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Genetic Algorithms for Topology Control Problems: Design and Performance Analysis of Genetic Algorithms for Topology Control Problems - Tapa blanda
Sinopsis
This book present a bio-inspired topology control mechanism, force-based genetic algorithm (FGA), where a GA is run by each mobile node to achieve a uniform spread of nodes and to provide a fully connected network over an unknown area. We provide a formal analysis of FGA in terms of convergence speed, uniformity at area coverage, and Lyapunov stability theorem. This work also emphasizes the use of nodes to achieve a uniform distribution over an unknown terrain without a priori information. In contrast, each node running our FGA has to make its own movement direction and speed decisions based on local neighborhood information, such as obstacles and the number of neighbors, without a centralized control unit or global knowledge. The stochastic behavior of FGA makes it difficult to analyze its convergence speed. We built metrically transitive homogeneous and inhomogeneous Markov chain models to analyze the convergence of our FGA with respect to the communication ranges of mobile nodes and the total number of nodes in the system. The Dobrushin contraction coefficient of ergodicity is used for measuring convergence speed for the Markov chain models of our FGA.
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Reseña del editor
This book present a bio-inspired topology control mechanism, force-based genetic algorithm (FGA), where a GA is run by each mobile node to achieve a uniform spread of nodes and to provide a fully connected network over an unknown area. We provide a formal analysis of FGA in terms of convergence speed, uniformity at area coverage, and Lyapunov stability theorem. This work also emphasizes the use of nodes to achieve a uniform distribution over an unknown terrain without a priori information. In contrast, each node running our FGA has to make its own movement direction and speed decisions based on local neighborhood information, such as obstacles and the number of neighbors, without a centralized control unit or global knowledge. The stochastic behavior of FGA makes it difficult to analyze its convergence speed. We built metrically transitive homogeneous and inhomogeneous Markov chain models to analyze the convergence of our FGA with respect to the communication ranges of mobile nodes and the total number of nodes in the system. The Dobrushin contraction coefficient of ergodicity is used for measuring convergence speed for the Markov chain models of our FGA.
Biografía del autor
Cem ?afak ?ahin, Ph.D.: received his BS degree from Gazi University, Turkey in 1996, MS degree from Middle East Technical University, Turkey in 2000, and MPhil. and Ph.D. degrees from the City University of New York, USA in 2010, all in Electrical Engineering. He is currently with the Strategic Analysis and Testing team at American Express.
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Genetic Algorithms for Topology Control Problems
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LAP LAMBERT Academic Publishing Feb 2011, 2011
ISBN 10: 3844309020
ISBN 13: 9783844309027
Nuevo
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book present a bio-inspired topology control mechanism, force-based genetic algorithm (FGA), where a GA is run by each mobile node to achieve a uniform spread of nodes and to provide a fully connected network over an unknown area. We provide a formal analysis of FGA in terms of convergence speed, uniformity at area coverage, and Lyapunov stability theorem. This work also emphasizes the use of nodes to achieve a uniform distribution over an unknown terrain without a priori information. In contrast, each node running our FGA has to make its own movement direction and speed decisions based on local neighborhood information, such as obstacles and the number of neighbors, without a centralized control unit or global knowledge. The stochastic behavior of FGA makes it difficult to analyze its convergence speed. We built metrically transitive homogeneous and inhomogeneous Markov chain models to analyze the convergence of our FGA with respect to the communication ranges of mobile nodes and the total number of nodes in the system. The Dobrushin contraction coefficient of ergodicity is used for measuring convergence speed for the Markov chain models of our FGA. 136 pp. Englisch. Nº de ref. del artículo: 9783844309027
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Genetic Algorithms for Topology Control Problems
Publicado por
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3844309020
ISBN 13: 9783844309027
Nuevo
Tapa blanda
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sahin CemCem Safak Sahin, Ph.D.: received his BS degree from Gazi University, Turkey in 1996, MS degree from Middle East Technical University, Turkey in 2000, and MPhil. and Ph.D. degrees from the City University of New York, USA . Nº de ref. del artículo: 5471385
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Genetic Algorithms for Topology Control Problems
Publicado por
LAP LAMBERT Academic Publishing Feb 2011, 2011
ISBN 10: 3844309020
ISBN 13: 9783844309027
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Neuware -This book present a bio-inspired topology control mechanism, force-based genetic algorithm (FGA), where a GA is run by each mobile node to achieve a uniform spread of nodes and to provide a fully connected network over an unknown area. We provide a formal analysis of FGA in terms of convergence speed, uniformity at area coverage, and Lyapunov stability theorem. This work also emphasizes the use of nodes to achieve a uniform distribution over an unknown terrain without a priori information. In contrast, each node running our FGA has to make its own movement direction and speed decisions based on local neighborhood information, such as obstacles and the number of neighbors, without a centralized control unit or global knowledge. The stochastic behavior of FGA makes it difficult to analyze its convergence speed. We built metrically transitive homogeneous and inhomogeneous Markov chain models to analyze the convergence of our FGA with respect to the communication ranges of mobile nodes and the total number of nodes in the system. The Dobrushin contraction coefficient of ergodicity is used for measuring convergence speed for the Markov chain models of our FGA.Books on Demand GmbH, Überseering 33, 22297 Hamburg 136 pp. Englisch. Nº de ref. del artículo: 9783844309027
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Genetic Algorithms for Topology Control Problems : Design and Performance Analysis of Genetic Algorithms for Topology Control Problems
Publicado por
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3844309020
ISBN 13: 9783844309027
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book present a bio-inspired topology control mechanism, force-based genetic algorithm (FGA), where a GA is run by each mobile node to achieve a uniform spread of nodes and to provide a fully connected network over an unknown area. We provide a formal analysis of FGA in terms of convergence speed, uniformity at area coverage, and Lyapunov stability theorem. This work also emphasizes the use of nodes to achieve a uniform distribution over an unknown terrain without a priori information. In contrast, each node running our FGA has to make its own movement direction and speed decisions based on local neighborhood information, such as obstacles and the number of neighbors, without a centralized control unit or global knowledge. The stochastic behavior of FGA makes it difficult to analyze its convergence speed. We built metrically transitive homogeneous and inhomogeneous Markov chain models to analyze the convergence of our FGA with respect to the communication ranges of mobile nodes and the total number of nodes in the system. The Dobrushin contraction coefficient of ergodicity is used for measuring convergence speed for the Markov chain models of our FGA. Nº de ref. del artículo: 9783844309027
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Genetic Algorithms for Topology Control Problems: Design and Performance Analysis of Genetic Algorithms for Topology Control Problems
Publicado por
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3844309020
ISBN 13: 9783844309027
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Librería: Mispah books, Redhill, SURRE, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
Paperback. Condición: Like New. Like New. book. Nº de ref. del artículo: ERICA75838443090206
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