Yueyue Dai
- Computer Networks and Communications top 0.5%
- IoT and Edge/Fog Computing 22
- Caching and Content Delivery 8
- Age of Information Optimization 6
- Information Systems top 0.5%
- Blockchain Technology Applications and Security 12
- Artificial Intelligence top 0.5%
- Privacy-Preserving Technologies in Data 21
- Cryptography and Data Security 7
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- Advanced Wireless Communication Technologies 6
- Vehicular Ad Hoc Networks (VANETs) 6
Yueyue Dai
53 papers receiving 3.7k citations
Hit Papers
Peers
Comparison fields: 5 of 97
- Computer Networks and Communications 2.1k
- Information Systems 1.3k
- Artificial Intelligence 1.6k
- Computer Science Applications 219
- Industrial and Manufacturing Engineering 229
Countries citing papers authored by Yueyue Dai
This map shows the geographic impact of Yueyue Dai'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 Yueyue Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yueyue Dai more than expected).
Fields of papers citing papers by Yueyue Dai
This network shows the impact of papers produced by Yueyue Dai. 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 Yueyue Dai. The network helps show where Yueyue Dai may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yueyue Dai, 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 | 1 | |
| 4 | 2024 | 27 | |
| 5 | 2024 | 5 | |
| 6 | 2024 | 6 | |
| 7 | 2024 | 7 | |
| 8 | 2024 | 3 | |
| 9 | 2024 | 1 | |
| 10 | 2024 | 1 | |
| 11 | 2024 | 3 | |
| 12 | 2024 | 0 | |
| 13 | 2024 | 23 | |
| 14 | Energy Efficient Computation Offloading in Aerial Edge Networks With Multi-Agent Cooperationbreakdown → | 2023 | 111 |
| 15 | 2022 | 18 | |
| 16 | 2020 | 161 | |
| 17 | Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informaticsbreakdown → | 2019 | 312 |
| 18 | Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoTbreakdown → | 2019 | 861 |
| 19 | Joint Computation Offloading and User Association in Multi-Task Mobile Edge Computingbreakdown → | 2018 | 294 |
| 20 | Joint Load Balancing and Offloading in Vehicular Edge Computing and Networksbreakdown → | 2018 | 362 |
About Yueyue Dai
Yueyue Dai is a scholar working on Computational Mathematics, Computer Networks and Communications and Artificial Intelligence, having authored 60 papers that have together received 3.8k indexed citations. Recurring topics across this work include IoT and Edge/Fog Computing (22 papers), Privacy-Preserving Technologies in Data (21 papers), Blockchain Technology Applications and Security (12 papers), Caching and Content Delivery (8 papers), Cryptography and Data Security (7 papers), Advanced Wireless Communication Technologies (6 papers), Vehicular Ad Hoc Networks (VANETs) (6 papers) and Age of Information Optimization (6 papers). The work is most often cited by research in Computer Networks and Communications (2.1k citations), Information Systems (1.3k citations) and Artificial Intelligence (1.6k citations). Yueyue Dai has collaborated with scholars based in China, Norway and Singapore. Frequent co-authors include Yan Zhang, Sabita Maharjan, Du Xu, Yunlong Lu, Xiaohong Huang, Ke Zhang, Guanhua Qiao, Zhuang Chen, Yan Zhang and Qian He. Their work appears in journals such as Journal of Hazardous Materials, IEEE Communications Surveys & Tutorials and IEEE Communications Magazine.
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.