Reinforcement learning-based cell selection in sparse mobile crowdsensing

Sparse Mobile Crowdsensing (MCS) is a novel MCS paradigm which allows us to use the mobile devices to collect sensing data from only a small subset of cells (sub-areas) in the target sensing area while intelligently inferring the data of other cells with quality guarantee. Since selecting sensed dat...

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Bibliographic Details
Published in:	Computer networks (Amsterdam, Netherlands : 1999) Vol. 161; pp. 102 - 114
Main Authors:	Liu, Wenbin, Wang, Leye, Wang, En, Yang, Yongjian, Zeghlache, Djamal, Zhang, Daqing
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 09.10.2019 Elsevier Sequoia S.A Elsevier
Subjects:	Algorithms Cell selection Compressive sensing Computer Science Data acquisition Data collection Data quality Dependence Dependency Detection Electronic devices Experiments Inference Learning Machine learning Mobile crowdsensing Mobile phones Networks Recurrent Reinforcement Reinforcement learning Temporal patterns Compressive sensing Mobile crowdsensing Reinforcement learning Cell selection
ISSN:	1389-1286, 1872-7069
Online Access:	Get full text
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