scholarly journals A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks

2015 ◽  
Vol 11 (3) ◽  
pp. 374391 ◽  
Author(s):  
Wei Kuang Lai ◽  
Mei-Tso Lin ◽  
Yu-Hsuan Yang
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Ad Hoc Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Learning System ◽  
Hoc Networks

APPLICATION OF ROBUST M-ESTIMATIONS FOR MACHINE LEARNING IN VANETs

2020 ◽  
Author(s):  
А.Р. Абделлах ◽  
А. Мутханна ◽  
А.Е. Кучерявый
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Ad Hoc Networks ◽  
Communication Systems ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Telecommunication Networks ◽  
Artificial Intelligence Techniques ◽  
Hoc Networks

Исследования в области сетей и систем связи пятого и последующих поколений требуют применения новых технологических решений. Представлены методы искусственного интеллекта, которые в последнее время все чаще используются при решении разнообразных задач в области сетей и систем связи. Предлагается и исследуется эффективность применения робастных М-оценок для машинного обучения в сетях транспортных средств VANET (Vehicular Ad Hoc Networks). Investigations in the field of telecommunication networks and systems of the fifth and beyond generations require the use of new technological solutions. Artificial intelligence techniques, which have recently been increasingly used in solving various problems in the field of networks and communication systems, are presented. The paper proposes and investigates the effectiveness of applying robust M-estimations for machine learning in vehicular ad hoc networks (VANET).

Analysis on Fog Computing Enabled Vehicular Ad hoc Networks

2019 ◽  
Vol 16 (10) ◽  
pp. 4356-4361 ◽  
Author(s):  
Seema Gaba ◽  
Kavita ◽  
Sahil Verma
Keyword(s):  
Decision Making ◽  
Ad Hoc Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Fog Computing ◽  
Computing Time ◽  
Vehicular Network ◽  
The Other ◽  
Localization System ◽  
Hoc Networks

The vehicular ad-hoc networks make a simple case of networks which are supposed to be very smart because of the tasks and crucial decision making they have to carry out. Since they are on the move, the transmission and reception of information has to be quick in order to make the networks efficient in term of computing time. Fog enabled VANET make the systems more capable by processing much of the information locally and only sending crucial decision making to the cloud which saves time and worth for all sub dependent systems. In this work we have reviewed the two fog enabled VANET schemes, one is SIVNFC (Secure intelligent vehicular network using fog computing) and the other is SOLVE (localization system frameworks).

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