Articles liés à Enhanced Routing in Wireless Network using Ant Colony...

Enhanced Routing in Wireless Network using Ant Colony Optmization: Wireless Network - Couverture souple

Sharma, Priyanka; Siddarth Khasgiwala, Mr; K Kotecha, Dr

 
9783838398952: Enhanced Routing in Wireless Network using Ant Colony Optmization: Wireless Network
Couverture souple
ISBN 10 : 3838398955 ISBN 13 : 9783838398952
Editeur : LAP LAMBERT Academic Publishing, 2010

Synopsis

Multimedia applications requires QoS based routing methodologies. Routing is an NP Hard Problem. Hence Artificial Intelligence techniques will be required to solve such problems. Ant Colony Optimization can be used to enhance routing using multiple QoS parameters. ACO is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Each ant is an autonomous agent that constructs a path. There might be one or more ants concurrently active at the same time. Ants do not need synchronization. Forward ants move to good looking neighbor from the current node probabilistically. Probabilistic choice is biased by pheromone trails previously deposited and heuristic function. Without heuristics information, algorithms tend to converge towards initial random solution. In backward mode, ants lay down pheromone. In ACO pheromone is added only to arcs belonging to the global best solution. Pheromone intensity of all the paths decreases with time, called pheromone evaporation. It helps in unlearning poor quality solution. After some time, the shortest path has the highest probability.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Multimedia applications requires QoS based routing methodologies. Routing is an NP Hard Problem. Hence Artificial Intelligence techniques will be required to solve such problems. Ant Colony Optimization can be used to enhance routing using multiple QoS parameters. ACO is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Each ant is an autonomous agent that constructs a path. There might be one or more ants concurrently active at the same time. Ants do not need synchronization. Forward ants move to good looking neighbor from the current node probabilistically. Probabilistic choice is biased by pheromone trails previously deposited and heuristic function. Without heuristics information, algorithms tend to converge towards initial random solution. In backward mode, ants lay down pheromone. In ACO pheromone is added only to arcs belonging to the global best solution. Pheromone intensity of all the paths decreases with time, called pheromone evaporation. It helps in unlearning poor quality solution. After some time, the shortest path has the highest probability.

Biographie de l'auteur

Prof Priyanka Sharma is working as a Section Head for MTech ICT department at Institute of Technology, Nirma University. Currently she is doing her Phd in the area of Multimedia Networks and Ant COlony Optmization

Prof Priyanka Sharma is working as a Section Head for MTech ICT department at Institute of Technology, Nirma University. Currently she is doing her Phd in the area of Multimedia Networks and Ant COlony Optmization

Prof Priyanka Sharma is working as a Section Head for MTech ICT department at Institute of Technology, Nirma University. Currently she is doing her Phd in the area of Multimedia Networks and Ant COlony Optmization

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2010
Langue
anglais
ISBN 10 
3838398955
ISBN 13 
9783838398952
Reliure
Broché
Nombre de pages
104
Coordonnées du fabricant
non disponible
Personne responsable
KreGa Onlinehandel GmbH
4950000000000
info@krega-onlinehandel.de

Birkenweg 31
Brakel
33034
Allemagne