survey

Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm

Authors:

Xingshe ZhouAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 48, Issue 1

Article No.: 7, Pages 1 - 31

https://doi.org/10.1145/2794400

Published: 10 August 2015 Publication History

Abstract

With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and large-scale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and user-contributed data from mobile social networking services (online). Further, it explores the complementary roles and presents the fusion/collaboration of machine and human intelligence in the crowd sensing and computing processes. This article characterizes the unique features and novel application areas of MCSC and proposes a reference framework for building human-in-the-loop MCSC systems. We further clarify the complementary nature of human and machine intelligence and envision the potential of deep-fused human--machine systems. We conclude by discussing the limitations, open issues, and research opportunities of MCSC.

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Liwang MGao ZHosseinalipour SCheng ZWang XJiao Z(2025)Long-Term or Temporary? Hybrid Worker Recruitment for Mobile Crowd Sensing and ComputingIEEE Transactions on Mobile Computing10.1109/TMC.2024.347099324:2(1055-1072)Online publication date: Mar-2025
https://doi.org/10.1109/TMC.2024.3470993
Jiang CLao HZhang CCheng JZhang CLi J(2025)HeteroStamp: leveraging heterogeneous social interactions for mobility prediction-enhanced cost-aware spatiotemporal crowdsensingThe VLDB Journal10.1007/s00778-024-00891-834:2Online publication date: 22-Jan-2025
https://doi.org/10.1007/s00778-024-00891-8
Cao QTan YZhang G(2025)An Incentive Mechanism and An Offline Trajectory Publishing Algorithm Considering Sensing Area Coverage Maximization and Participant Privacy LevelSecurity and Privacy in New Computing Environments10.1007/978-3-031-73699-5_4(54-66)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-73699-5_4
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Index Terms

Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm
1. Social and professional topics
  1. Computing / technology policy

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 48, Issue 1

September 2015

592 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2808687

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

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Copyright © 2015 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 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: 10 August 2015

Accepted: 01 June 2015

Revised: 01 May 2015

Received: 01 July 2014

Published in CSUR Volume 48, Issue 1

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Program for New Century Excellent Talents in University
Scientific and Technology New Star of Shaanxi Province
National Natural Science Foundation of China
National Basic Research Program of China 973

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Cited By

Liwang MGao ZHosseinalipour SCheng ZWang XJiao Z(2025)Long-Term or Temporary? Hybrid Worker Recruitment for Mobile Crowd Sensing and ComputingIEEE Transactions on Mobile Computing10.1109/TMC.2024.347099324:2(1055-1072)Online publication date: Mar-2025
https://doi.org/10.1109/TMC.2024.3470993
Jiang CLao HZhang CCheng JZhang CLi J(2025)HeteroStamp: leveraging heterogeneous social interactions for mobility prediction-enhanced cost-aware spatiotemporal crowdsensingThe VLDB Journal10.1007/s00778-024-00891-834:2Online publication date: 22-Jan-2025
https://doi.org/10.1007/s00778-024-00891-8
Cao QTan YZhang G(2025)An Incentive Mechanism and An Offline Trajectory Publishing Algorithm Considering Sensing Area Coverage Maximization and Participant Privacy LevelSecurity and Privacy in New Computing Environments10.1007/978-3-031-73699-5_4(54-66)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-73699-5_4
Girolami MCipullo EColella TChessa S(2024)A UAV deployment strategy based on a probabilistic data coverage model for mobile CrowdSensing applicationsJournal of Ambient Intelligence and Smart Environments10.3233/AIS-22060116:2(241-268)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/AIS-220601
Liu JWang JZhao G(2024)Conscious Task Recommendation via Cognitive Reasoning Computing in Mobile Crowd SensingACM Transactions on Internet Technology10.1145/3694786Online publication date: 4-Sep-2024
https://doi.org/10.1145/3694786
Liu YYu ZZhang FCui HGuo B(2024)Wisdom of Crowds: A Human-Machine-Things Cooperative Scheduling Method for Heterogeneous Mobile CrowdsensingProceedings of the ACM on Human-Computer Interaction10.1145/36869588:CSCW2(1-25)Online publication date: 7-Nov-2024
https://dl.acm.org/doi/10.1145/3686958
Zhang HTi NWang DDu XWang QXia W(2024)Quality-Aware Incentive Mechanism for Efficient Federated Learning in Mobile CrowdsensingIEEE Transactions on Vehicular Technology10.1109/TVT.2024.344909273:12(19696-19707)Online publication date: Dec-2024
https://doi.org/10.1109/TVT.2024.3449092
Wei FChen WGuo XZhao BJeon SZhang J(2024)CrowdEC: Crowdsourcing-based Evolutionary Computation for Distributed OptimizationIEEE Transactions on Services Computing10.1109/TSC.2024.3433487(1-14)Online publication date: 2024
https://doi.org/10.1109/TSC.2024.3433487
Qi HLiwang MHosseinalipour SXia XCheng ZWang XJiao Z(2024)Matching-Based Hybrid Service Trading for Task Assignment Over Dynamic Mobile Crowdsensing NetworksIEEE Transactions on Services Computing10.1109/TSC.2023.333383217:5(2597-2612)Online publication date: Sep-2024
https://doi.org/10.1109/TSC.2023.3333832
Guo XHuang FYang DTu CYu ZGuo W(2024)Spatiotemporal Fracture Data Inference in Sparse Mobile Crowdsensing: A Graph- and Attention-Based ApproachIEEE/ACM Transactions on Networking10.1109/TNET.2023.332352232:2(1631-1644)Online publication date: Apr-2024
https://doi.org/10.1109/TNET.2023.3323522
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