research-article

Campus-Scale Mobile Crowd-Tasking: Deployment & Behavioral Insights

Authors:

Koustuv DasguptaAuthors Info & Claims

CSCW '16: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing

Pages 800 - 812

https://doi.org/10.1145/2818048.2819995

Published: 27 February 2016 Publication History

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Abstract

Mobile crowd-tasking markets are growing at an unprecedented rate with increasing number of smartphone users. Such platforms differ from their online counterparts in that they demand physical mobility and can benefit from smartphone processors and sensors for verification purposes. Despite the importance of such mobile crowd-tasking markets, little is known about the labor supply dynamics and mobility patterns of the users.

In this paper we design, develop and experiment with a real-world mobile crowd-tasking platform, called TA$Ker. Our contributions are two-fold: (a) We develop TA$Ker, a system that allows us to empirically study the worker responses to push vs. pull strategies for task recommendation and selection. (b) We evaluate our system via experimentation with 80 real users on our campus, over a 4 week period with a corpus of over 1000 tasks. We then provide an in-depth analysis of labor supply, worker behavior & task selection preferences (including the phenomenon of super agents who complete large portions of the tasks) and the efficacy of push-based approaches that recommend tasks based on predicted movement patterns of individual workers.

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Wang ETian ZYang YLiu WLi BJiang NWu J(2023)Data-Driven Similarity-based Worker Recruitment Towards Multi-task Data Inference for Sparse Mobile Crowdsensing2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)10.1109/IWQoS57198.2023.10188803(1-10)Online publication date: 19-Jun-2023
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Information & Contributors

Information

Published In

CSCW '16: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing

February 2016

1866 pages

ISBN:9781450335928

DOI:10.1145/2818048

General Chairs:
Darren Gergle
Northwestern University
,
Meredith Ringel Morris
Microsoft Research
,
Program Chairs:
Pernille Bjørn
University of Copenhagen
,
Joseph Konstan
University of Minnesota

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 ACM 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 February 2016

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CSCW '16

Sponsor:

SIGCHI

CSCW '16: Computer Supported Cooperative Work and Social Computing

February 27 - March 2, 2016

California, San Francisco, USA

Acceptance Rates

CSCW '16 Paper Acceptance Rate 142 of 571 submissions, 25%;

Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

Upcoming Conference

CSCW '25

Sponsor:
sigchi

Computer-Supported Cooperative Work and Social Computing

October 18 - 22, 2025

Bergen , Norway

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Yang GWu XZhang YHe XWang SLiu Y(2023)Revisiting Path Planning Problem Towards Participant Executing Time Optimization in Mobile Crowd SensingIEEE Transactions on Network Science and Engineering10.1109/TNSE.2022.323136810:3(1599-1611)Online publication date: 1-May-2023
https://doi.org/10.1109/TNSE.2022.3231368
Wang ETian ZYang YLiu WLi BJiang NWu J(2023)Data-Driven Similarity-based Worker Recruitment Towards Multi-task Data Inference for Sparse Mobile Crowdsensing2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)10.1109/IWQoS57198.2023.10188803(1-10)Online publication date: 19-Jun-2023
https://doi.org/10.1109/IWQoS57198.2023.10188803
Ngo QYoon S(2023)Context-Aware Worker Recruitment for Mobile Crowd Sensing Based on Mobility PredictionIEEE Access10.1109/ACCESS.2023.330820211(92353-92364)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3308202
Pramanik PPal SChoudhury P(2023)Mobile crowd computing: potential, architecture, requirements, challenges, and applicationsThe Journal of Supercomputing10.1007/s11227-023-05545-080:2(2223-2318)Online publication date: 29-Jul-2023
https://doi.org/10.1007/s11227-023-05545-0
Chung CLin YWang YChen TChen CJiang XLin FWeng YChang Y(2022)CAMPUS: A University Crowdsourcing Platform for Reporting Facility, Status Update, and Problem Area InformationCompanion Publication of the 2022 Conference on Computer Supported Cooperative Work and Social Computing10.1145/3500868.3559447(59-62)Online publication date: 8-Nov-2022
https://dl.acm.org/doi/10.1145/3500868.3559447
Lin FLee CHo YChen YYen GChang Y(2022)What Kinds of Experiences Do You Desire? A Preliminary Study of the Desired Experiences of Contributors to Location-Based Mobile CrowdsourcingExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491101.3519744(1-7)Online publication date: 27-Apr-2022
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White RNouri EWoffinden-Luey JEncarnacióN MJauhar S(2021)Microtask DetectionACM Transactions on Information Systems10.1145/343229039:2(1-29)Online publication date: 8-Jan-2021
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Sousa PRocha A(2021)Scalability Support with Future Internet in Mobile Crowdsourcing SystemsAdvanced Information Networking and Applications10.1007/978-3-030-75100-5_53(614-625)Online publication date: 24-Apr-2021
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Wang JWang FWang YWang LQiu ZZhang DGuo BLv Q(2020)HyTasker: Hybrid Task Allocation in Mobile Crowd SensingIEEE Transactions on Mobile Computing10.1109/TMC.2019.289895019:3(598-611)Online publication date: 1-Mar-2020
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Bettini CKanhere SLangheinrich MMisra AReinhardt D(2020)Is Privacy Regulation Slowing Down Research on Pervasive Computing?Computer10.1109/MC.2020.296801353:6(44-52)Online publication date: Jun-2020
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