Intelligent Traffic Prediction by Multi-sensor Fusion using Multi-threaded Machine Learning

2016 ◽  
Vol 5 (6) ◽  
pp. 430-439 ◽  
Author(s):  
Swe Sw Aung ◽  
Itaru Nagayama ◽  
Shiro Tamaki
Keyword(s):  
Machine Learning ◽  
Sensor Fusion ◽  
Traffic Prediction

scholarly journals Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1578
Author(s):  
Daniel Szostak ◽  
Adam Włodarczyk ◽  
Krzysztof Walkowiak
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Network Traffic ◽  
Optical Network ◽  
Optimization Methods ◽  
Supervised Machine Learning ◽  
Traffic Prediction ◽  
Machine Learning Classification ◽  
Classification And Regression ◽  
Network Technologies

Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks’ (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations.

On the possibility of short-term traffic prediction during disaster with machine learning approaches: An exploratory analysis

2020 ◽  
Vol 98 ◽  
pp. 91-104 ◽  
Author(s):  
Makoto Chikaraishi ◽  
Prateek Garg ◽  
Varun Varghese ◽  
Kazuki Yoshizoe ◽  
Junji Urata ◽  
...  
Keyword(s):  
Machine Learning ◽  
Exploratory Analysis ◽  
Traffic Prediction ◽  
Learning Approaches ◽  
Short Term

scholarly journals Distributed Routing Strategy Based on Machine Learning for LEO Satellite Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhenyu Na ◽  
Zheng Pan ◽  
Xin Liu ◽  
Zhian Deng ◽  
Zihe Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Low Earth Orbit ◽  
Traffic Prediction ◽  
Satellite Network ◽  
Integrated Networks ◽  
Distributed Routing ◽  
Learning Machine ◽  
Terrestrial Communications ◽  
Leo Satellite

As the indispensable supplement of terrestrial communications, Low Earth Orbit (LEO) satellite network is the crucial part in future space-terrestrial integrated networks because of its unique advantages. However, the effective and reliable routing for LEO satellite network is an intractable task due to time-varying topology, frequent link handover, and imbalanced communication load. An Extreme Learning Machine (ELM) based distributed routing (ELMDR) strategy was put forward in this paper. Considering the traffic distribution density on the surface of the earth, ELMDR strategy makes routing decision based on traffic prediction. For traffic prediction, ELM, which is a fast and efficient machine learning algorithm, is adopted to forecast the traffic at satellite node. For the routing decision, mobile agents (MAs) are introduced to simultaneously and independently search for LEO satellite network and determine routing information. Simulation results demonstrate that, in comparison to the conventional Ant Colony Optimization (ACO) algorithm, ELMDR not only sufficiently uses underutilized link, but also reduces delay.

Sign in / Sign up

Export Citation Format

Share Document