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Title: Semitensor Product Compressive Sensing for Big Data Transmission in Wireless Sensor Networks
Authors: Peng, H.Tian, Y.Kurths, J.
Publishers Version: https://doi.org/10.1155/2017/8158465
Issue Date: 2017
Published in: Mathematical Problems in Engineering Vol. 2017 (2017)
Publisher: London : Hindawi Limited
Abstract: Big data transmission in wireless sensor network (WSN) consumes energy while the node in WSN is energy-limited, and the data transmitted needs to be encrypted resulting from the ease of being eavesdropped in WSN links. Compressive sensing (CS) can encrypt data and reduce the data volume to solve these two problems. However, the nodes in WSNs are not only energy-limited, but also storage and calculation resource-constrained. The traditional CS uses the measurement matrix as the secret key, which consumes a huge storage space. Moreover, the calculation cost of the traditional CS is large. In this paper, semitensor product compressive sensing (STP-CS) is proposed, which reduces the size of the secret key to save the storage space by breaking through the dimension match restriction of the matrix multiplication and decreases the calculation amount to save the calculation resource. Simulation results show that STP-CS encryption can achieve better performances of saving storage and calculation resources compared with the traditional CS encryption.
Keywords: Compressed sensing; Cryptography; Data communication systems; Data transfer; Digital storage; Matrix algebra; Sensor nodes; Signal reconstruction; Wireless sensor networks; Calculation cost; Compressive sensing; Data volume; MAtrix multiplication; Measurement matrix; Secret key; Semi-tensor product; Storage spaces; Big data
DDC: 620
License: CC BY 4.0 Unported
Link to License: https://creativecommons.org/licenses/by/4.0/
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