Xu Chen
Impact in
- Computer Networks and Communications top 0.05%
- IoT and Edge/Fog Computing
- Age of Information Optimization
- Caching and Content Delivery
- Computer Science Applications top 0.2%
Papers in
-
- IoT and Edge/Fog Computing 112
- Age of Information Optimization 52
- Caching and Content Delivery 31
-
- Privacy-Preserving Technologies in Data 73
- Stochastic Gradient Optimization Techniques 23
- Co-authors
- Zhi Zhou (79 shared papers)Xiaoming Fu (18 shared papers)Wenzhong Li (6 shared papers)Lei Jiao (5 shared papers)Junshan Zhang (22 shared papers)En Li (4 shared papers)Liekang Zeng (25 shared papers)Xiaofei Wang (8 shared papers)
- Journals
- IEEE Transactions on Mobile Computing (33 papers)IEEE Internet of Things Journal (20 papers)IEEE/ACM Transactions on Networking (17 papers)IEEE Journal on Selected Areas in Communications (14 papers)IEEE Transactions on Parallel and Distributed Systems (10 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Xu Chen
367 papers receiving 15.9k citations
Xu Chen's Hit Papers
Peers
Comparison fields: 5 of 183
- Computer Networks and Communications 9.6k
- Computer Science Applications 979
- Information Systems 3.3k
- Artificial Intelligence 4.4k
- Computer Vision and Pattern Recognition 2.5k
Countries citing papers authored by Xu Chen
This map shows the geographic impact of Xu Chen'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 Xu Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xu Chen more than expected).
Fields of papers citing papers by Xu Chen
This network shows the impact of papers produced by Xu Chen. 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 Xu Chen. The network helps show where Xu Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside Xu Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 397 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing Hit paper breakdown → | 2015 | 2107 |
| 2 | Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing Hit paper breakdown → | 2019 | 1425 |
| 3 | Convergence of Edge Computing and Deep Learning: A Comprehensive Survey Hit paper breakdown → | 2020 | 980 |
| 4 | In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning Hit paper breakdown → | 2019 | 780 |
| 5 | Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing Hit paper breakdown → | 2019 | 589 |
| 6 | A survey on large language model based autonomous agents Hit paper breakdown → | 2024 | 524 |
| 7 | Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing Hit paper breakdown → | 2018 | 401 |
| 8 | A big data approach for logistics trajectory discovery from RFID-enabled production data Hit paper breakdown → | 2015 | 336 |
| 9 | HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning Hit paper breakdown → | 2020 | 319 |
| 10 | D2D Fogging: An Energy-Efficient and Incentive-Aware Task Offloading Framework via Network-assisted D2D Collaboration Hit paper breakdown → | 2016 | 306 |
| 11 | Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors Hit paper breakdown → | 2015 | 300 |
| 12 | Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework Hit paper breakdown → | 2020 | 281 |
| 13 | Edge Intelligence Hit paper breakdown → | 2018 | 274 |
| 14 | When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multitimescale Resource Management for Multiaccess Edge Computing in 5G Ultradense Network Hit paper breakdown → | 2020 | 264 |
| 15 | FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring Hit paper breakdown → | 2020 | 251 |
| 16 | 2020 | 193 | |
| 17 | 2017 | 193 | |
| 18 | 2014 | 187 | |
| 19 | 2015 | 175 | |
| 20 | 2019 | 156 |
About Xu Chen
Xu Chen is a scholar working on Computer Networks and Communications, Artificial Intelligence, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Information Systems, having authored 397 papers that have together received 16.2k indexed citations. Recurring topics across this work include IoT and Edge/Fog Computing (112 papers), Privacy-Preserving Technologies in Data (73 papers), Age of Information Optimization (52 papers), Mobile Crowdsensing and Crowdsourcing (37 papers), Caching and Content Delivery (31 papers), Cloud Computing and Resource Management (27 papers), Blockchain Technology Applications and Security (25 papers) and Stochastic Gradient Optimization Techniques (23 papers). The work is most often cited by research in Computer Networks and Communications (9.6k citations), Computer Science Applications (979 citations), Information Systems (3.3k citations), Artificial Intelligence (4.4k citations) and Computer Vision and Pattern Recognition (2.5k citations). Xu Chen has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Zhi Zhou, Xiaoming Fu, Wenzhong Li, Lei Jiao, Junshan Zhang, En Li, Liekang Zeng, Xiaofei Wang, Qiong Wu and Yiwen Han. Their work appears in journals such as IEEE Transactions on Mobile Computing, IEEE Internet of Things Journal, IEEE/ACM Transactions on Networking, IEEE Journal on Selected Areas in Communications and IEEE Transactions on Parallel and Distributed Systems.
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.