Hongke Zhang – författare

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 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 book constitutes the refereed proceedings of the Third International Conference on Mobile Ad-hoc and Sensor Networks, MSN 2007, held in Beijing, China, in December 2007. The papers address all current issues in mobile ad hoc and sensor networks and are organized in topical sections on routing, network protocols, energy efficiency, data processing, self-organization and synchronization, deployment and application, as well as security.

This book introduces a promising design for future Internet, the Smart Collaborative Identifier NETwork (SINET). By examining cutting-edge research from around the world, it is the first book to provide a comprehensive survey of SINET, including its basic theories and principles, a broad range of architectures, protocols, standards, and future research directions. For further investigation, the book also provides readers an experimental analysis of SINET to promote further, independent research. The second part of the book presents in detail key technologies in SINET such as scalable routing, efficient mapping systems, mobility management and security issues. In turn, the last part presents various implementations of SINET, assessing its merits. The authors believe SINET will greatly benefit researchers involved in designing future Internet thanks to its high degree of flexibility, security, manageability, mobility support and efficient resource utilization.