文章基本信息

标题：Comparing Serial and Parallel Compressive Sensing for Internet Traffic Matrix
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作者：Indrarini Dyah Irawati ; Andriyan Bayu Suksmono ; Ian Joseph Matheus Edward 等
期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2018
卷号：2233&2234
页码：387-392
出版社：Newswood and International Association of Engineers
摘要：Compressive Sensing (CS) is a new method capable of efficiently reconstructing signals by using sparse sample. However, CS algorithms require processing time very extensive especially since the amount of data is very large. In this paper, we evaluated the effect of using double CS processes either serial CS (SCS) and parallel CS (PCS) on Internet traffic matrix. We also compared two reconstruction algorithms, which are Orthogonal Matching Pursuit and Iteratively Reweighted Least Square (IRLS). SCS produces poor accuracy with longer processing time, while PCS produce accuracy similar to CS scheme with shorter processing time. We also examine the effect of subparallel on the performance results. The results show that the greater number of subparallel accelerate the processing time for IRLS, contrary to OMP, where more subparallel, decreasing accuracy.
关键词：compressive sensing; parallel; subparallel; serial; processing time; accuracy