High reliable disjoint path set selection in mobile ad-hoc network using Hopfield neural network

2011 ◽  
Vol 5 (11) ◽  
pp. 1566-1576 ◽  
Author(s):  
M. Sheikhan ◽  
E. Hemmati
Keyword(s):  
Neural Network ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Hopfield Neural Network ◽  
Disjoint Path ◽  
Mobile Ad Hoc

Mobile ad hoc network proactive routing with delay prediction using neural network

2009 ◽  
Vol 16 (6) ◽  
pp. 1601-1620 ◽  
Author(s):  
Zhihao Guo ◽  
Shaya Sheikh ◽  
Camelia Al-Najjar ◽  
Hyun Kim ◽  
Behnam Malakooti
Keyword(s):  
Neural Network ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Proactive Routing ◽  
Mobile Ad Hoc ◽  
Delay Prediction

Cognition in a Cognitive Routing System for Mobile Ad-Hoc Network through Leaning Automata and Neural Network

2013 ◽  
Vol 421 ◽  
pp. 694-700
Author(s):  
Muhammad Ishaq Afridi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Ad Hoc Network ◽  
Network Performance ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Learning Automata ◽  
Learning System ◽  
Mobile Ad Hoc ◽  
Hoc Networks

A cognitive routing system intelligently selects one protocol at a time for specific routing conditions and environment in MANET. Cognition or self-learning can be achieved in a cognitive routing system for mobile ad-hoc network (MANET) through a learning system like learning automata or neural networks. This article covers the application of learning automata and neural network to achieve cognition in MANET routing system. Mobile Ad-hoc networks are dynamic in nature and lack any fixed infrastructure, so the implementation of cognition enhances the performance of overall routing system in these networks. In learning automata the process of learning is different from reasoning or decision making. Learning automata require little knowledge to take decisions. Neural network can be improved by increasing the number of neurons and changing parameters. Self-training enhance neural network performance and it select suitable protocol for a given network environment. Cognition in MANET is either based upon learning automata as in some wireless sensor networks or specialized cognitive neural networks like Elman network. Learning automata do not follow predetermine rules and has the ability to learn and evolve. The interaction of learning automata with the MANET environment results in the evolution of cognition system.

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