Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet

Ali R. Abdellah, Omar Abdulkareem Mahmood, Ruslan Kirichek, Alexander Paramonov and Andrey Koucheryavy
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Ali R. Abdellah: Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Qena 83513, Egypt
Omar Abdulkareem Mahmood: Department of Communications Engineering, College of Engineering, University of Diyala, Baquba 32001, Iraq
Ruslan Kirichek: Department of Communication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 St. Petersburg, Russia
Alexander Paramonov: Department of Communication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 St. Petersburg, Russia
Andrey Koucheryavy: Department of Communication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, 193232 St. Petersburg, Russia

Future Internet, 2021, vol. 13, issue 12, 1-19

Abstract: The next-generation cellular systems, including fifth-generation cellular systems (5G), are empowered with the recent advances in artificial intelligence (AI) and other recent paradigms. The internet of things (IoT) and the tactile internet are paradigms that can be empowered with AI solutions and integrated with 5G systems to deliver novel services that impact the future. Machine learning technologies (ML) can understand examples of nonlinearity from the environment and are suitable for network traffic prediction. Network traffic prediction is one of the most active research areas that integrates AI with information networks. Traffic prediction is an integral approach to ensure security, reliability, and quality of service (QoS) requirements. Nowadays, it can be used in various applications, such as network monitoring, resource management, congestion control, network bandwidth allocation, network intrusion detection, etc. This paper performs time series prediction for IoT and tactile internet delays, using the k -step-ahead prediction approach with nonlinear autoregressive with external input (NARX)-enabled recurrent neural network (RNN). The ML was trained with four different training functions: Bayesian regularization backpropagation (Trainbr), Levenberg–Marquardt backpropagation (Trainlm), conjugate gradient backpropagation with Fletcher–Reeves updates (Traincgf), and the resilient backpropagation algorithm (Trainrp). The accuracy of the predicted delay was measured using three functions based on ML: mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE).

Keywords: 5G; IoT; tactile internet; machine learning; AI; traffic prediction (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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