Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Traffic model in heterogeneous networks based on experimental data

View through CrossRef
Almost all modern networks provide data transfer between different subscriber devices types using subnets operating in different standards. Therefore, they are heterogeneous. To obtain correct estimates of the load and throughput of heterogeneous networks, it is necessary to be able to simulate such networks traffic close to real. Therefore, the traffic model development in heterogeneous networks based on experimental data is relevant. To do this, the article conducted an experimental study of real traffic in the operator “New Technologies of the XXI Century” heterogeneous network, analyzed the traffic generation main models in wireless networks, mathematical models IPP, 4IPP, IDP and 2IRP were selected for generating traffic in heterogeneous networks and determined the necessary for them application settings. Subscribers are classified depending on the subscriber devices types, the traffic types they use, and the data transferred volume. Based on the experimental data obtained, subscribers are classified depending on the subscriber devices types, the traffic types they use, as well as the data volume they transmit. A list of the subscribers’ main classes has been generated, indicating the models used to generate their traffic, and their parameters have been determined. A traffic mathematical model in heterogeneous networks has been developed, the transmission simulation of real and generated according to the developed model traffic over a heterogeneous network has been carried out, on the basis of which the correctness of the developed model has been shown. The resulting model allows us to obtain correct estimates of heterogeneous networks main characteristics using the mathematical modeling method.
Title: Traffic model in heterogeneous networks based on experimental data
Description:
Almost all modern networks provide data transfer between different subscriber devices types using subnets operating in different standards.
Therefore, they are heterogeneous.
To obtain correct estimates of the load and throughput of heterogeneous networks, it is necessary to be able to simulate such networks traffic close to real.
Therefore, the traffic model development in heterogeneous networks based on experimental data is relevant.
To do this, the article conducted an experimental study of real traffic in the operator “New Technologies of the XXI Century” heterogeneous network, analyzed the traffic generation main models in wireless networks, mathematical models IPP, 4IPP, IDP and 2IRP were selected for generating traffic in heterogeneous networks and determined the necessary for them application settings.
Subscribers are classified depending on the subscriber devices types, the traffic types they use, and the data transferred volume.
Based on the experimental data obtained, subscribers are classified depending on the subscriber devices types, the traffic types they use, as well as the data volume they transmit.
A list of the subscribers’ main classes has been generated, indicating the models used to generate their traffic, and their parameters have been determined.
A traffic mathematical model in heterogeneous networks has been developed, the transmission simulation of real and generated according to the developed model traffic over a heterogeneous network has been carried out, on the basis of which the correctness of the developed model has been shown.
The resulting model allows us to obtain correct estimates of heterogeneous networks main characteristics using the mathematical modeling method.

Related Results

The Burden of Road Traffic Injuries: A Global Perspective
The Burden of Road Traffic Injuries: A Global Perspective
Introduction     Road Traffic Injury (RTI) pose a significant health challenge. It represents the eighth leading cause of death globally, prompting the UN to designate 2011-2020 as...
Traffic Prediction in 5G Networks Using Machine Learning
Traffic Prediction in 5G Networks Using Machine Learning
The advent of 5G technology promises a paradigm shift in the realm of telecommunications, offering unprecedented speeds and connectivity. However, the ...
Introduction to Artificial Intelligence in Traffic Systems
Introduction to Artificial Intelligence in Traffic Systems
Traffic management is a pressing challenge in modern societies. The population of humans is increasing at a substantial pace, and along with that, the expanse of urban areas and th...
TYPES OF AI ALGORİTHMS USED İN TRAFFİC FLOW PREDİCTİON
TYPES OF AI ALGORİTHMS USED İN TRAFFİC FLOW PREDİCTİON
The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic...
Smart Traffic Control Using Computer Vision
Smart Traffic Control Using Computer Vision
A Smart Traffic Control System using Computer Vision utilizes cameras, image processing techniques, and machine learning algorithms to monitor, analyze, and manage traffic flow aut...
Novel traffic congestion detection algorithms for smart city applications
Novel traffic congestion detection algorithms for smart city applications
Summary Traffic congestion detection (TCD) techniques are becoming a critical component of traffic management systems. They can be considered a pre‐step to addres...
A Traffic Flow Prediction Method Based on Blockchain and Federated Learning
A Traffic Flow Prediction Method Based on Blockchain and Federated Learning
Abstract Traffic flow prediction is the an important issue in the field of intelligent transportation, and real-time and accurate traffic flow prediction plays a crucial ro...

Back to Top