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Badr Jarah, Nada. (1398). Simulate Congestion Prediction in a Wireless Network Using the LSTM Deep Learning Model. , 11(4), 70-79. doi: 10.22059/jitm.2019.74762
Nada Badr Jarah. "Simulate Congestion Prediction in a Wireless Network Using the LSTM Deep Learning Model". , 11, 4, 1398, 70-79. doi: 10.22059/jitm.2019.74762
Badr Jarah, Nada. (1398). 'Simulate Congestion Prediction in a Wireless Network Using the LSTM Deep Learning Model', , 11(4), pp. 70-79. doi: 10.22059/jitm.2019.74762
Badr Jarah, Nada. Simulate Congestion Prediction in a Wireless Network Using the LSTM Deep Learning Model. , 1398; 11(4): 70-79. doi: 10.22059/jitm.2019.74762

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

Journal of Information Technology Management
مقاله 5، دوره 11، شماره 4، 2019، صفحه 70-79    اصل مقاله (667.3 K)
نوع مقاله: Special Issue: Deep Learning for Visual Information Analytics and Management.
شناسه دیجیتال (DOI): 10.22059/jitm.2019.74762
نویسنده
Nada Badr Jarah*
Assistant Professor, Statistics Department, Collage of management and economic, University of Basra, Iraq.
چکیده
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.
کلیدواژه‌ها
AP؛ Android؛ Congestion؛ Deep learning؛ LSTM؛ Wireless networks
مراجع

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