Article

Using Context Annotated Mobility Profiles to Recruit Data Collectors in Participatory Sensing

Authors:

Deborah Estrin,

Mani SrivastavaAuthors Info & Claims

LoCA '09: Proceedings of the 4th International Symposium on Location and Context Awareness

Pages 52 - 69

https://doi.org/10.1007/978-3-642-01721-6_4

Published: 07 May 2009 Publication History

Abstract

Mobile phones and accompanying network layers provide a platform to capture and share location, image, and acoustic data. This substrate enables participatory sensing: coordinated data gathering by individuals and communities to explore the world around them. Realizing such widespread and participatory sensing poses difficult challenges. In this paper, we discuss one particular challenge: creating a recruitment service to enable sensing organizers to select well-suited participants. Our approach concentrates on finding participants based on geographic and temporal coverage, as determined by context-annotated mobility profiles that model transportation mode, location, and time. We outline a three-stage recruitment framework designed to be parsimonious so as to limit risk to participants by reducing the location and context information revealed to the system. Finally, we illustrate the utility of the framework, along with corresponding modeling technique for mobility information, by analyzing data from a pilot mobility study consisting of ten users.

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Fu YQi KShi YShen YXu LZhang X(2023)Dynamic Task Assignment Framework for Mobile Crowdsensing with Deep Reinforcement LearningWireless Communications & Mobile Computing10.1155/2023/70937922023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/7093792
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https://dl.acm.org/doi/10.1155/2020/8822251
Zhu WGuo WYu ZXiong H(2018)Multitask Allocation to Heterogeneous Participants in Mobile Crowd SensingWireless Communications & Mobile Computing10.1155/2018/72180612018Online publication date: 1-Jan-2018
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Information & Contributors

Information

Published In

cover image Guide Proceedings

LoCA '09: Proceedings of the 4th International Symposium on Location and Context Awareness

May 2009

282 pages

ISBN:9783642017209

Editors:
Tanzeem Choudhury
Computer Science Department, Dartmouth, USA NH 03755
,
Aaron Quigley
Complex & Adaptive Systems Laboratory, School of Computer Science & Informatics, University College Dublin, Dublin 4, Ireland
,
Thomas Strang
Institute of Communications and Navigation, German Aerospace Center, 82234 Wessling/Oberpfaffenhofen, Germany, and Digital Enterprise Research Institute (DERI), University of Innsbruck, Innsbruck, Austria 6020
,
Koji Suginuma
Semiconductor Group R&D Division, Sony Corporation, Tokyo, Japan 141-0032

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 07 May 2009

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Cited By

Fu YQi KShi YShen YXu LZhang X(2023)Dynamic Task Assignment Framework for Mobile Crowdsensing with Deep Reinforcement LearningWireless Communications & Mobile Computing10.1155/2023/70937922023Online publication date: 1-Jan-2023
Zhu WGuo WYu Z(2020)Social-Aware Task Allocation in Mobile Crowd SensingWireless Communications & Mobile Computing10.1155/2020/88222512020Online publication date: 1-Jan-2020
Zhu WGuo WYu ZXiong H(2018)Multitask Allocation to Heterogeneous Participants in Mobile Crowd SensingWireless Communications & Mobile Computing10.1155/2018/72180612018Online publication date: 1-Jan-2018
Yu ZZhou JGuo WGuo LYu Z(2018)Participant selection for t-sweep k-coverage crowd sensing tasksWorld Wide Web10.1007/s11280-017-0481-x21:3(741-758)Online publication date: 1-May-2018
Wang JWang YZhang DWang FHe YMa LLee CPoltrock SBarkhuus LBorges MKellogg W(2017)PSAllocatorProceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing10.1145/2998181.2998193(1139-1151)Online publication date: 25-Feb-2017
Lee CPoltrock SBarkhuus LBorges MKellogg W(2017)Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social ComputingundefinedOnline publication date: 25-Feb-2017
Wang JWang YHelal SZhang D(2016)A Context-Driven Worker Selection Framework for Crowd-SensingInternational Journal of Distributed Sensor Networks10.1155/2016/69587102016(12)Online publication date: 1-Mar-2016
Wang JWang YHe Y(2016)Lowering the technical threshold for organizers to create and deliver Mobile Crowd Sensing ApplicationsInternational Journal of Distributed Sensor Networks10.1155/2015/7216472015(10-10)Online publication date: 1-Jan-2016
Xiong HZhang DChen GWang LGauthier VBarnes L(2016)iCrowd: Near-Optimal Task Allocation for Piggyback CrowdsensingIEEE Transactions on Mobile Computing10.1109/TMC.2015.248350515:8(2010-2022)Online publication date: 1-Aug-2016
Zhang DXiong HWang LChen GBrush AFriday AKientz JScott JSong J(2014)CrowdRecruiterProceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/2632048.2632059(703-714)Online publication date: 13-Sep-2014
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