Mobile agent is a vehicle equipped with powerful transceiver and battery which finds the shortest path to gather data from the sensors and finally transports the data to the sink. The problem of minimizing the length of each data-gathering tour is mainly focused here. The mobile agent involves Bee Colony Optimization for travelling salesman problem. The Artificial Bee Colony algorithm (ABC) optimization is a population-based search algorithm which applies the concept of social interaction to problem solving. This algorithm is applied to the process of path planning problems for the mobile agents; it finds the shortest path for the mobile agents to collect the data from sensors as well as best in computation time. The effectiveness of the paths has been evaluated with the parameters such as tour length, agent travel time by Artificial Bee Colony Algorithm. This algorithm also prolong the lifetime of the sensors when compared to other existing approaches. Artificial Bee Colony algorithm has the advantages of strong robustness, fast convergence and high flexibility. This approach gives the best results for finding the shortest path in a shortest time.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Mobile agent is a vehicle equipped with powerful transceiver and battery which finds the shortest path to gather data from the sensors and finally transports the data to the sink. The problem of minimizing the length of each data-gathering tour is mainly focused here. The mobile agent involves Bee Colony Optimization for travelling salesman problem. The Artificial Bee Colony algorithm (ABC) optimization is a population-based search algorithm which applies the concept of social interaction to problem solving. This algorithm is applied to the process of path planning problems for the mobile agents; it finds the shortest path for the mobile agents to collect the data from sensors as well as best in computation time. The effectiveness of the paths has been evaluated with the parameters such as tour length, agent travel time by Artificial Bee Colony Algorithm. This algorithm also prolong the lifetime of the sensors when compared to other existing approaches. Artificial Bee Colony algorithm has the advantages of strong robustness, fast convergence and high flexibility. This approach gives the best results for finding the shortest path in a shortest time.
Dr. S. Ramesh, Faculty in the Department of Computer Science and Engineering at Anna University Regional Office, Madurai. Received his Ph.D in Information and Communication Engineering by 2015 from Anna University, Chennai.His research interests include Wireless Networks, Network Security, and Optimization Techniques.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Mobile agent is a vehicle equipped with powerful transceiver and battery which finds the shortest path to gather data from the sensors and finally transports the data to the sink. The problem of minimizing the length of each data-gathering tour is mainly focused here. The mobile agent involves Bee Colony Optimization for travelling salesman problem. The Artificial Bee Colony algorithm (ABC) optimization is a population-based search algorithm which applies the concept of social interaction to problem solving. This algorithm is applied to the process of path planning problems for the mobile agents; it finds the shortest path for the mobile agents to collect the data from sensors as well as best in computation time. The effectiveness of the paths has been evaluated with the parameters such as tour length, agent travel time by Artificial Bee Colony Algorithm. This algorithm also prolong the lifetime of the sensors when compared to other existing approaches. Artificial Bee Colony algorithm has the advantages of strong robustness, fast convergence and high flexibility. This approach gives the best results for finding the shortest path in a shortest time. 68 pp. Englisch. N° de réf. du vendeur 9783659786693
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ramesh S.Dr. S. Ramesh, Faculty in the Department of Computer Science and Engineering at Anna University Regional Office, Madurai. Received his Ph.D in Information and Communication Engineering by 2015 from Anna University, Chennai.H. N° de réf. du vendeur 158962483
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Mobile agent is a vehicle equipped with powerful transceiver and battery which finds the shortest path to gather data from the sensors and finally transports the data to the sink. The problem of minimizing the length of each data-gathering tour is mainly focused here. The mobile agent involves Bee Colony Optimization for travelling salesman problem. The Artificial Bee Colony algorithm (ABC) optimization is a population-based search algorithm which applies the concept of social interaction to problem solving. This algorithm is applied to the process of path planning problems for the mobile agents; it finds the shortest path for the mobile agents to collect the data from sensors as well as best in computation time. The effectiveness of the paths has been evaluated with the parameters such as tour length, agent travel time by Artificial Bee Colony Algorithm. This algorithm also prolong the lifetime of the sensors when compared to other existing approaches. Artificial Bee Colony algorithm has the advantages of strong robustness, fast convergence and high flexibility. This approach gives the best results for finding the shortest path in a shortest time.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 68 pp. Englisch. N° de réf. du vendeur 9783659786693
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Mobile agent is a vehicle equipped with powerful transceiver and battery which finds the shortest path to gather data from the sensors and finally transports the data to the sink. The problem of minimizing the length of each data-gathering tour is mainly focused here. The mobile agent involves Bee Colony Optimization for travelling salesman problem. The Artificial Bee Colony algorithm (ABC) optimization is a population-based search algorithm which applies the concept of social interaction to problem solving. This algorithm is applied to the process of path planning problems for the mobile agents; it finds the shortest path for the mobile agents to collect the data from sensors as well as best in computation time. The effectiveness of the paths has been evaluated with the parameters such as tour length, agent travel time by Artificial Bee Colony Algorithm. This algorithm also prolong the lifetime of the sensors when compared to other existing approaches. Artificial Bee Colony algorithm has the advantages of strong robustness, fast convergence and high flexibility. This approach gives the best results for finding the shortest path in a shortest time. N° de réf. du vendeur 9783659786693
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Taschenbuch. Etat : Neu. Mobile Agent for Data Gathering in Wireless Sensor Networks | Research Perspective | S. Ramesh (u. a.) | Taschenbuch | 68 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659786693 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 104169035
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