A data value-driven collaborative data collection method in complex multi-constraint environments
by LinLiang Zhang; LianShan Yan; ZhiSheng Liu; Shuo Li; RuiFang Du; ZhiGuo Hu
International Journal of Data Science (IJDS), Vol. 10, No. 1, 2025

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.

Online publication date: Tue, 04-Mar-2025

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Science (IJDS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com