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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Green Internet of Things for Smart World
TL;DR: Various technologies and issues regarding green IoT, which further reduces the energy consumption of IoT are discussed, and the latest developments and future vision about sensor cloud are reviewed and introduced.
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Recent Advances in Industrial Wireless Sensor Networks Toward Efficient Management in IoT
TL;DR: The key approach to enable efficient and reliable management of WSN within an infrastructure supporting various WSN applications and services is a cross-layer design of lightweight and cloud-based RESTful Web service.
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A Multi-Objective Optimization Scheduling Method Based on the Ant Colony Algorithm in Cloud Computing
TL;DR: Experimental results show that based on these four metrics, a multi-objective optimization method is better than other similar methods, especially as it increased 56.6% in the best case scenario.
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Real-Time Lateral Movement Detection Based on Evidence Reasoning Network for Edge Computing Environment
Zhihong Tian,Wei Shi,Yuhang Wang,Chunsheng Zhu,Xiaojiang Du,Shen Su,Yanbin Sun,Nadra Guizani +7 more
TL;DR: In this paper, the authors proposed a real-time lateral movement detection method, named CloudSEC, based on an evidence reasoning network for the edge-cloud environment, where the concept of vulnerability correlation is introduced.
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An Incremental CFS Algorithm for Clustering Large Data in Industrial Internet of Things
TL;DR: An incremental clustering algorithm by fast finding and searching of density peaks based on k-mediods is proposed in this paper and validated on three popular UCI datasets and two real datasets collected from industrial Internet of Things in terms of clustering accuracy and computational time.