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.
