Lingjun Pu

1.3k total citations · 1 hit paper
41 papers, 1.0k citations indexed

About

Lingjun Pu is a scholar working on Computer Networks and Communications, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Lingjun Pu has authored 41 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Computer Networks and Communications, 15 papers in Computer Vision and Pattern Recognition and 10 papers in Information Systems. Recurrent topics in Lingjun Pu's work include IoT and Edge/Fog Computing (17 papers), Caching and Content Delivery (11 papers) and Image and Video Quality Assessment (11 papers). Lingjun Pu is often cited by papers focused on IoT and Edge/Fog Computing (17 papers), Caching and Content Delivery (11 papers) and Image and Video Quality Assessment (11 papers). Lingjun Pu collaborates with scholars based in China, Germany and United States. Lingjun Pu's co-authors include Xu Chen, Jingdong Xu, Xiaoming Fu, Di Wu, Lin Gao, Weigang Wu, Lei Jiao, Lin Wang, Shuai Yu and Guoqiang Mao and has published in prestigious journals such as IEEE Journal on Selected Areas in Communications, IEEE Transactions on Computers and IEEE Internet of Things Journal.

In The Last Decade

Lingjun Pu

38 papers receiving 1.0k citations

Hit Papers

D2D Fogging: An Energy-Efficient and Incentive-Aware Task... 2016 2026 2019 2022 2016 100 200 300
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.

  1. D2D Fogging: An Energy-Efficient and Incentive-Aware Task Offloading Framework via Network-assisted D2D Collaboration
    2016 305 citations Lingjun Pu, Xu Chen et al. IEEE Journal on Selected Areas in Communications profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lingjun Pu China 14 795 388 217 204 187 41 1.0k
Jingdong Xu China 15 832 1.0× 580 1.5× 145 0.7× 138 0.7× 147 0.8× 60 1.1k
Moo-Ryong Ra United States 10 934 1.2× 474 1.2× 332 1.5× 114 0.6× 276 1.5× 24 1.2k
Wei Shu United States 21 1.1k 1.3× 440 1.1× 214 1.0× 79 0.4× 71 0.4× 106 1.3k
Yuben Qu China 19 568 0.7× 338 0.9× 95 0.4× 283 1.4× 80 0.4× 72 1.0k
Jinliang Xu China 10 690 0.9× 207 0.5× 362 1.7× 128 0.6× 54 0.3× 14 847
Xu Zheng China 8 299 0.4× 161 0.4× 172 0.8× 421 2.1× 111 0.6× 11 777
Minghui Liwang China 19 524 0.7× 389 1.0× 181 0.8× 119 0.6× 58 0.3× 64 912
Shashi Raj Pandey South Korea 16 552 0.7× 453 1.2× 141 0.6× 486 2.4× 172 0.9× 38 1.1k
Lujie Zhong China 19 840 1.1× 372 1.0× 150 0.7× 163 0.8× 39 0.2× 70 1.1k
Long Cheng United States 18 598 0.8× 226 0.6× 194 0.9× 395 1.9× 81 0.4× 59 987

Countries citing papers authored by Lingjun Pu

Since Specialization
Citations

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

Fields of papers citing papers by Lingjun Pu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lingjun Pu

This figure shows the co-authorship network connecting the top 25 collaborators of Lingjun Pu. A scholar is included among the top collaborators of Lingjun Pu 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 Lingjun Pu. Lingjun Pu 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.
Zhang, Miao, et al.. (2025). Implicit Representation-based Volumetric Video Streaming for Photorealistic Full-scene Experience. ACM Transactions on Multimedia Computing Communications and Applications. 22(3). 1–21.
2.
Jiao, Lei, et al.. (2025). Toward sustainable diffusion-based AIGC: Design and online orchestration in distributed edge networks. Computer Networks. 259. 111077–111077.
3.
Zhang, Yuan, et al.. (2025). Bimodal Semantic-Driven 3D Immersive Telepresence System. 57–63.
4.
Xiang, Qiao, et al.. (2024). NetDPI: Efficient Deep Packet Inspection via Filtering-Plus-Verification in Programmable 5G Data Plane for Multi-Access Edge Computing. IEEE Transactions on Mobile Computing. 23(12). 15031–15047. 2 indexed citations
5.
6.
Zhang, Yuan, et al.. (2023). To Distill or Not to Distill: Toward Fast, Accurate, and Communication-Efficient Federated Distillation Learning. IEEE Internet of Things Journal. 11(6). 10040–10053. 6 indexed citations
7.
Zhang, Yuan, Lingjun Pu, Tao Lin, & Jinyao Yan. (2023). QoE-Oriented Mobile Virtual Reality Game in Distributed Edge Networks. IEEE Transactions on Multimedia. 25. 9132–9146. 7 indexed citations
8.
9.
Zeng, Liekang, et al.. (2022). Adaptive Progressive Image Enhancement for Edge-Assisted Mobile Vision. 21. 744–751. 1 indexed citations
10.
Pu, Lingjun, et al.. (2022). Sophon: Super-Resolution Enhanced 360° Video Streaming with Visual Saliency-aware Prefetch. Proceedings of the 30th ACM International Conference on Multimedia. 3124–3133. 14 indexed citations
11.
Pu, Lingjun, et al.. (2022). Muster: Multi-source Streaming for Tile-based 360° Videos within Cloud Native 5G Networks. IEEE Transactions on Mobile Computing. 1–16. 4 indexed citations
12.
Li, Yusen, Haoyuan Liu, Lingjun Pu, et al.. (2021). Reinforcement Learning-Based Resource Partitioning for Improving Responsiveness in Cloud Gaming. IEEE Transactions on Computers. 71(5). 1049–1062. 14 indexed citations
13.
Zhang, Yuan, et al.. (2020). Dynamic Component Placement and Request Scheduling for IoT Big Data Streaming. IEEE Internet of Things Journal. 7(8). 7156–7170. 4 indexed citations
14.
15.
Zhou, Pan, Yulai Xie, Ben Niu, et al.. (2020). QoE-Aware 3D Video Streaming via Deep Reinforcement Learning in Software Defined Networking Enabled Mobile Edge Computing. IEEE Transactions on Network Science and Engineering. 8(1). 419–433. 27 indexed citations
16.
Zhang, Jianfeng, Wensheng Zhang, Lingjun Pu, & Jingdong Xu. (2020). QoS Optimization of DNN Serving Systems Based on Per-Request Latency Characteristics. 2 indexed citations
17.
Zhou, Zhi, Song Yang, Lingjun Pu, & Shuai Yu. (2020). CEFL: Online Admission Control, Data Scheduling, and Accuracy Tuning for Cost-Efficient Federated Learning Across Edge Nodes. IEEE Internet of Things Journal. 7(10). 9341–9356. 56 indexed citations
18.
Li, Yusen, Haoyuan Liu, Lingjun Pu, et al.. (2019). Themis: Efficient and Adaptive Resource Partitioning for Reducing Response Delay in Cloud Gaming. 491–499. 6 indexed citations
19.
Pu, Lingjun, et al.. (2018). Online Resource Allocation, Content Placement and Request Routing for Cost-Efficient Edge Caching in Cloud Radio Access Networks. IEEE Journal on Selected Areas in Communications. 36(8). 1751–1767. 78 indexed citations
20.
Pu, Lingjun, et al.. (2018). Joint Task Offloading and Base Station Association in Mobile Edge Computing. 55(3). 537. 8 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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