S
Song Guo
Researcher at Hong Kong Polytechnic University
Publications - 660
Citations - 17892
Song Guo is an academic researcher from Hong Kong Polytechnic University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 55, co-authored 578 publications receiving 12454 citations. Previous affiliations of Song Guo include Chongqing University of Posts and Telecommunications & Nanjing University of Posts and Telecommunications.
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Green Industrial Internet of Things Architecture: An Energy-Efficient Perspective
TL;DR: This article adopts an energy-efficient architecture for Industrial IoT, which consists of a sense entities domain, RESTful service hosted networks, a cloud server, and user applications, and a sleep scheduling and wake-up protocol, supporting the prediction of sleep intervals.
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Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System
TL;DR: A computation-efficient solution is proposed based on the formulation and validated by extensive simulation based studies to deal with the high computation complexity of fog computing supported software-defined embedded system.
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Information and Communications Technologies for Sustainable Development Goals: State-of-the-Art, Needs and Perspectives
TL;DR: There are essential and urgent needs to raise the awareness and call for attentions on how to innovate and energize ICTs in order to best assist all nations to achieve the SDGs by 2030.
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A Learning-Based Incentive Mechanism for Federated Learning
TL;DR: The incentive mechanism for federated learning to motivate edge nodes to contribute model training is studied and a deep reinforcement learning-based (DRL) incentive mechanism has been designed to determine the optimal pricing strategy for the parameter server and the optimal training strategies for edge nodes.
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Cost Efficient Resource Management in Fog Computing Supported Medical Cyber-Physical System
TL;DR: Fog computation and MCPS are integrated to build fog computing supported MCPS (FC-MCPS), and an LP-based two-phase heuristic algorithm is proposed that produces near optimal solution and significantly outperforms a greedy algorithm.