[vc_empty_space][vc_empty_space]
Enhanced OMP for Missing Traffic Reconstruction based on Sparse SVD
Irawati I.D.a, Suksmono A.B.a, Matheus Edward I.J.a
a School of Electrical and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.Missing large amount of internet data is a crucial issue to be addressed in network monitoring. The missing information should be restored using only a minimum knowledge of the data. Compressive Sampling (CS) algorithm provides a solution to complete data by utilizing the properties of randomness in the input data. Recently the reconstruction algorithm has developed in the base dictionary using orthogonal based operators. In this paper, we consider a CS approch to solve the missing problem using Singular Value Decomposition (SVD) sparsity, routing matrix for measurement matrix, and Orthogonal Matching Pursuit (OMP) as a recovery algorithm. To improve the accuracy, we also incorporating linear interpolation after OMP and Bilinear interpolation after SVD reconstruction. The missing scheme is randomized to simulate the actual behaviour of the network. Our experiments show that our proposed method is capable to fix large missing values with a high degree of accuracy for all missing type. This method is superior compared to the method in previous studies.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]accuracy,Bilinear interpolation,Compressive sampling,High degree of accuracy,Linear Interpolation,Orthogonal matching pursuit,Reconstruction algorithms,Traffic data[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]accuracy,compression ratio,compressive sampling,missing reconstruction,traffic data[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]ACKNOWLEDGMENT This research is partially supported by Telkom Foundation and LPPM ITB. We are grateful to our colleagues from LTRGM Laboratory who contributed greatly to this research.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICT.2019.8798801[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]