AoI-Aware Incentive Mechanism for UAV-Assisted Mobile Crowdsensing: A Contract-Theoretic Approach

With the popularization of mobile devices, mobile crowdsensing (MCS) has become a paradigm with broad application prospects. However, traditional MCS face numerous challenges, such as surges in network traffic and infrastructure failures. To address these issues, we leverage flexible and low-cost Unmanned Aerial Vehicles (UAVs) in the MCS framework. UAV-assisted crowdsensing (UCS) provides an innovative approach to data collection that effectively addresses problems such as insufficient network coverage and congestion. In the UCS framework, UAVs can serve not only as temporary base stations (BSs) but also participate in collecting data and processing tasks. Nevertheless, the lack of adequate incentive mechanisms may lead both UAVs and mobile users to be reluctant to participate in sensing tasks. Therefore, this paper aims to investigate hierarchical incentive mechanisms for UCS. Considering the freshness of the collected data and the benefits of the platform, we adopt the Age of Information (AoI) metric to measure the quality of data. To ensure AoI of data, we model the incentive mechanisms from both the UAV and user perspectives, and we formulate them as single-dimensional and multi-dimensional contract-based incentives under scenarios of information asymmetry. Furthermore, we derive the optimal contract scheme under the constraints of individual rationality and incentive compatibility. Finally, experimental results confirm the effectiveness of the proposed contract design and maximize the utility of the model owner.

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Publication Details

Journal
IEEE Transactions on Mobile Computing
Published
2025-08-29
DOI
https://doi.org/10.1109/tmc.2025.3604073
Citations
1
Primary Topic
Mobile Crowdsensing and Crowdsourcing
Type
article
Field-Weighted Citation Impact
1.69
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article

AoI-Aware Incentive Mechanism for UAV-Assisted Mobile Crowdsensing: A Contract-Theoretic Approach

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1 citations
IEEE Transactions on Mobile Computing
Mobile Crowdsensing and Crowdsourcing
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article

AoI-Aware Incentive Mechanism for UAV-Assisted Mobile Crowdsensing: A Contract-Theoretic Approach

Hongtao Li, Jiwei Huang, Yuan Wu, Ying Chen, Yuran Guo
article en
1 citations

Abstract

With the popularization of mobile devices, mobile crowdsensing (MCS) has become a paradigm with broad application prospects. However, traditional MCS face numerous challenges, such as surges in network traffic and infrastructure failures. To address these issues, we leverage flexible and low-cost Unmanned Aerial Vehicles (UAVs) in the MCS framework. UAV-assisted crowdsensing (UCS) provides an innovative approach to data collection that effectively addresses problems such as insufficient network coverage and congestion. In the UCS framework, UAVs can serve not only as temporary base stations (BSs) but also participate in collecting data and processing tasks. Nevertheless, the lack of adequate incentive mechanisms may lead both UAVs and mobile users to be reluctant to participate in sensing tasks. Therefore, this paper aims to investigate hierarchical incentive mechanisms for UCS. Considering the freshness of the collected data and the benefits of the platform, we adopt the Age of Information (AoI) metric to measure the quality of data. To ensure AoI of data, we model the incentive mechanisms from both the UAV and user perspectives, and we formulate them as single-dimensional and multi-dimensional contract-based incentives under scenarios of information asymmetry. Furthermore, we derive the optimal contract scheme under the constraints of individual rationality and incentive compatibility. Finally, experimental results confirm the effectiveness of the proposed contract design and maximize the utility of the model owner.

IEEE Transactions on Mobile ComputingVol. 25(2)
University of Macau (MO), China University of Petroleum, Beijing (CN), City University of Macau (MO), Beijing Information Science & Technology University (CN)
Openalex Percentile: Top 11%
Mobile Crowdsensing and Crowdsourcing
1.69
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