TY - JOUR
T1 - Mobile device-centric approach for identifying problem spot in network using deep learning
AU - Lee, Woonghee
AU - Lee, Joon Yeop
AU - Kim, Hwangnam
N1 - Funding Information:
Manuscript received December 19, 2020; revised April 22, 2020; approved for publication by Namyoon Lee, guest editor, May 1, 2020. The authors gratefully acknowledge the support from Nano UAV Intelligence Systems Research Laboratory at Kwangwoon University, originally funded by Defense Acquisition Program Administration (DAPA) and Agency for Defense Development (ADD). The authors are with the School of Electrical Engineering, Korea University, email: {tgorevenge, charon7, hnkim}@korea.ac.kr. H. Kim is the corresponding author. W. Lee and J. Y. Lee equally contributed to this work. Digital Object Identifier: 10.1109/JCN.2020.000008
Publisher Copyright:
© 2011 KICS.
PY - 2020/6
Y1 - 2020/6
N2 - These days, mobile devices usually have multiple network interfaces and there are many usable access networks around the devices. To utilize a wide range of network options properly and make decisions more intelligently, the mobile devices should be able to understand networks' situations autonomously. The current mobile devices have powerful computing power and are able to collect various network information, and people nowadays almost always carry their mobile devices. Thus, the mobile devices can be utilized to figure out practical quality of service/experience and infer the network situation/context. However, networks have become not only larger but also more complex and dynamic than in the past, so it is hard to devise models, algorithms, or system platforms for mobile devices to understand such complex and diverse networks. To overcome this limitation, we leverage deep learning to devise a mobile device-centric approach to identifying problem spot having the most likely cause of network quality degradation, MoNPI. By using MoNPI, mobile devices are able to identify the network problem spot, which is like a black box to end nodes heretofore. Mobile devices with MoNPI are able to understand networks' situations and thus take a more proper action.
AB - These days, mobile devices usually have multiple network interfaces and there are many usable access networks around the devices. To utilize a wide range of network options properly and make decisions more intelligently, the mobile devices should be able to understand networks' situations autonomously. The current mobile devices have powerful computing power and are able to collect various network information, and people nowadays almost always carry their mobile devices. Thus, the mobile devices can be utilized to figure out practical quality of service/experience and infer the network situation/context. However, networks have become not only larger but also more complex and dynamic than in the past, so it is hard to devise models, algorithms, or system platforms for mobile devices to understand such complex and diverse networks. To overcome this limitation, we leverage deep learning to devise a mobile device-centric approach to identifying problem spot having the most likely cause of network quality degradation, MoNPI. By using MoNPI, mobile devices are able to identify the network problem spot, which is like a black box to end nodes heretofore. Mobile devices with MoNPI are able to understand networks' situations and thus take a more proper action.
KW - Deep learning
KW - mobile
KW - network
KW - problem spot identification
KW - transmission control protocol
UR - http://www.scopus.com/inward/record.url?scp=85089302789&partnerID=8YFLogxK
U2 - 10.1109/JCN.2020.000008
DO - 10.1109/JCN.2020.000008
M3 - Article
AN - SCOPUS:85089302789
SN - 1229-2370
VL - 22
SP - 259
EP - 268
JO - Journal of Communications and Networks
JF - Journal of Communications and Networks
IS - 3
M1 - 9143578
ER -