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

Document Type : Special Issue: Deep Learning for Visual Information Analytics and Management.

Author

Assistant Professor, Statistics Department, Collage of management and economic, University of Basra, Iraq.

Abstract

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.

Keywords

References

Al-Alawi, A. I. (2006). WiFi technology: Future market challenges and opportunities. Journal of computer Science, 2(1), 13-18.
Ali, A. M. A., Yadav, R., & Singh, H. M. (2014). Congestion Control Technique for Wireless Networks. IOSR Journal of Computer Engineering, 16 (2), 31-33.
Al-Sarkhi, A., & R Talburt, J. (2019). Estimating the parameters for linking unstandardized references with the matrix comparator. Journal of Information Technology Management, 10(4), 12-26.
Barzin, A., Sadeghieh, A., Khademi Zare, H., & Honarvar, M. (2019). Hybrid bio-inspired clustering algorithm for energy efficient wireless sensor networks. Journal of Information Technology Management, 11(1), 76-101.
Bianchi, F. M., Maiorino, E., Kampffmeyer, M. C., Rizzi, A., & Jenssen, R. (2017). An overview and comparative analysis of recurrent neural networks for short term load forecasting. arXiv preprint arXiv:1705.04378.
Cisco predicts more IP traffic in the next five years than in the history of the Internet (2018). https://newsroom.cisco.com/press-release content?type= webcontent & articleId=1955935
Ghoumid, K., Ameziane, K., & El Mrabet, O. (2013). Performance analysis of round trip time in narrowband RF networks for remote wireless communications. International Journal of Computer Science & Information Technology, 5(5), 1.
Gheni, A. Y., Jusoh, Y. Y., Jabbar, M. A., Mohd Ali, N., Shanmugam, M., & Yousif, H. A. (2019). Measuring the performance of the virtual teams in global software development projects. Journal of Information Technology Management, 11(1), 42-59.
 Khalaf, O. I., & Sabbar, B. M. (2019). An overview on wireless sensor networks and finding optimal location of nodes. Periodicals of Engineering and Natural Sciences, 7(3), 1096-1101.
Khalaf, O. I., & Sabbar, B. M. (2019). An overview on wireless sensor networks and finding optimal location of nodes. Periodicals of Engineering and Natural Sciences, 7(3), 1096- 1101.
Khasawneh, F. (2017). Mobility management and congestion control in wireless mesh networks.
Lv. Yisheng, Yanjie Duan, Wenwen Kang, Zhengxi Li, and Fei-Yue Wang, (2015).  Traffic Flow Prediction With Big Data: A Deep Learning Approach,  IEEE Transactions On Intelligent Transportation Systems, 16 (2).
Marwa O Al-Enany's., Attiya Gamal.,  Meseha, Nagy W. (2014). Improving Host-to-Host Congestion Control Protocols by Dynamic Bandwidth Estimation of the Network,   International Journal of Computer Applications, 104(1), 975-1008
Miki, K., Yamaguchi, S., & Oguchi, M. (2011). Kernel monitor of trans port layer developed for android working on mobile phone terminals.
Sak, H., Senior, A. W., & Beaufays, F. (2014). Long short-term memory recurrent neural network architectures for large scale acoustic modeling.
Salman, A. D., Khalaf, O. I., & Abdulsahib, G. M. (2019). An adaptive intelligent alarm system for wireless sensor network. Indonesian Journal of Electrical Engineering and Computer Science, 15(1), 142-147.
Thomas, G. (2000). Introduction to the transmission control protocol, contemporary controls.
Tokui, S., Oono, K., Hido, S., & Clayton, J. (2015, December). Chainer: a next-generation open source framework for deep learning. In Proceedings of workshop on machine learning systems (LearningSys) in the twenty-ninth annual conference on neural information processing systems (NIPS) (Vol. 5, pp. 1-6).
Yuanfang Cheny, Lei Wangz., (2017). Poster abstract: Traffic flow prediction with big data: a deep learning based time series model, 865.
Journal of Information Technology Management
Volume 11, Issue 4
2019
Pages 70-79

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