Simulate Congestion Prediction in a Wireless Network Using the LSTM Deep Learning Model

Achieved wireless networks since its beginning the prevalent wide due to the increasing wireless devices represented by smart phones and laptop, and the proliferation of networks coincides with the high speed and ease of use of the Internet and enjoy the delivery of various data such as video clips...

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Main Author: Nada Badr Jarah
Format: Article
Language:fas
Published: University of Tehran 2019-12-01
Series:Journal of Information Technology Management
Subjects:
ap
Online Access:https://jitm.ut.ac.ir/article_74762_442b6197cd4283c5f42ba88fadcacd64.pdf
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spelling doaj-7ff3f24d4fde40fba3a5f59d8c123a6a2020-11-25T02:43:22ZfasUniversity of TehranJournal of Information Technology Management 2008-58932423-50592019-12-01114707910.22059/jitm.2019.7476274762Simulate Congestion Prediction in a Wireless Network Using the LSTM Deep Learning ModelNada Badr Jarah0Assistant Professor, Statistics Department, Collage of management and economic, University of Basra, Iraq.Achieved wireless networks since its beginning the prevalent wide due to the increasing wireless devices represented by smart phones and laptop, and the proliferation of networks coincides with the high speed and ease of use of the Internet and enjoy the delivery of various data such as video clips and games. Here's the show the congestion problem arises and represent   aim of the research is to avoid congestion at APs to wireless networks by adding a control before congestion occurs. A wireless connection was made using the Android system, and congestion was predicted based on the analysis of wireless communication packages around the access point using the LSTM deep learning model. The results show that if the amount of information in the input data is large, a more accurate prediction can be made.https://jitm.ut.ac.ir/article_74762_442b6197cd4283c5f42ba88fadcacd64.pdfapandroidcongestiondeep learninglstmwireless networks
collection DOAJ
language fas
format Article
sources DOAJ
author Nada Badr Jarah
spellingShingle Nada Badr Jarah
Simulate Congestion Prediction in a Wireless Network Using the LSTM Deep Learning Model
Journal of Information Technology Management
ap
android
congestion
deep learning
lstm
wireless networks
author_facet Nada Badr Jarah
author_sort Nada Badr Jarah
title Simulate Congestion Prediction in a Wireless Network Using the LSTM Deep Learning Model
title_short Simulate Congestion Prediction in a Wireless Network Using the LSTM Deep Learning Model
title_full Simulate Congestion Prediction in a Wireless Network Using the LSTM Deep Learning Model
title_fullStr Simulate Congestion Prediction in a Wireless Network Using the LSTM Deep Learning Model
title_full_unstemmed Simulate Congestion Prediction in a Wireless Network Using the LSTM Deep Learning Model
title_sort simulate congestion prediction in a wireless network using the lstm deep learning model
publisher University of Tehran
series Journal of Information Technology Management
issn 2008-5893
2423-5059
publishDate 2019-12-01
description Achieved wireless networks since its beginning the prevalent wide due to the increasing wireless devices represented by smart phones and laptop, and the proliferation of networks coincides with the high speed and ease of use of the Internet and enjoy the delivery of various data such as video clips and games. Here's the show the congestion problem arises and represent   aim of the research is to avoid congestion at APs to wireless networks by adding a control before congestion occurs. A wireless connection was made using the Android system, and congestion was predicted based on the analysis of wireless communication packages around the access point using the LSTM deep learning model. The results show that if the amount of information in the input data is large, a more accurate prediction can be made.
topic ap
android
congestion
deep learning
lstm
wireless networks
url https://jitm.ut.ac.ir/article_74762_442b6197cd4283c5f42ba88fadcacd64.pdf
work_keys_str_mv AT nadabadrjarah simulatecongestionpredictioninawirelessnetworkusingthelstmdeeplearningmodel
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