Xiangyun Tang
- Information Systems top 2%
- Blockchain Technology Applications and Security 5
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- Network Security and Intrusion Detection 7
- Caching and Content Delivery 2
- Software-Defined Networks and 5G 2
- Artificial Intelligence top 5%
- Privacy-Preserving Technologies in Data 10
- Cryptography and Data Security 7
- Internet Traffic Analysis and Secure E-voting 3
- Health Informatics top 10%
- Signal Processing top 10%
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- UAV Applications and Optimization 3
- Co-authors
- Liehuang ZhuMeng ShenXiaojiang DuMohsen GuizaniJie ZhangKe XuDusit NiyatoJiawen Kang
- Journals
- IEEE Journal on Selected Areas in Communications (1 paper)IEEE Transactions on Vehicular Technology (1 paper)IEEE Internet of Things Journal (5 papers)
- Partner nations
- ChinaSingaporeUnited States
In The Last Decade
Xiangyun Tang
19 papers receiving 634 citations
Hit Papers
Peers
Comparison fields: 5 of 61
- Information Systems 322
- Computer Networks and Communications 290
- Artificial Intelligence 346
- Health Informatics 13
- Signal Processing 46
Countries citing papers authored by Xiangyun Tang
This map shows the geographic impact of Xiangyun Tang'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 Xiangyun Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiangyun Tang more than expected).
Fields of papers citing papers by Xiangyun Tang
This network shows the impact of papers produced by Xiangyun Tang. 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 Xiangyun Tang. The network helps show where Xiangyun Tang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Xiangyun Tang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 2025 | 0 | |
| 6 | 2025 | 10 | |
| 7 | 2025 | 1 | |
| 8 | 2024 | 1 | |
| 9 | 2024 | 0 | |
| 10 | 2024 | 4 | |
| 11 | 2024 | 1 | |
| 12 | 2024 | 0 | |
| 13 | 2024 | 14 | |
| 14 | 2024 | 1 | |
| 15 | 2023 | 1 | |
| 16 | 2023 | 4 | |
| 17 | 2023 | 41 | |
| 18 | 2022 | 16 | |
| 19 | 2021 | 16 | |
| 20 | Privacy-Preserving Support Vector Machine Training Over Blockchain-Based Encrypted IoT Data in Smart Citiesbreakdown → | 2019 | 316 |
About Xiangyun Tang
Xiangyun Tang is a scholar working on Artificial Intelligence, Computer Networks and Communications and Information Systems, having authored 28 papers that have together received 649 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (10 papers), Network Security and Intrusion Detection (7 papers), Cryptography and Data Security (7 papers), Blockchain Technology Applications and Security (5 papers), Internet Traffic Analysis and Secure E-voting (3 papers), UAV Applications and Optimization (3 papers), Caching and Content Delivery (2 papers) and Software-Defined Networks and 5G (2 papers). The work is most often cited by research in Information Systems (322 citations), Computer Networks and Communications (290 citations) and Artificial Intelligence (346 citations). Xiangyun Tang has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Liehuang Zhu, Meng Shen, Xiaojiang Du, Mohsen Guizani, Jie Zhang, Ke Xu, Dusit Niyato, Jiawen Kang, Qi Li and Qiang Qu. Their work appears in journals such as IEEE Journal on Selected Areas in Communications, IEEE Transactions on Vehicular Technology and IEEE Internet of Things Journal.
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.