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Adaptive sampling and sensing approach with mobile sensor networks
Zhang H(张浩); Zhu YL(朱云龙); Tan JD(谈金东)
Department信息服务与智能控制技术研究室
Conference Name2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
Conference DateJune 8-12, 2015
Conference PlaceShenyang, China
Source Publication2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2015
Pages654-660
Indexed ByEI ; CPCI(ISTP)
EI Accession number20161402187629
WOS IDWOS:000380502300125
Contribution Rank1
ISSN2379-7711
ISBN978-1-4799-8730-6
AbstractThis paper presents an adaptive sampling and sensing method for mobile sensor networks to reduce the number of measurements and the sampling cost based on compressive sensing. Compressive sensing always uses random measurements, whose information amount cannot be determined previously. The proposed adaptive sampling and sensing approach can find the most informative measurements in unknown environments and reconstruct the original signals. With mobile sensors, measurements are collected sequentially and organically, which give the chance to optimize each of them uniquely. When a mobile sensor is about to collect a new measurement from the surrounding environment, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under a sparse domain to infer a model of the environments. The most informative measurements can be determined by optimizing the model. The simulation results demonstrate the effectiveness and the adaptability of the proposed, theoretically-correct algorithm.
Language英语
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/17371
Collection信息服务与智能控制技术研究室
Affiliation1.Dept. of Information Service and Intelligent Control, Shenyang Institute of Automation of Chinese Academy of Sciences, Shenyang, China
2.Dept. of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN, United States
Recommended Citation
GB/T 7714
Zhang H,Zhu YL,Tan JD. Adaptive sampling and sensing approach with mobile sensor networks[C]. Piscataway, NJ, USA:IEEE,2015:654-660.
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