Dynamic SIoT Network Status Prediction

Limei Peng

¹ Department of Data Convergence Computing, Kyungpook National University, Daegu 41566, South Korea, e-mail: [email protected]

² Department of Computer Science and Engineering, Kyungpook National University, Daegu 41566, South Korea, e-mail: [email protected]

³ Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA , e-mail: [email protected]

⁴ Department of Computer Science and Engineering, Kyungpook National University, Daegu 41566, South Korea. e-mail: [email protected], 

⁵ Department of Electrical and Computer Enginnering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada, e-mail: [email protected]

*Corresponding author

Prediction of social IoT (SIoT) data traffic is helpful in characterizing the complicated relationships for such as device-to-device, user-to-user, and user-to-device. One of the most popular traffic prediction methods in noisy environments is the Kalman filter (KF), which is extremely simple and general. Nevertheless, KF requires a dynamic traffic and measurement model as a priori, which introduces extra overhead and is often difficult to obtain in reality. In comparison, deep learning models with a Recurrent Neural Network (RNN) structure have been used extensively in modeling dynamic models evolving over time. On the other hand, the Content Adaptive Recurrent Unit (CARU) is an improvement of RNN that uses fewer parameters than the LSTM and GRU and thus is more promising in predicting the SIoT data traffic. This paper proposes the CARU-based extended Kalman filter (CARU-EKF) model, which is a new deep learning cell that utilizes CARU to predict extended Kalman filter (EKF) system parameters. Note that EKF is proper to predict nonlinear SIoT traffic in noisy environments. The proposed CARU-EKF can improve the performance of time-series data forecasting for nonlinear SIoT data traffic. Numerical experiments are conducted to evaluate the SIoT traffic prediction performance of the proposed CARU-EKF approach over two real datasets, i.e., IoT device traffic and wikipedia webpage visiting traffic. The proposed method shows better performance than existing prediction methods in terms of metrics of Mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and determination coefficient (R2).

Extended Kalman filter (EKF), video frame-size predictor, multimedia network, MPEG-4 codec, matrix-based Levenberg-Marquardt algorithm (MLMA), normalized mean square error (NMSE)

Dong Hu, Shuai Lyu, Shih Yu Chang, Limei Peng and Pin-Han Ho (2022). Dynamic SIoT Network Status Prediction. Journal of Networking and Network Applications, Volume 2, Issue 2, pp. 78–85. https://doi.org/10.33969/J-NaNA.2022.020203.

[1] P. Gokhale, O. Bhat, and S. Bhat, “Introduction to IOT,” International Advanced Research Journal in Science, Engineering and Technology, vol. 5, no. 1, pp. 41–44, 2018.

[2] F. Al-Turjman, “5G-enabled devices and smart-spaces in social-IoT: an overview,” Future Generation Computer Systems, vol. 92, pp. 732–744, 2019.

[3] X. Su, Y. Zheng, J. Lin, and X. Liu, “A network traffic-aware mo-bile application recommendation system based on network traffic cost consideration,” International Journal of Computational Science and Engineering, vol. 19, no. 2, pp. 259–273, 2019.

[4] K. Xu, F. Wang, and L. Gu, “Behavior analysis of internet traffic via bipartite graphs and one-mode projections,” IEEE/ACM Transactions on Networking, vol. 22, no. 3, pp. 931–942, 2013.

[5] H. Z. Moayedi and M. Masnadi-Shirazi, “ARIMA model for network traffic prediction and anomaly detection,” in 2008 international sympo-sium on information technology, vol. 4. IEEE, 2008, pp. 1–6.

[6] B. Zhou, D. He, Z. Sun, and W. H. Ng, “Network traffic modeling and prediction with ARIMA/GARCH,” in Proc. of HET-NETs Conference, 2005, pp. 1–10.

[7] A. Sang and S.-q. Li, “A predictability analysis of network traffic,” Computer networks, vol. 39, no. 4, pp. 329–345, 2002.

[8] R. Vinayakumar, K. Soman, and P. Poornachandran, “Applying deep learning approaches for network traffic prediction,” in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2017, pp. 2353–2358.

[9] H. D. Trinh, L. Giupponi, and P. Dini, “Mobile traffic prediction from raw data using LSTM networks,” in 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Com-munications (PIMRC). IEEE, 2018, pp. 1827–1832.

[10] K.-H. Chan, W. Ke, and S.-K. Im, “CARU: A content-adaptive recurrent unit for the transition of hidden state in NLP,” in International Confer-ence on Neural Information Processing. Springer, 2020, pp. 693–703. 

[11] C. P. Van Hinsbergen, T. Schreiter, F. S. Zuurbier, J. Van Lint, and H. J. Van Zuylen, “Localized extended Kalman filter for scalable real-time traffic state estimation,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 1, pp. 385–394, 2011.

[12] M. L. Martin, A. Sanchez-Esguevillas, and B. Carro, “Review of methods to predict connectivity of IoT wireless devices.” Ad Hoc Sens. Wirel. Networks, vol. 38, no. 1-4, pp. 125–141, 2017.

[13]“Web traffic time series forecast,” https://www.kaggle.com/c/web-traffic-time-series-forecasting, accessed: 2018-09-30.

[14]“The a´n´d wikipedia webpage,” https://zh.wikipedia.org/wiki/AND.

[15]“The erni mangold wikipedia webpag,” https://en.wikipedia.org/wiki/Erni Mangold.