When Crowdsourcing Meets Unmanned Vehicles: Toward Cost-Effective Collaborative Urban Sensing via Deep Reinforcement Learning

Author:

Ding Lige,Zhao DongORCID,Cao Mingzhe,Ma HuadongORCID

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Funds for International Cooperation and Exchange of NSFC

111 Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Location-Privacy-Aware Taxi-Hailing System: Adaptive Differential Privacy-Based Dynamic Incentive Method;IEEE Internet of Things Journal;2024-01-01

2. A survey of urban drive-by sensing: An optimization perspective;Sustainable Cities and Society;2023-12

3. Exploring both Individuality and Cooperation for Air-Ground Spatial Crowdsourcing by Multi-Agent Deep Reinforcement Learning;2023 IEEE 39th International Conference on Data Engineering (ICDE);2023-04

4. Spatio-temporal Feature Based Multi-participant Recruitment in Heterogeneous Crowdsensing;2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta);2022-12

5. Diversity-aware unmanned vehicle team arrangement in mobile crowdsourcing;EURASIP Journal on Wireless Communications and Networking;2022-06-23

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