Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204725432 ISBN 13: 9786204725437
Librería: moluna, Greven, Alemania
EUR 50,66
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Añadir al carritoCondición: New.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204725432 ISBN 13: 9786204725437
Librería: preigu, Osnabrück, Alemania
EUR 53,25
Cantidad disponible: 5 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. Develop Mobility Management Techniques in Mobile Cloud Computing | For Next Generation Networks | Lanke Pallavi (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204725437 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing Nov 2021, 2021
ISBN 10: 6204725432 ISBN 13: 9786204725437
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 61,90
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Recently, mobile devices are becoming the primary platforms for every user who always roam around and access the cloud computing applications. Mobile cloud computing (MCC) combines the both mobile and cloud computing, which provides optimal services to the mobile users. In next-generation mobile environments, mainly due to the huge number of mobile users in conjunction with the small cell size and their portable information, the influence of mobility on the network performance is strengthened. In this, we propose an energy efficient mobility management in mobile cloud computing (E2M2MC2) system for 5G heterogeneous networks. In this book, the major improvement in proposed E2M2MC2 system was proposed ERMO2 (Elective Repeat Multi-Objective Optimization) Algorithm BTS (BackTrack Searching) Algorithm and CHS (Cluster Head Selection) Algorithm, and demonstrated all results by comparing with few existing known algorithms for showing better improvement of our proposed approaches. 152 pp. Englisch.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing Nov 2021, 2021
ISBN 10: 6204725432 ISBN 13: 9786204725437
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 61,90
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Recently, mobile devices are becoming the primary platforms for every user who always roam around and access the cloud computing applications. Mobile cloud computing (MCC) combines the both mobile and cloud computing, which provides optimal services to the mobile users. In next-generation mobile environments, mainly due to the huge number of mobile users in conjunction with the small cell size and their portable information, the influence of mobility on the network performance is strengthened. In this, we propose an energy efficient mobility management in mobile cloud computing (E2M2MC2) system for 5G heterogeneous networks. In this book, the major improvement in proposed E2M2MC2 system was proposed ERMO2 (Elective Repeat Multi-Objective Optimization) Algorithm BTS (BackTrack Searching) Algorithm and CHS (Cluster Head Selection) Algorithm, and demonstrated all results by comparing with few existing known algorithms for showing better improvement of our proposed approaches.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 152 pp. Englisch.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204725432 ISBN 13: 9786204725437
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 62,64
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Recently, mobile devices are becoming the primary platforms for every user who always roam around and access the cloud computing applications. Mobile cloud computing (MCC) combines the both mobile and cloud computing, which provides optimal services to the mobile users. In next-generation mobile environments, mainly due to the huge number of mobile users in conjunction with the small cell size and their portable information, the influence of mobility on the network performance is strengthened. In this, we propose an energy efficient mobility management in mobile cloud computing (E2M2MC2) system for 5G heterogeneous networks. In this book, the major improvement in proposed E2M2MC2 system was proposed ERMO2 (Elective Repeat Multi-Objective Optimization) Algorithm BTS (BackTrack Searching) Algorithm and CHS (Cluster Head Selection) Algorithm, and demonstrated all results by comparing with few existing known algorithms for showing better improvement of our proposed approaches.