Genetic Algorithms for Topology Control Problems: Design and Performance Analysis of Genetic Algorithms for Topology Control Problems

 
9783844309027: Genetic Algorithms for Topology Control Problems: Design and Performance Analysis of Genetic Algorithms for Topology Control Problems

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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About the Author:

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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Book Description LAP Lambert Academic Publishing, Germany, 2011. Paperback. Book Condition: New. 220 x 150 mm. Language: English . Brand New Book ***** Print on Demand *****.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. Bookseller Inventory # AAV9783844309027

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Book Description LAP Lambert Academic Publishing. Paperback. Book Condition: New. Paperback. 136 pages. Dimensions: 8.7in. x 5.9in. x 0.3in.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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Bookseller Inventory # 9783844309027

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