IoT traffic prediction using multi-step ahead prediction with neural network

Abstract

Internet of Things (IoT) is a network of interconnected devices, such as sensors and smart devices that have processing, sensing, and communication capabilities, as well as can transmit information to each other and a supreme console through the Internet. Network traffic prediction is an important operational and management function for any data network. It has a significant role in today's increasingly complex and diverse networks. Network traffic prediction is also more important for IoT networks to provide reliable communication. The artificial neural network (ANN) has been successfully applied to traffic prediction. In this paper, we perform the IoT traffic time series prediction using a multistep ahead prediction with Time Series NARX Feedback Neural Networks. The estimation error of a prediction approach has been evaluated using the performance functions MSE, SSE, and MAE, besides, another measure of prediction accuracy the mean absolute percent of error (MAPE).