22 February 2017 Comparative study of lossy and lossless data compression in distributed optical fiber sensing systems
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Typical fully distributed optical fiber sensors (DOFS) with dozens of kilometers are equivalent to tens of thousands of point sensors along the whole monitoring line, which means tens of thousands of data will be generated for one pulse launching period. Therefore, in an all-day nonstop monitoring, large volumes of data are created thereby triggering the demand for large storage space and high speed for data transmission. In addition, when the monitoring length and channel numbers increase, the data also increase extensively. The task of mitigating large volumes of data accumulation, large storage capacity, and high-speed data transmission is, therefore, the aim of this paper. To demonstrate our idea, we carried out a comparative study of two lossless methods, Huffman and Lempel Ziv Welch (LZW), with a lossy data compression algorithm, fast wavelet transform (FWT) based on three distinctive DOFS sensing data, such as Φ - OTDR , P-OTDR, and B-OTDA. Our results demonstrated that FWT yielded the best compression ratio with good consumption time, irrespective of errors in signal construction of the three DOFS data. Our outcomes indicate the promising potentials of FWT which makes it more suitable, reliable, and convenient for real-time compression of the DOFS data. Finally, it was observed that differences in the DOFS data structure have some influence on both the compression ratio and computational cost.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
David Atubga, David Atubga, Huijuan Wu, Huijuan Wu, Lidong Lu, Lidong Lu, Xiaoyan Sun, Xiaoyan Sun, } "Comparative study of lossy and lossless data compression in distributed optical fiber sensing systems," Optical Engineering 56(2), 024108 (22 February 2017). https://doi.org/10.1117/1.OE.56.2.024108 . Submission: Received: 14 August 2016; Accepted: 17 January 2017
Received: 14 August 2016; Accepted: 17 January 2017; Published: 22 February 2017

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