Ruiyun Yu

1.4k total citations
68 papers, 1.0k citations indexed

About

Ruiyun Yu is a scholar working on Computer Networks and Communications, Artificial Intelligence and Computer Science Applications. According to data from OpenAlex, Ruiyun Yu has authored 68 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Networks and Communications, 19 papers in Artificial Intelligence and 17 papers in Computer Science Applications. Recurrent topics in Ruiyun Yu's work include Mobile Crowdsensing and Crowdsourcing (17 papers), Human Mobility and Location-Based Analysis (10 papers) and Opportunistic and Delay-Tolerant Networks (9 papers). Ruiyun Yu is often cited by papers focused on Mobile Crowdsensing and Crowdsourcing (17 papers), Human Mobility and Location-Based Analysis (10 papers) and Opportunistic and Delay-Tolerant Networks (9 papers). Ruiyun Yu collaborates with scholars based in China, United States and Hong Kong. Ruiyun Yu's co-authors include Ann Move Oguti, Yonghe Liu, Junbo Wang, Zixue Cheng, Koichi Sato, Guangjie Han, Yu Yang, Chunhai Feng, Xingwei Wang and Bo Jin and has published in prestigious journals such as Applied Physics Letters, IEEE Access and Sensors.

In The Last Decade

Ruiyun Yu

63 papers receiving 970 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ruiyun Yu China 17 292 212 208 135 126 68 1.0k
Xuesong Xu China 17 216 0.7× 180 0.8× 190 0.9× 147 1.1× 99 0.8× 70 1.1k
Koji Zettsu Japan 14 230 0.8× 71 0.3× 245 1.2× 230 1.7× 227 1.8× 131 1.0k
Tuan Anh Nguyen Vietnam 16 341 1.2× 332 1.6× 189 0.9× 97 0.7× 112 0.9× 56 1.2k
Brian Dougherty United States 20 541 1.9× 484 2.3× 202 1.0× 307 2.3× 97 0.8× 60 1.4k
M. Rajasekhara Babu India 11 220 0.8× 255 1.2× 412 2.0× 166 1.2× 160 1.3× 65 980
Feng Qi China 19 308 1.1× 288 1.4× 721 3.5× 587 4.3× 104 0.8× 80 1.3k
Gabe Fierro United States 16 245 0.8× 229 1.1× 265 1.3× 131 1.0× 95 0.8× 52 1.1k
Raju Kannadasan India 22 167 0.6× 826 3.9× 172 0.8× 70 0.5× 73 0.6× 71 1.6k
Claudio Badii Italy 10 86 0.3× 163 0.8× 231 1.1× 106 0.8× 43 0.3× 18 814

Countries citing papers authored by Ruiyun Yu

Since Specialization
Citations

This map shows the geographic impact of Ruiyun Yu'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 Ruiyun Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ruiyun Yu more than expected).

Fields of papers citing papers by Ruiyun Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ruiyun Yu. 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 Ruiyun Yu. The network helps show where Ruiyun Yu may publish in the future.

