MAB-RSP: Data pricing based on Stackelberg game in MCS
With the proliferation of mobile smart devices and wireless communication technologies, Mobile CrowdSensing (MCS) has emerged as a significant data collection method. MCS faces two key challenges: selecting high-quality data sellers with unknown reliability and determining fair compensation that add...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
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| Series: | Array |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590005625000074 |
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| Summary: | With the proliferation of mobile smart devices and wireless communication technologies, Mobile CrowdSensing (MCS) has emerged as a significant data collection method. MCS faces two key challenges: selecting high-quality data sellers with unknown reliability and determining fair compensation that addresses device wear and privacy risks. We introduce two novel contributions. First, the MAB-RS algorithm leverages multi-armed bandit reinforcement learning and a data freshness model to dynamically optimize seller recruitment, efficiently balancing exploration of unknown sellers and exploitation of high-quality ones. Second, the MAB-RSP employs a Stackelberg game framework, enabling platforms and sellers to collaboratively maximize profits through strategic pricing and participation incentives. Experiments demonstrate that the algorithm improves revenue while ensuring balanced benefits for all participants. |
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| ISSN: | 2590-0056 |