Deliver more for less Energy | Exploring Metrics Prediction Methods and their Usage in Multi-objective Manet Proactive Routing

Zhihao Guo

ISBN 10: 3836493942 ISBN 13: 9783836493949

Edit� par VDM Verlag Dr. Müller, 2008

Neuf(s) Taschenbuch

Vendeur preigu, Osnabr�ck, Allemagne �valuation du vendeur 5 sur 5 �toiles

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Synopsis :

Most existing MANET Proactive routing protocols rely heavily on hopcount evaluation. Although this is simple and efficient, it sacrificesthe potential performance gains obtainable by considering dynamicnetwork conditions such as congestion levels and link stability.Remedies of this issue have been presented by considering variouspredictive dynamic routing metrics mostly related to bandwidth andenergy consumption. However, those predictions do not fully appreciatethe nonstationary nature of the dynamic routing metrics and possibleheterogeneous power consumption of the nodes. With these addedconcerns, in this work, in-depth investigation is conducted onprediction and evaluation of dynamic routing metrics including queuingdelay, energy cost, and link stability. Artificial neural network modelsand advanced statistical methods are used for prediction of thesemetrics. Queuing delay and energy cost are investigated individuallyand then evaluated compositively together with predicted link stability. Their effects on multiple routingobjectives are extensively simulated and analyzed.

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Titre : Deliver more for less Energy | Exploring ...
�diteur : VDM Verlag Dr. M�ller
Date d'�dition : 2008
Reliure : Taschenbuch
Etat : Neu

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Deliver more for less Energy : Exploring Metrics Prediction Methods and their Usage in Multi-objective Manet Proactive Routing

Zhihao Guo

Edit� par VDM Verlag Dr. M�ller, VDM Verlag Dr. M�ller E.K., 2008

ISBN 10 : 3836493942 ISBN 13 : 9783836493949

Neuf Taschenbuch

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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Most existing MANET Proactive routing protocols rely heavily on hopcount evaluation. Although this is simple and efficient, it sacrificesthe potential performance gains obtainable by considering dynamicnetwork conditions such as congestion levels and link stability.Remedies of this issue have been presented by considering variouspredictive dynamic routing metrics mostly related to bandwidth andenergy consumption. However, those predictions do not fully appreciatethe nonstationary nature of the dynamic routing metrics and possibleheterogeneous power consumption of the nodes. With these addedconcerns, in this work, in-depth investigation is conducted onprediction and evaluation of dynamic routing metrics including queuingdelay, energy cost, and link stability. Artificial neural network modelsand advanced statistical methods are used for prediction of thesemetrics. Queuing delay and energy cost are investigated individuallyand then evaluated compositively together with predicted link stability. Their effects on multiple routingobjectives are extensively simulated and analyzed. N� de r�f. du vendeur 9783836493949

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