Co-authorship network of co-authors of Ruiyun Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Ruiyun Yu. A scholar is included among the top collaborators of Ruiyun Yu 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 Ruiyun Yu. Ruiyun Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yu, Ruiyun, et al.. (2025). A multiscale process-aware retention network for fault prediction in mixed-model production. Computers in Industry. 170. 104313–104313.
2.
Yu, Ruiyun, et al.. (2025). Background-Weaken Generalization Network for Few-Shot Industrial Metal Defect Segmentation. IEEE Transactions on Instrumentation and Measurement. 74. 1–11.
3.
Wang, Pengfei, Guangjie Han, Ruiyun Yu, et al.. (2024). Decentralized Navigation With Heterogeneous Federated Reinforcement Learning for UAV-Enabled Mobile Edge Computing. IEEE Transactions on Mobile Computing. 23(12). 13621–13638. 11 indexed citations
4.
Yu, Ruiyun, et al.. (2024). Is multi-level data enhancement helpful for knowledge graph? A new perspective on multimodal fusion. Knowledge-Based Systems. 301. 112285–112285. 4 indexed citations
5.
Wang, Pengfei, et al.. (2024). Hypergraph-based Truth Discovery for Sparse Data in Mobile Crowdsensing. ACM Transactions on Sensor Networks. 20(3). 1–23. 4 indexed citations
6.
Wang, Xueyi, Xingwei Wang, Rongfei Zeng, et al.. (2024). Differentially Private and Truthful Reverse Auction With Dynamic Resource Provisioning for VNFI Procurement in NFV Markets. IEEE Transactions on Cloud Computing. 13(1). 259–272.
7.
Cao, Shuang, et al.. (2023). Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication. Frontiers in Neurorobotics. 16. 1076338–1076338. 4 indexed citations
8.
Yu, Ruiyun, Ann Move Oguti, Mohammad S. Obaidat, et al.. (2023). Blockchain-based solutions for mobile crowdsensing: A comprehensive survey. Computer Science Review. 50. 100589–100589. 6 indexed citations
9.
Lin, Fuyu, et al.. (2023). PAG-TSN: Ridership Demand Forecasting Model for Shared Travel Services of Smart Transportation. IEEE Transactions on Intelligent Transportation Systems. 24(12). 15876–15889. 6 indexed citations
10.
Wang, Xueyi, et al.. (2023). Truthful Auction-Based Resource Allocation Mechanisms With Flexible Task Offloading in Mobile Edge Computing. IEEE Transactions on Mobile Computing. 23(5). 6377–6391. 16 indexed citations
11.
Yu, Ruiyun, et al.. (2023). Spatial-Aware Multi-Level Parsing Network for Human-Object Interaction.. International Journal of Interactive Multimedia and Artificial Intelligence. 9(2). 39–48. 1 indexed citations
12.
Yu, Ruiyun, et al.. (2022). Towards a privacy-preserving smart contract-based data aggregation and quality-driven incentive mechanism for mobile crowdsensing. Journal of Network and Computer Applications. 207. 103483–103483. 19 indexed citations
13.
Han, Guangjie, et al.. (2022). TraGCAN: Trajectory Prediction of Heterogeneous Traffic Agents in IoV Systems. IEEE Internet of Things Journal. 10(8). 7100–7113. 15 indexed citations
14.
An, Yang, Liang Zhang, Leilei Sun, et al.. (2021). Prediction of Treatment Medicines With Dual Adaptive Sequential Networks. IEEE Transactions on Knowledge and Data Engineering. 34(11). 5496–5509. 12 indexed citations
15.
Yu, Ruiyun, Zhihong Wang, Ann Move Oguti, et al.. (2021). CFFNN: Cross Feature Fusion Neural Network for Collaborative Filtering. IEEE Transactions on Knowledge and Data Engineering. 34(10). 4650–4662. 27 indexed citations
16.
Yu, Ruiyun, et al.. (2020). RePiDeM: A Refined POI Demand Modeling based on Multi-Source Data. 964–973. 7 indexed citations
17.
Yu, Ruiyun, Jiannong Cao, Rui Liu, et al.. (2019). Participant Incentive Mechanism Toward Quality-Oriented Sensing. ACM Transactions on Sensor Networks. 15(2). 1–25. 8 indexed citations
18.
Yu, Ruiyun, et al.. (2018). HMM of Telecommunication Big Data for Consumer Churn Prediction. 1903–1910. 2 indexed citations
19.
Liu, Rui, Junbin Liang, Jiannong Cao, et al.. (2016). Understanding Mobile Users’ Privacy Expectations: A Recommendation-Based Method Through Crowdsourcing. IEEE Transactions on Services Computing. 12(2). 304–318. 18 indexed citations
20.
Chen, Xiaofeng, et al.. (2013). Improved Quantum Ant Colony Algorithm based on Bloch Coordinates. Journal of Computers. 8(6). 1 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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