scholarly journals SDN based Network Traffic Routing in Vehicular Networks: A Scheme and Simulation Analysis

2020 ◽  
Author(s):  
Jitendra Bhatia ◽  
Mohammad Obaidat ◽  
Tirath Savasaiya ◽  
Hardik Trivedi ◽  
Sudeep Tanwar ◽  
...  
Keyword(s):  
Network Traffic ◽  
Vehicular Networks ◽  
Simulation Analysis ◽  
Traffic Routing

Network traffic routing using effective bandwidth theory

2009 ◽  
Vol 20 (7) ◽  
pp. 660-667 ◽  
Author(s):  
Adam Kozakiewicz ◽  
Krzysztof Malinowski
Keyword(s):  
Network Traffic ◽  
Effective Bandwidth ◽  
Traffic Routing

A STUDY OF ROUTING METHODS IN URBAN WIRELESS MESH NETWORKS BASED ON REINFORCEMENT LEARNING

2021 ◽  
Author(s):  
В.Д. ФАМ ◽  
Р.В. КИРИЧЕК ◽  
А.С. БОРОДИН
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Simulation Model ◽  
Network Traffic ◽  
Mesh Networks ◽  
Mesh Network ◽  
Queueing Model ◽  
Traffic Routing ◽  
Wireless Mesh ◽  
Routing Methods

Приведены результаты исследования методов маршрутизации на основе обучения с подкреплением с помощью имитационной модели. Рассмотрена задача маршрутизации сетевого трафика для фрагмента ячеистой сети городского масштаба, управляемой на основе технологий искусственного интеллекта. Представлена модель системы массового обслуживания для изучения процесса маршрутизации, а также обучения выбора маршрута. Имитационная модель фрагмента ячеистой сети разработана в пакете Anylogic и обучается на основе платформы Microsoft Bonsai. The results of the study of network traffic routing methods based on reinforcement learning using a simulation model are presented. The problem of network traffic routing for a fragment of a city-scale mesh network, controlled on the basis of artificial intelligence technologies, is considered. The article presents a queueing model for studying the routing process, as well as learning how to choose a route. The mesh network fragment simulation model was developed in the Anylogic package and is trained on the basis of the Microsoft Bonsai platform.

Editorial: Simulation, analysis and measurement of broadband network traffic

2009 ◽  
Vol 3 (6) ◽  
pp. 883 ◽  
Author(s):  
J. Schormans
Keyword(s):  
Network Traffic ◽  
Simulation Analysis ◽  
Broadband Network

scholarly journals A Credit-Based System for Traffic Routing in Support of Vehicular Networks

2021 ◽  
Vol 23 (2) ◽  
pp. 1-11
Author(s):  
Ammar Kamel ◽  
Maysaa Husam ◽  
Zaid Shafeeq Bakr ◽  
Ziad M. Abood
Keyword(s):  
Vehicular Networks ◽  
Mixed Strategy ◽  
Optimal Strategies ◽  
Network Routing ◽  
Theory Model ◽  
Traffic Network ◽  
Quantal Response ◽  
Network Graph ◽  
Traffic Routing ◽  
Consistent Performance

Network routing has a great impact on the efficiency and reliability of the traffic network system in a real-world scenario. To date, achieving network-consistent performance is the main goal of many traffic network research studies. In this research, a mixed strategy game-theory model for network routing is proposed that discovers the optimal strategies that can be adopted by network route players in a network graph. This model has been validated by measuring the model outcomes using quantal response equilibrium (QRE) technique, which explores the players' noisy decisions by comparing the utilized optimal strategies with Nash equilibrium. The experimental results demonstrate that there is an equilibrium with a mixed strategy of a given network.

scholarly journals Search algorithm for loopless routes

2020 ◽  
pp. 10-25
Author(s):  
Maksim Sergeevich Demichev ◽  
Konstantin Eduardovich Gaipov
Keyword(s):  
Network Topology ◽  
Network Traffic ◽  
Data Transmission ◽  
Search Algorithm ◽  
Heavy Traffic ◽  
Speed Reduction ◽  
C Language ◽  
Traffic Routing ◽  
Route Search ◽  
Formation Phase

The subject of this research is the search algorithm for loopless routes from transmitter to the recipient of network traffic in the conditions of a known network topology. In designing data transmission network, one of the primary problems is the formation of network traffic routing, due to the fact that heavy traffic often cause the occurrence of bottlenecks in form of the overloaded communication node, which results in speed reduction of data transmission. This article provides the search algorithm for loopless routes from transmitter to the recipient of network traffic; the result is presented as a set of loopless  routes in accordance with the specified network topology. The article also provides the software code of the algorithm written in the C# language, as well as the results of test solutions of the specified topologies. The algorithm was developed via experimental and theoretical methods, on the bases of the available route search algorithms, such as Floyd's algorithm and Dijkstra's algorithm, as well as mechanisms of static and dynamic routing, such as RIP, OSPF, and EIGRP. The novelty of this work consists in elaboration of search algorithm for loopless routes from transmitter to the recipient in the conditions of the available network topology; and in comparison of the acquired results with other methods of formation phase variables. This algorithm allows generating a list of all loopless routes within the indicated network topology between the pair of interacting nodes.

scholarly journals Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 241
Author(s):  
Qasem Abu Al-Haija ◽  
Ahmad Al-Badawi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Network Traffic ◽  
Kernel Methods ◽  
Ensemble Methods ◽  
Error Rates ◽  
Identification Accuracy ◽  
Traffic Routing ◽  
Network Intrusion ◽  
Network Methods

Network Intrusion Detection Systems (NIDSs) are indispensable defensive tools against various cyberattacks. Lightweight, multipurpose, and anomaly-based detection NIDSs employ several methods to build profiles for normal and malicious behaviors. In this paper, we design, implement, and evaluate the performance of machine-learning-based NIDS in IoT networks. Specifically, we study six supervised learning methods that belong to three different classes: (1) ensemble methods, (2) neural network methods, and (3) kernel methods. To evaluate the developed NIDSs, we use the distilled-Kitsune-2018 and NSL-KDD datasets, both consisting of a contemporary real-world IoT network traffic subjected to different network attacks. Standard performance evaluation metrics from the machine-learning literature are used to evaluate the identification accuracy, error rates, and inference speed. Our empirical analysis indicates that ensemble methods provide better accuracy and lower error rates compared with neural network and kernel methods. On the other hand, neural network methods provide the highest inference speed which proves their suitability for high-bandwidth networks. We also provide a comparison with state-of-the-art solutions and show that our best results are better than any prior art by 1~20%.

scholarly journals Simulation Analysis of the Market Diffusion Effects of Risk-averse Route Guidance on Network Traffic

2013 ◽  
Vol 19 ◽  
pp. 874-881 ◽  
Author(s):  
Kaoru Yamada ◽  
Jiangshan Ma ◽  
Daisuke Fukuda
Keyword(s):  
Network Traffic ◽  
Simulation Analysis ◽  
Route Guidance ◽  
Risk Averse ◽  
Diffusion Effects ◽  
Market Diffusion

A Simulation Analysis on the Existence of Network Traffic Flow Equilibria

2014 ◽  
Vol 15 (4) ◽  
pp. 1706-1713 ◽  
Author(s):  
Shu Lin ◽  
Qing-Jie Kong ◽  
Qingming Huang
Keyword(s):  
Traffic Flow ◽  
Network Traffic ◽  
Simulation Analysis
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