Compressed sensing signal and data acquisition in wireless sensor networks and internet of things

Li, Shancang; Xu, Li Da; Wang, Xinheng

Abstract

The emerging compressed sensing (CS) theory can significantly reduce the number of sampling points that directly corresponds to the volume of data collected, which means that part of the redundant data is never acquired. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear reconstruction algorithm and random sampling on a sparse basis that provides a promising approach to compress signal and data in information systems. This paper investigates how CS can provide new insights into data sampling and acquisition in wireless sensor networks and IoT. First, we briefly introduce the CS theory with respect to the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the end nodes measure, transmit, and store the sampled data in the framework. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment. © 2013 IEEE.

Citation

Li, S., Xu, L. D., & Wang, X. (2013). Compressed sensing signal and data acquisition in wireless sensor networks and internet of things. IEEE Transactions on Industrial Informatics, 9(4), 2177-2186. https://doi.org/10.1109/TII.2012.2189222

• Journal Article Type: Article
• Acceptance Date: Feb 1, 2013
• Publication Date: Nov 4, 2013
• Deposit Date: Feb 1, 2018
• Publicly Available Date: Feb 1, 2018
• Journal: IEEE Transactions on Industrial Informatics
• Print ISSN: 1551-3203
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 4
• Pages: 2177-2186
• DOI: https://doi.org/10.1109/TII.2012.2189222
• Keywords: compessed sensing, data acquisition, internet of things
• Public URL: https://uwe-repository.worktribe.com/output/939095
• Publisher URL: https://doi.org/10.1109/TII.2012.2189222
• Additional Information: Additional Information : This is the accepted version of the article, the final version of which can be viewed online at: https://doi.org/10.1109/TII.2012.2189222