Research Progress on Incentive Mechanisms in Mobile Crowdsensing

With the continuous improvement of the sensing, transmission, storage, and computing capabilities of mobile devices, they have become important tools for perceiving the physical environment and social phenomena. Mobile crowdsensing (MCS) is a data sensing paradigm that utilizes a large number of mob...

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Bibliographic Details
Published in:IEEE internet of things journal 2024-07, Vol.11 (14), p.24621-24633
Main Authors: Wu, Enhui, Peng, Zhenlong
Format: Article
Language:English
Subjects:
Continuous improvement
Data collection
Data integrity
Data privacy
Data quality
Electronic devices
Game theory
incentive mechanism
Internet of Things
learning
mobile crowdsensing (MCS)
Pricing
Resource management
Sensors
Software
State-of-the-art reviews
Task analysis
Task complexity
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Summary:With the continuous improvement of the sensing, transmission, storage, and computing capabilities of mobile devices, they have become important tools for perceiving the physical environment and social phenomena. Mobile crowdsensing (MCS) is a data sensing paradigm that utilizes a large number of mobile devices to collect various types of sensing data, ultimately accomplishing large-scale and complex tasks. Effective incentive mechanisms can motivate users to actively participate in data collection tasks and provide high-quality data, making it one of the key issues in MCS. This article reviews the state-of-the-art incentive mechanisms in MCS systems. This article begins with an introduction to the concept of the MCS incentive mechanism, categorizing incentive mechanisms based on different standards. Subsequently, it addresses the primary research issues concerning incentive mechanisms, including data quality, online scenarios, and privacy protection. Then, from the perspective of incentive mechanism technology, it reviews the research progress of incentive mechanisms in recent years, mainly including four types of incentive mechanisms: 1) game theory-based incentive mechanisms; 2) auction theory-based incentive mechanisms; 3) reward allocation-based incentive mechanisms; and 4) learning-based incentive mechanisms, and provides a brief evaluation of each mechanism. Finally, we propose future research directions for MCS incentive mechanisms.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3400965