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Idioma: Inglés
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ISBN 10: 0578436310 ISBN 13: 9780578436319
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Añadir al carritoTaschenbuch. Condición: Neu. Zhang, Wendy (Weiting) Ilustrador. Neuware.
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Publicado por Springer Nature Switzerland AG, Cham, 2026
ISBN 10: 3032076668 ISBN 13: 9783032076663
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Añadir al carritoHardcover. Condición: new. Hardcover. This book investigates technologies that enable more powerful resources and improve resource utilization for end-edge-cloud networks. The authors cover tools such as federated learning (FL) and real-time inference in industrial IoT and they present a novel communication and computation integration architecture for end-edge-cloud networks. Under the considered end-edge-cloud network architecture, the authors then propose different resource scheduling schemes based on centralized and distributed deep reinforcement learning methods to improve overall resource utilization for guaranteeing the diversified quality of service (QoS) requirements from different applications. The proposed architecture and schemes can not only be adopted in future end-edge-cloud networks to efficiently manage the multi-dimensional resources in real time, but also provide useful guidelines for multi-dimensional resource scheduling scheme designing and resource utilization enhancement in complex end-edge-cloud networks with diversified data services and applications. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book investigates technologies that enable more powerful resources and improve resource utilization for end-edge-cloud networks. The authors cover tools such as federated learning (FL) and real-time inference in industrial IoT and they present a novel communication and computation integration architecture for end-edge-cloud networks. Under the considered end-edge-cloud network architecture, the authors then propose different resource scheduling schemes based on centralized and distributed deep reinforcement learning methods to improve overall resource utilization for guaranteeing the diversified quality of service (QoS) requirements from different applications. The proposed architecture and schemes can not only be adopted in future end-edge-cloud networks to efficiently manage the multi-dimensional resources in real time, but also provide useful guidelines for multi-dimensional resource scheduling scheme designing and resource utilization enhancement in complex end-edge-cloud networks with diversified data services and applications.
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Añadir al carritoHardcover. Condición: Brand New. 180 pages. 9.25x6.10x9.21 inches. In Stock.
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Añadir al carritoPaperback / softback. Condición: New. Zhang, Wendy (Weiting) Ilustrador. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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Añadir al carritoHardcover. Condición: new. Hardcover. Mobile crowdsensing (MCS) has emerged as a pivotal element in contemporary communication technology, witnessing substantial growth recently. The advent of 5G, the Internet of Things (IoT), and edge computing has propelled MCS researchers to achieve enhanced sensing efficiency and broaden its application spectrum across various domains such as environmental monitoring, traffic management, and healthcare. However, despite these advantages, MCS confronts significant security and privacy challenges due to its open and diverse nature. Critical concerns encompass data leakage, unauthorized access, data tampering, and cross-network attacks. These issues can severely compromise the stability, privacy, and security of MCS systems. Furthermore, the dynamic mobility of users and devices within MCS introduces additional complexity to conventional security measures, particularly concerning communication and cross-domain access control. To tackle these challenges, researchers have devised several strategies aimed at bolstering the security and privacy of MCS systems. These novel protection mechanisms offer distinct benefits over traditional approaches. They are capable of securing data even with constrained computational and communication resources, enhancing system flexibility, and effectively thwarting sophisticated cyberattacks. These strategies provide both theoretical and practical underpinnings for fortifying MCS security and lay a robust foundation for the field's future evolution. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Idioma: Inglés
Publicado por Springer Nature Switzerland AG, Cham, 2026
ISBN 10: 3032076668 ISBN 13: 9783032076663
Librería: AussieBookSeller, Truganina, VIC, Australia
