research-article

Incentive-Based Crowdsourcing of Hotspot Services

Authors:

Azadeh Ghari Neiat,

Athman Bouguettaya,

Sajib MistryAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 19, Issue 1

Article No.: 5, Pages 1 - 24

https://doi.org/10.1145/3229047

Published: 29 January 2019 Publication History

Abstract

We present a new spatio-temporal incentive-based approach to achieve a geographically balanced coverage of crowdsourced services. The proposed approach is based on a new spatio-temporal incentive model that considers multiple parameters including location entropy, time of day, and spatio-temporal density to encourage the participation of crowdsourced service providers. We present a greedy network flow algorithm that offers incentives to redistribute crowdsourced service providers to improve the crowdsourced coverage balance within an area. A novel participation probability model is also introduced to estimate the expected number of crowdsourced service providers’ movement based on spatio-temporal features. Experimental results validate the efficiency and effectiveness of the proposed approach.

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Cited By

Li YHe WCui LXu YGuo WFeng Z(2025)A capsule-based reinforcement learning framework for supply–demand matching in mobile crowdsourcingExpert Systems with Applications10.1016/j.eswa.2024.126203267(126203)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2024.126203
Bahutair MBouguettaya A(2023)An End-to-end Trust Management Framework for Crowdsourced IoT ServicesACM Transactions on Internet Technology10.1145/360023223:3(1-32)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1145/3600232
Xiao SChen KRen XYuan H(2022)Pedestrian Trajectory Prediction in Heterogeneous Traffic using Facial Keypoints-based Convolutional Encoder-decoder NetworkACM Transactions on Internet Technology10.1145/341044422:4(1-14)Online publication date: 14-Nov-2022
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Index Terms

Incentive-Based Crowdsourcing of Hotspot Services
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
  2. World Wide Web
    1. Web applications
      1. Crowdsourcing
        Incentive schemes

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 19, Issue 1

Regular Papers, Special Issue on Service Management for IOT and Special Issue on Knowledge-Driven BPM

February 2019

321 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3283809

Editor:
Ling Liu
Georgia Institute of Technology, USA

Issue’s Table of Contents

Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 January 2019

Accepted: 01 May 2018

Revised: 01 February 2018

Received: 01 January 2017

Published in TOIT Volume 19, Issue 1

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Qatar National Research Fund (a member of The Qatar Foundation)
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Cited By

Li YHe WCui LXu YGuo WFeng Z(2025)A capsule-based reinforcement learning framework for supply–demand matching in mobile crowdsourcingExpert Systems with Applications10.1016/j.eswa.2024.126203267(126203)Online publication date: Apr-2025
https://doi.org/10.1016/j.eswa.2024.126203
Bahutair MBouguettaya A(2023)An End-to-end Trust Management Framework for Crowdsourced IoT ServicesACM Transactions on Internet Technology10.1145/360023223:3(1-32)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1145/3600232
Xiao SChen KRen XYuan H(2022)Pedestrian Trajectory Prediction in Heterogeneous Traffic using Facial Keypoints-based Convolutional Encoder-decoder NetworkACM Transactions on Internet Technology10.1145/341044422:4(1-14)Online publication date: 14-Nov-2022
https://dl.acm.org/doi/10.1145/3410444
Neiat ABouguettaya ABahutair M(2022)A Deep Reinforcement Learning Approach for Composing Moving IoT ServicesIEEE Transactions on Services Computing10.1109/TSC.2021.306432915:5(2538-2550)Online publication date: 1-Sep-2022
https://doi.org/10.1109/TSC.2021.3064329
Li ZHe WLiu NXu YCui LQi K(2022)Balancing Supply and Demand for Mobile Crowdsourcing ServicesService-Oriented Computing10.1007/978-3-031-20984-0_20(285-299)Online publication date: 22-Nov-2022
https://doi.org/10.1007/978-3-031-20984-0_20
Bouguettaya ASheng QBenatallah BNeiat AMistry SGhose ANepal SYao L(2021)An internet of things service roadmapCommunications of the ACM10.1145/346496064:9(86-95)Online publication date: 24-Aug-2021
https://dl.acm.org/doi/10.1145/3464960
Jiang ZTan W(2021)Multi-task Allocation Strategy and Incentive Mechanism Based on Spatial-Temporal CorrelationComputer Supported Cooperative Work and Social Computing10.1007/978-981-16-2540-4_12(155-166)Online publication date: 7-May-2021
https://doi.org/10.1007/978-981-16-2540-4_12
Nguyen CSaputra YVan Huynh NNguyen NKhoa TTuan BNguyen DHoang DVu TDutkiewicz EChatzinotas SOttersten B(2020)A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open IssuesIEEE Access10.1109/ACCESS.2020.30181248(154209-154236)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3018124
Moayedikia AGhaderi HYeoh W(2020)Optimizing microtask assignment on crowdsourcing platforms using Markov chain Monte CarloDecision Support Systems10.1016/j.dss.2020.113404(113404)Online publication date: Sep-2020
https://doi.org/10.1016/j.dss.2020.113404

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