Abstract
A large number of IoT devices are being used in the world. Data acquired by IoT devices are used as Big Data. These data sent from IoT devices to access points (AP) or base stations through wireless networks are stored in the cloud or data centers via wired networks such as back haul and backbone networks. The bandwidth of wireless or wired networks is oppressed by the transmission of a large amount of data. It causes network congestion, and the data cannot transmit and receive properly. To detect a sign of network congestion and prevent it is a very effective way to alleviate such congestion. However, it is difficult for us to predict a fluctuation of network traffic because it is caused by many factors and highly complex. Therefore, we try to predict it using Recurrent Neural Network (RNN), which exhibits dynamic temporal behavior for a time sequence in Deep Learning classes.
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Yamamoto, A. et al. (2019). Prediction of Traffic Congestion on Wired and Wireless Networks Using RNN. In: Lee, S., Ismail, R., Choo, H. (eds) Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019. IMCOM 2019. Advances in Intelligent Systems and Computing, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-19063-7_26
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