Qingmin Jia

423 total citations · 1 hit paper
27 papers, 217 citations indexed

About

Qingmin Jia is a scholar working on Computer Networks and Communications, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Qingmin Jia has authored 27 papers receiving a total of 217 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Networks and Communications, 8 papers in Computer Vision and Pattern Recognition and 7 papers in Artificial Intelligence. Recurrent topics in Qingmin Jia's work include IoT and Edge/Fog Computing (10 papers), Caching and Content Delivery (9 papers) and Image and Video Quality Assessment (6 papers). Qingmin Jia is often cited by papers focused on IoT and Edge/Fog Computing (10 papers), Caching and Content Delivery (9 papers) and Image and Video Quality Assessment (6 papers). Qingmin Jia collaborates with scholars based in China, Canada and Thailand. Qingmin Jia's co-authors include Renchao Xie, Tao Huang, Yunjie Liu, Yujiao Hu, Jiang Liu, Jiang Liu, Xiaomao Zhou, Yuan Yao, Yong Lee and F. Richard Yu and has published in prestigious journals such as IEEE Access, IEEE Communications Magazine and IEEE Transactions on Vehicular Technology.

In The Last Decade

Qingmin Jia

24 papers receiving 214 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Review

2024 61 citations Yujiao Hu, Qingmin Jia et al. IEEE Internet of Things Journal profile →

Peers

Qingmin Jia

CNC

EEE

CVPR

AI

IS

Ognjen Dobrijević Croatia

V. Porkodi Iraq

Leon Wong Japan

Jung-Shian Li Taiwan

Marcelo A. Marotta Brazil

Jan Rüth Germany

Pedro Martínez-Julia Japan

Arshad Ali Pakistan

Prohim Tam South Korea

Mounir Kellil France

Ognjen Dobrijević Croatia

Qingmin Jia

211 ×1.5

CNC

83 ×1.4

EEE

61 ×1.4

CVPR

19 ×0.7

AI

17 ×0.6

IS

Citations per year, relative to Qingmin Jia Qingmin Jia (= 1×) peers Ognjen Dobrijević

Countries citing papers authored by Qingmin Jia

Since Specialization

Citations

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

Fields of papers citing papers by Qingmin Jia

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qingmin Jia

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Hu, Yujiao, et al.. (2025). Efficient and Adaptive Human Pose Estimation on Resource-Constrained Computing Devices via Knowledge Distillation and Temporal Propagation. IEEE Internet of Things Journal. 12(11). 17263–17280. 1 indexed citations

2.

Hu, Yujiao, et al.. (2025). Solving Scalable Multiagent Routing Problems With Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems. 36(11). 19604–19618. 7 indexed citations

3.

Hu, Yujiao, et al.. (2025). Deterministic Scheduling and Network Structure Optimization for Time-Critical Computing Tasks in Industrial IoT. 33(6). 3391–3407. 1 indexed citations

4.

Hu, Yujiao, et al.. (2025). LLM-Enabled Multi-Modal Data Synthesis via Cross-Domain Collaboration. IEEE Communications Magazine. 63(10). 86–93.

5.

Zhou, Junlong, et al.. (2025). Air-FedGA: A Grouping Asynchronous Federated Learning Mechanism Exploiting Over-The-Air Computation. 1–12.

6.

Hu, Yujiao, Qingmin Jia, Jinchao Chen, et al.. (2024). CoRaiS: Lightweight Real-Time Scheduler for Multiedge Cooperative Computing. IEEE Internet of Things Journal. 11(17). 28649–28666. 1 indexed citations

7.

Zhou, Xiaomao, Qingmin Jia, Yujiao Hu, et al.. (2024). GenG: An LLM-Based Generic Time Series Data Generation Approach for Edge Intelligence via Cross-Domain Collaboration. 1–6. 3 indexed citations

8.

Jia, Qingmin, et al.. (2024). Advancing General Sensor Data Synthesis by Integrating LLMs and Domain-Specific Generative Models. IEEE Sensors Letters. 8(11). 1–4. 1 indexed citations

9.

Zhou, Xiaomao, et al.. (2024). Cross-Domain Integration for General Sensor Data Synthesis: Leveraging LLMs and Domain-Specific Generative Models in Collaborative Environments. IEEE Sensors Journal. 24(24). 42311–42326. 1 indexed citations

10.

Xie, Junfeng, et al.. (2024). Energy-Efficient Intelligence Sharing in Intelligence Networking-Empowered Edge Computing: A Deep Reinforcement Learning Approach. IEEE Access. 12. 141639–141652.

11.

Xie, Junfeng, et al.. (2024). Computation Offloading and Resource Allocation in Satellite-Terrestrial Integrated Networks: A Deep Reinforcement Learning Approach. IEEE Access. 12. 97184–97195. 4 indexed citations

12.

Hu, Yujiao, Qingmin Jia, Yuan Yao, et al.. (2024). Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Review. IEEE Internet of Things Journal. 11(11). 19143–19167. 61 indexed citations breakdown →

13.

Jia, Qingmin, et al.. (2023). Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Review. arXiv (Cornell University). 1 indexed citations

14.

Hu, Yujiao, et al.. (2022). 3CL-Net: A Four-in-One Networking Paradigm for 6G System. 1 indexed citations

15.

Xie, Renchao, et al.. (2019). Energy-efficient joint caching and transcoding for HTTP adaptive streaming in 5G networks with mobile edge computing. China Communications. 16(7). 229–244. 2 indexed citations

16.

Jia, Qingmin, et al.. (2019). Energy‐efficient computation offloading in 5G cellular networks with edge computing and D2D communications. IET Communications. 13(8). 1122–1130. 28 indexed citations

17.

Jia, Qingmin, Renchao Xie, Tao Huang, Jiang Liu, & Yunjie Liu. (2019). Caching Resource Sharing for Network Slicing in 5G Core Network. Journal of Organizational and End User Computing. 31(4). 1–18. 8 indexed citations

18.

Xie, Renchao, et al.. (2018). Energy-Efficient Joint Caching and Transcoding for HTTP Adaptive Streaming in 5G Networks with Mobile Edge Computing. 1–6. 14 indexed citations

19.

Jia, Qingmin, Renchao Xie, Tao Huang, Jiang Liu, & Yunjie Liu. (2017). Energy-efficient cooperative coded caching for heterogeneous small cell networks. 468–473. 15 indexed citations

20.

Jia, Qingmin, Renchao Xie, Tao Huang, Jiang Liu, & Yunjie Liu. (2017). The Collaboration for Content Delivery and Network Infrastructures: A Survey. IEEE Access. 5. 18088–18106. 21 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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