 A data value-driven collaborative data collection method in complex multi-constraint environmentsLinLiang Zhang, LianShan Yan, ZhiSheng Liu, Shuo Li, RuiFang Du and ZhiGuo Hu International Journal of Data Science, 2025, vol. 10, issue 1, 27-52 Abstract: Data collection is a foundational task in mobile crowd sensing. However, existing data collection methods prioritise quantity, neglecting heterogeneity, cooperation, energy efficiency, and collision avoidance, causing low multi-agent efficiency in complex scenarios. To address this issue, this paper integrates multi-agent reinforcement learning and deep learning to propose the CS_MCE method. The CS_MCE method, applying to unmanned aerial vehicle (UAV) collaborative data collection scenarios, utilises deep neural networks to solve representation problems in vast state-action spaces and provides intelligent decision-making capabilities. In various experimental environments with different data values, experiments comparing CS_MCE with the MADDPG and IL-DDPG algorithms in terms of reward values, data quality, energy efficiency, and the number of collisions showed that the data quality collected by CS_MCE increased by 5-6 times, and energy efficiency improved by more than 60%, demonstrating the efficiency and stability of the CS_MCE method. Keywords: MCS; mobile crowd-sensing; data collection; heterogeneous data; unmanned vehicles; deep reinforcement learning. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdsci:v:10:y:2025:i:1:p:27-52 Access Statistics for this article More articles in International Journal of Data Science from Inderscience Enterprises Ltd |
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