research-article

A Survey of Mobile Crowdsensing Techniques: A Critical Component for The Internet of Things

Authors:

Husnu S. Narman,

Zongfang LinAuthors Info & Claims

ACM Transactions on Cyber-Physical Systems, Volume 2, Issue 3

Article No.: 18, Pages 1 - 26

https://doi.org/10.1145/3185504

Published: 13 June 2018 Publication History

Abstract

Mobile crowdsensing serves as a critical building block for emerging Internet of Things (IoT) applications. However, the sensing devices continuously generate a large amount of data, which consumes much resources (e.g., bandwidth, energy, and storage) and may sacrifice the Quality-of-Service (QoS) of applications. Prior work has demonstrated that there is significant redundancy in the content of the sensed data. By judiciously reducing redundant data, data size and load can be significantly reduced, thereby reducing resource cost and facilitating the timely delivery of unique, probably critical information and enhancing QoS. This article presents a survey of existing works on mobile crowdsensing strategies with an emphasis on reducing resource cost and achieving high QoS. We start by introducing the motivation for this survey and present the necessary background of crowdsensing and IoT. We then present various mobile crowdsensing strategies and discuss their strengths and limitations. Finally, we discuss future research directions for mobile crowdsensing for IoT. The survey addresses a broad range of techniques, methods, models, systems, and applications related to mobile crowdsensing and IoT. Our goal is not only to analyze and compare the strategies proposed in prior works, but also to discuss their applicability toward the IoT and provide guidance on future research directions for mobile crowdsensing.

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Index Terms

A Survey of Mobile Crowdsensing Techniques: A Critical Component for The Internet of Things
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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Published In

cover image ACM Transactions on Cyber-Physical Systems

ACM Transactions on Cyber-Physical Systems Volume 2, Issue 3

Special Issue on the Internet of Things: Part 2

July 2018

181 pages

ISSN:2378-962X

EISSN:2378-9638

DOI:10.1145/3232714

Editor:
Tei-Wei Kuo
National Taiwan University, and Academia Sinica, Taiwan

Issue’s Table of Contents

Copyright © 2018 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

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 13 June 2018

Accepted: 01 January 2018

Revised: 01 June 2017

Received: 01 August 2016

Published in TCPS Volume 2, Issue 3

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Sawwan AWu J(2025)Diversity-Based Recruitment in Crowdsensing by Combinatorial Multi-Armed BanditsTsinghua Science and Technology10.26599/TST.2024.901005330:2(732-747)Online publication date: Apr-2025
https://doi.org/10.26599/TST.2024.9010053
Wang YLi YWang WDuan PSai ACai Z(2025)Mobile crowdsourcing based on 5G and 6G: A surveyNeurocomputing10.1016/j.neucom.2024.128993618(128993)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128993
Casadei R(2025)System-wide IoT design and programming: Patterns for decentralised collective processesInternet of Things10.1016/j.iot.2024.10143629(101436)Online publication date: Jan-2025
https://doi.org/10.1016/j.iot.2024.101436
Cao HYu YLiu GWu Y(2025)A hyper-heuristic optimization multi-task allocation in mobile crowdsensing based on inherent attributesAd Hoc Networks10.1016/j.adhoc.2024.103717168(103717)Online publication date: Mar-2025
https://doi.org/10.1016/j.adhoc.2024.103717
Ju ZLi Y(2024)Local Differential Privacy-Based Privacy-Preserving Data Range Query Scheme for Electric Vehicle ChargingIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.330441311:1(673-684)Online publication date: Jan-2024
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Ogawa YHasegawa GMurata M(2024)Probabilistic Control of Dynamic Crowds Toward Uniform Spatial-Temporal CoverageIEEE Transactions on Mobile Computing10.1109/TMC.2022.323153023:2(1050-1065)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TMC.2022.3231530
Sánchez PCeldrán ASchenk TIten ABovet GPérez GStiller B(2024)Studying the Robustness of Anti-Adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum SensorsIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.320453521:2(573-584)Online publication date: 1-Mar-2024
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Liu CZhu KTao CChen BZhao Y(2024)UAV-Assisted Active Sparse Crowdsensing for Ground Signal Map Construction Based on 3-D Spatial–Temporal CorrelationIEEE Internet of Things Journal10.1109/JIOT.2024.339940911:16(27260-27274)Online publication date: 15-Aug-2024
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Liu SWan ZYuan YDong QYang BHao F(2024)Efficient Certificateless Blind Signature Scheme With Conditional Revocation for Mobile Crowdsensing Within Smart CityIEEE Internet of Things Journal10.1109/JIOT.2024.335002311:9(15985-15997)Online publication date: 1-May-2024
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Liu XZhou SPeng JYu JHe YZhang W(2024)Adaptive Sampling Allocation for Distributed Data Storage in Compressive CrowdSensingIEEE Internet of Things Journal10.1109/JIOT.2023.333184811:7(12022-12032)Online publication date: 1-Apr-2024
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