Neural Network based Network Traffic Predictability for IoT Environment
AUTHORS
Min-Su Seok,Dept. of Computer Science & Engineering, Dongguk Univ., Seoul, Republic of Korea
JinYeong Um*,Dept. of Computer Science & Engineering, Dongguk Univ., Seoul, Republic of Korea
ABSTRACT
The purpose of this paper is to predict of compare network changes using neural networks. Many devices operate for various purposes using network technology, but unmanaged network environments cause packet loss for a variety of reasons. This packet loss causes data retransmissions and delays, and in environment where limited battery or limited network resources must be utilized, various problems can occur. Experiments show how neural networks can be used to predict network changes and whether they can predict changes in real networks. Predictability of Network Traffic indicates that LSTM (Long Short-Term Memory) is higher than MLP (Multi-Layer Perceptron).
KEYWORDS
Network prediction, Internet of things, Deep learning, LSTM, MLP, Matlab
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