

ISBN:9781450330046

DOI:10.1145/2628363

Conference Chairs:
Aaron Quigley
University of St. Andrews, UK
,
Sara Diamond
OCAD University, Canada
,
Program Chairs:
Pourang Irani
University of Manitoba, Canada
,
Sriram Subramanian
University of Bristol, UK

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 September 2014

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Funding Sources

the IT R&D Program of MSIP/KEIT
National Research Foundation of Korea

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MobileHCI '14

Sponsor:

SIGCHI

MobileHCI '14: 16th International Conference on Human-Computer Interaction with Mobile Devices and Services

September 23 - 26, 2014

ON, Toronto, Canada

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MobileHCI '14 Paper Acceptance Rate 35 of 124 submissions, 28%;

Overall Acceptance Rate 202 of 906 submissions, 22%

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Cited By

Rasikannan L Suganthi J Sasikumar R Reshma V. K. (2022)Region Proposal-Based Convolutional Neural Network for Missing Child DetectionInternational Journal of Sociotechnology and Knowledge Development10.4018/IJSKD.29905014:1(1-18)Online publication date: 1-Jan-2022
https://doi.org/10.4018/IJSKD.299050
Ding YJiang DYang YLiu YHe TZhang D(2022)P2-LocProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35172386:1(1-24)Online publication date: 29-Mar-2022
https://dl.acm.org/doi/10.1145/3517238
Ray AChowdhury CBhattacharya SRoy S(2022)A survey of mobile crowdsensing and crowdsourcing strategies for smart mobile device usersCCF Transactions on Pervasive Computing and Interaction10.1007/s42486-022-00110-95:1(98-123)Online publication date: 15-Jul-2022
https://doi.org/10.1007/s42486-022-00110-9
Yu ZHan LChen CGuo WYu Z(2021)Object Tracking by the Least Spatiotemporal SearchesIEEE Internet of Things Journal10.1109/JIOT.2021.30637798:16(12934-12946)Online publication date: 15-Aug-2021
https://doi.org/10.1109/JIOT.2021.3063779
Shen HShi JZhang Y(2021)CrowdEIM: Crowdsourcing Emergency Information Management Tasks to Mobile Social Media UsersInternational Journal of Disaster Risk Reduction10.1016/j.ijdrr.2020.102024(102024)Online publication date: Jan-2021
https://doi.org/10.1016/j.ijdrr.2020.102024
Chen HGuo BYu ZHan Q(2019)CrowdTracking: Real-Time Vehicle Tracking Through Mobile CrowdsensingIEEE Internet of Things Journal10.1109/JIOT.2019.29010936:5(7570-7583)Online publication date: Oct-2019
https://doi.org/10.1109/JIOT.2019.2901093
Shen HShi JDownie SZhu QHahn J(2019)CrowdEIMProceedings of the 18th Joint Conference on Digital Libraries10.1109/JCDL.2019.00064(347-348)Online publication date: 2-Jun-2019
https://dl.acm.org/doi/10.1109/JCDL.2019.00064
Luan DYang YWang EZeng QLi ZZhou L(2019)An Efficient Target Tracking Approach Through Mobile CrowdsensingIEEE Access10.1109/ACCESS.2019.29320497(110749-110760)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2932049
Phuttharak JLoke S(2019)A Review of Mobile Crowdsourcing Architectures and Challenges: Toward Crowd-Empowered Internet-of-ThingsIEEE Access10.1109/ACCESS.2018.28853537(304-324)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2018.2885353
Wang LYu ZHan QGuo BXiong H(2018)Multi-Objective Optimization Based Allocation of Heterogeneous Spatial Crowdsourcing TasksIEEE Transactions on Mobile Computing10.1109/TMC.2017.277125917:7(1637-1650)Online publication date: 1-Jul-2018
https://doi.org/10.1109/TMC.2017.2771259
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