S
Shengli Xie
Researcher at Guangdong University of Technology
Publications - 344
Citations - 12728
Shengli Xie is an academic researcher from Guangdong University of Technology. The author has contributed to research in topics: Computer science & Blind signal separation. The author has an hindex of 52, co-authored 298 publications receiving 9021 citations. Previous affiliations of Shengli Xie include South China University of Technology.
Papers
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Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks
TL;DR: A reputation-based data sharing scheme to ensure high-quality data sharing among vehicles and a consortium blockchain and smart contract technologies to achieve secure data storage and sharing in vehicular edge networks.
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Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory
TL;DR: This article introduces reputation as the metric to measure the reliability and trustworthiness of the mobile devices, then designs a reputation-based worker selection scheme for reliable federated learning by using a multiweight subjective logic model and leverages the blockchain to achieve secure reputation management for workers with nonrepudiation and tamper-resistance properties.
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Home M2M networks: Architectures, standards, and QoS improvement
TL;DR: The architecture of home M2M networks decomposed into three subareas depending on the radio service ranges and potential applications is presented, and cross-layer joint admission and rate control design is reported for QoS-aware multimedia sharing.
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Cognitive machine-to-machine communications: visions and potentials for the smart grid
TL;DR: A CM2M communications architecture for the smart grid is presented, for which an energy-efficiency driven spectrum discovery scheme is presented and significant energy saving and the reliability in supporting data transmissions in thesmart grid are demonstrated.
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Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities
TL;DR: This article presents an efficient energy scheduling scheme with deep reinforcement learning for the proposed framework of an IoT-based energy management system based on edge computing infrastructure withDeep reinforcement learning.