C
Chunsheng Zhu
Researcher at Southern University of Science and Technology
Publications - 178
Citations - 5360
Chunsheng Zhu is an academic researcher from Southern University of Science and Technology. The author has contributed to research in topics: Wireless sensor network & Cloud computing. The author has an hindex of 33, co-authored 170 publications receiving 3981 citations. Previous affiliations of Chunsheng Zhu include University of British Columbia & Zhengzhou University.
Papers
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A survey on communication and data management issues in mobile sensor networks
TL;DR: Different research methods regarding communication and data management in MWSNs are discussed and some further open research areas in MW SNs are proposed.
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Mobile big data fault-tolerant processing for ehealth networks
TL;DR: An interests-based reduced variable neighborhood search (RVNS) queue architecture (IRQA) is proposed, a fault-tolerant mechanism based on interests matching is designed to ensure the completeness of eHealth data in the data gathering layer, and an RVNS queue is adopted for rapid data processing in theData analyzing layer.
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Trust-Based Communication for the Industrial Internet of Things
TL;DR: It is shown that trust-based communication can greatly enhance the performance of sensor- cloud and three types of trust- based communication mechanisms for sensor-cloud are proposed.
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Dynamic-Fusion-Based Federated Learning for COVID-19 Detection
Weishan Zhang,Tao Zhou,Qinghua Lu,Xiao Wang,Chunsheng Zhu,Haoyun Sun,Zhipeng Wang,Sin Kit Lo,Fei-Yue Wang +8 more
TL;DR: The proposed novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections is feasible and performs better than the default setting of federatedLearning in terms of model performance, communication efficiency, and fault tolerance.
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A Novel Sensory Data Processing Framework to Integrate Sensor Networks With Mobile Cloud
TL;DR: A novel sensory data processing framework is proposed, which aims at transmitting desirable sensory data to the mobile users in a fast, reliable, and secure manner and further decreases the storage and processing overhead of the cloud, while enabling mobile users to securely obtain their desired sensory data faster.