Wentai Wu

926 total citations
38 papers, 529 citations indexed

About

Wentai Wu is a scholar working on Computer Networks and Communications, Information Systems and Artificial Intelligence. According to data from OpenAlex, Wentai Wu has authored 38 papers receiving a total of 529 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Computer Networks and Communications, 16 papers in Information Systems and 11 papers in Artificial Intelligence. Recurrent topics in Wentai Wu's work include IoT and Edge/Fog Computing (18 papers), Cloud Computing and Resource Management (14 papers) and Privacy-Preserving Technologies in Data (6 papers). Wentai Wu is often cited by papers focused on IoT and Edge/Fog Computing (18 papers), Cloud Computing and Resource Management (14 papers) and Privacy-Preserving Technologies in Data (6 papers). Wentai Wu collaborates with scholars based in China, United Kingdom and United States. Wentai Wu's co-authors include Weiwei Lin, Ligang He, Ching‐Hsien Hsu, Keqin Li, Rui Mao, James Z. Wang, Zhiping Peng, Carsten Maple, Haoyu Wang and Bo Liu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, ACM Computing Surveys and IEEE Transactions on Industrial Informatics.

In The Last Decade

Wentai Wu

32 papers receiving 511 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Wentai Wu China	13	326	276	200	82	33	38	529
Lewis Tseng United States	9	322 1.0×	251 0.9×	217 1.1×	173 2.1×	21 0.6×	54	580
Weichao Gao United States	9	282 0.9×	212 0.8×	144 0.7×	101 1.2×	43 1.3×	21	491
Junyan Qian China	12	204 0.6×	341 1.2×	169 0.8×	57 0.7×	32 1.0×	67	565
Inshil Doh South Korea	11	296 0.9×	141 0.5×	197 1.0×	139 1.7×	38 1.2×	59	535
Shucun Fu China	6	365 1.1×	244 0.9×	114 0.6×	124 1.5×	20 0.6×	13	515
D. Janakiram India	12	384 1.2×	202 0.7×	151 0.8×	53 0.6×	47 1.4×	59	526
Darshan Vishwasrao Medhane India	8	242 0.7×	195 0.7×	166 0.8×	82 1.0×	62 1.9×	15	492
MingJian Tang Australia	11	192 0.6×	183 0.7×	259 1.3×	80 1.0×	84 2.5×	26	503

Countries citing papers authored by Wentai Wu

Since Specialization

Citations

This map shows the geographic impact of Wentai Wu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Wentai Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wentai Wu more than expected).

Fields of papers citing papers by Wentai Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wentai Wu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Wentai Wu. The network helps show where Wentai Wu may publish in the future.

Co-authorship network of co-authors of Wentai Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Wentai Wu. A scholar is included among the top collaborators of Wentai Wu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Wentai Wu. Wentai Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Li, Shenghai, et al.. (2026). Revisiting workflow scheduling with the power of edge computing: Taxonomy, review, and open challenges. Computer Science Review. 60. 100887–100887.

Lin, Weiwei, et al.. (2025). SegRNN: Segment Recurrent Neural Network for Long-Term Time-Series Forecasting. IEEE Internet of Things Journal. 13(5). 9861–9871.

Lin, Weiwei, et al.. (2025). On Efficiency, Fairness and Security in AI Accelerator Resource Sharing: A Survey. ACM Computing Surveys. 57(9). 1–35.

Lin, Weiwei, et al.. (2025). Cacomp: A Cloud-Assisted Collaborative Deep Learning Compiler Framework for DNN Tasks on Edge. IEEE Transactions on Computers. 74(8). 2663–2674.

Lin, Weiwei, et al.. (2025). SynFlowFL: A Dynamic Synaptic Flow Framework for Efficient, Personalized Federated Learning. IEEE Transactions on Emerging Topics in Computational Intelligence. 9(5). 3426–3440.

Lin, Weiwei, et al.. (2025). SparseTSF: Lightweight and Robust Time Series Forecasting via Sparse Modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence. 48(1). 170–183. 3 indexed citations

Liu, Guozhi, et al.. (2025). AdaptiveFL: Communication-Adaptive Federated Learning Under Dynamic Bandwidth. IEEE Transactions on Neural Networks and Learning Systems. 36(9). 17199–17211. 1 indexed citations

Lin, Weiwei, et al.. (2025). Reinforcement learning-based task scheduling for heterogeneous computing in end-edge-cloud environment. Cluster Computing. 28(3). 7 indexed citations

Peng, Peng, Weiwei Lin, Wentai Wu, et al.. (2024). A survey on computation offloading in edge systems: From the perspective of deep reinforcement learning approaches. Computer Science Review. 53. 100656–100656. 15 indexed citations

10.

Wang, Zirui, et al.. (2024). Generative data augmentation with differential privacy for non-IID problem in decentralized clinical machine learning. Future Generation Computer Systems. 160. 171–184. 4 indexed citations

11.

Lin, Weiwei, et al.. (2024). PETformer: Long-Term Time Series Forecasting via Placeholder-Enhanced Transformer. IEEE Transactions on Emerging Topics in Computational Intelligence. 9(2). 1189–1201. 10 indexed citations

12.

Peng, Peng, et al.. (2024). Reliable Task Offloading in Sustainable Edge Computing with Imperfect Channel State Information. IEEE Transactions on Network and Service Management. 21(6). 6423–6436. 1 indexed citations

13.

Li, Zhetao, et al.. (2024). Pattern-Sensitive Local Differential Privacy for Finite-Range Time-Series Data in Mobile Crowdsensing. IEEE Transactions on Mobile Computing. 24(1). 1–14. 3 indexed citations

14.

Lin, Weiwei, et al.. (2023). HybridAD: A Hybrid Model-Driven Anomaly Detection Approach for Multivariate Time Series. IEEE Transactions on Emerging Topics in Computational Intelligence. 8(1). 866–878. 5 indexed citations

15.

Lin, Weiwei, et al.. (2022). Evolving Deep Multiple Kernel Learning Networks Through Genetic Algorithms. IEEE Transactions on Industrial Informatics. 19(2). 1569–1580. 4 indexed citations

16.

Wu, Wentai, et al.. (2020). Developing an Unsupervised Real-Time Anomaly Detection Scheme for Time Series With Multi-Seasonality. IEEE Transactions on Knowledge and Data Engineering. 34(9). 4147–4160. 40 indexed citations

17.

Lin, Weiwei, Wentai Wu, & Ligang He. (2019). An On-Line Virtual Machine Consolidation Strategy for Dual Improvement in Performance and Energy Conservation of Server Clusters in Cloud Data Centers. IEEE Transactions on Services Computing. 15(2). 766–777. 46 indexed citations

18.

Wu, Wentai, Ligang He, & Weiwei Lin. (2019). Local Trend Inconsistency: A Prediction-driven Approach to Unsupervised Anomaly Detection in Multi-seasonal Time Series.. arXiv (Cornell University). 2 indexed citations

19.

Wu, Wentai, et al.. (2019). A Power Consumption Model for Cloud Servers Based on Elman Neural Network. IEEE Transactions on Cloud Computing. 9(4). 1268–1277. 29 indexed citations

20.

Lin, Weiwei, Wentai Wu, Haoyu Wang, James Z. Wang, & Ching‐Hsien Hsu. (2016). Experimental and quantitative analysis of server power model for cloud data centers. Future Generation Computer Systems. 86. 940–950. 32 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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