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Añadir al carritoHardcover. Condición: new. Hardcover. This book investigates technologies that enable more powerful resources and improve resource utilization for end-edge-cloud networks. The authors cover tools such as federated learning (FL) and real-time inference in industrial IoT and they present a novel communication and computation integration architecture for end-edge-cloud networks. Under the considered end-edge-cloud network architecture, the authors then propose different resource scheduling schemes based on centralized and distributed deep reinforcement learning methods to improve overall resource utilization for guaranteeing the diversified quality of service (QoS) requirements from different applications. The proposed architecture and schemes can not only be adopted in future end-edge-cloud networks to efficiently manage the multi-dimensional resources in real time, but also provide useful guidelines for multi-dimensional resource scheduling scheme designing and resource utilization enhancement in complex end-edge-cloud networks with diversified data services and applications. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Idioma: Inglés
Publicado por Springer, Berlin, Springer, 2026
ISBN 10: 3032076668 ISBN 13: 9783032076663
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Añadir al carritoBuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book investigates technologies that enable more powerful resources and improve resource utilization for end-edge-cloud networks. The authors cover tools such as federated learning (FL) and real-time inference in industrial IoT and they present a novel communication and computation integration architecture for end-edge-cloud networks. Under the considered end-edge-cloud network architecture, the authors then propose different resource scheduling schemes based on centralized and distributed deep reinforcement learning methods to improve overall resource utilization for guaranteeing the diversified quality of service (QoS) requirements from different applications. The proposed architecture and schemes can not only be adopted in future end-edge-cloud networks to efficiently manage the multi-dimensional resources in real time, but also provide useful guidelines for multi-dimensional resource scheduling scheme designing and resource utilization enhancement in complex end-edge-cloud networks with diversified data services and applications. 145 pp. Englisch.
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
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Añadir al carritoBuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.
Idioma: Inglés
Publicado por Springer Nature Switzerland AG, Cham, 2026
ISBN 10: 3032076668 ISBN 13: 9783032076663
Librería: CitiRetail, Stevenage, Reino Unido
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Añadir al carritoHardcover. Condición: new. Hardcover. This book investigates technologies that enable more powerful resources and improve resource utilization for end-edge-cloud networks. The authors cover tools such as federated learning (FL) and real-time inference in industrial IoT and they present a novel communication and computation integration architecture for end-edge-cloud networks. Under the considered end-edge-cloud network architecture, the authors then propose different resource scheduling schemes based on centralized and distributed deep reinforcement learning methods to improve overall resource utilization for guaranteeing the diversified quality of service (QoS) requirements from different applications. The proposed architecture and schemes can not only be adopted in future end-edge-cloud networks to efficiently manage the multi-dimensional resources in real time, but also provide useful guidelines for multi-dimensional resource scheduling scheme designing and resource utilization enhancement in complex end-edge-cloud networks with diversified data services and applications. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Librería: Majestic Books, Hounslow, Reino Unido
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Añadir al carritoCondición: New. Print on Demand.
Idioma: Inglés
Publicado por Springer, Springer Jan 2026, 2026
ISBN 10: 3032076668 ISBN 13: 9783032076663
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 149,79
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Añadir al carritoBuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book investigates technologies that enable more powerful resources and improve resource utilization for end-edge-cloud networks. The authors cover tools such as federated learning (FL) and real-time inference in industrial IoT and they present a novel communication and computation integration architecture for end-edge-cloud networks. Under the considered end-edge-cloud network architecture, the authors then propose different resource scheduling schemes based on centralized and distributed deep reinforcement learning methods to improve overall resource utilization for guaranteeing the diversified quality of service (QoS) requirements from different applications. The proposed architecture and schemes can not only be adopted in future end-edge-cloud networks to efficiently manage the multi-dimensional resources in real time, but also provide useful guidelines for multi-dimensional resource scheduling scheme designing and resource utilization enhancement in complex end-edge-cloud networks with diversified data services and applications.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 160 pp. Englisch.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Añadir al carritoBuch. Condición: Neu. Data Privacy and Cybersecurity in Mobile Crowdsensing | Buch | Englisch | 2025 | MDPI AG | EAN 9783725845408 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.