Predicting Internet of Things Data Traffic Through LSTM and Autoregressive Spectrum Analysis

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM’s performance deterioration with the increase of sequence length. A sliding window enables predicting the long–term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.

Authors:
Li Yuhong, Wang Bailin, Su Xiang, Riekki Jukka, Sun Chao, Wei Hanyu, Wang Hao, Han Lei

Publication type:
A4 Article in conference proceedings

Place of publication:
2020 IEEE/IFIP Network Operations and Management Symposium, NOMS 2020

Keywords:
autoregressive spectrum analysis, data traffic prediction, Internet of Things, LSTM

Published:

Full citation:
Y. Li et al., “Predicting Internet of Things Data Traffic Through LSTM and Autoregressive Spectrum Analysis,” NOMS 2020 – 2020 IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary, 2020, pp. 1-8, doi: 10.1109/NOMS47738.2020.9110357

DOI:
https://doi.org/10.1109/NOMS47738.2020.9110357

Read the publication here:
http://urn.fi/urn:nbn:fi-fe2020110989691