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Sensors 2017, 17(11), 2575;

Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks

College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China
Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, Fuzhou 350116, China
Department of Mathematics and Computer Science, Northeastern State University, Muskogee, OK 74401, USA
Authors to whom correspondence should be addressed.
Received: 28 August 2017 / Revised: 31 October 2017 / Accepted: 4 November 2017 / Published: 8 November 2017
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Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs . View Full-Text
Keywords: compressive sensing; mobile data gathering; machine learning theory; random walk; Gaussian kernel; wireless sensor networks compressive sensing; mobile data gathering; machine learning theory; random walk; Gaussian kernel; wireless sensor networks

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Zheng, H.; Li, J.; Feng, X.; Guo, W.; Chen, Z.; Xiong, N. Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks. Sensors 2017, 17, 2575.

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