Jun‐Hong Cui

10.6k total citations · 1 hit paper
215 papers, 6.6k citations indexed

About

Jun‐Hong Cui is a scholar working on Ocean Engineering, Computer Networks and Communications and Electrical and Electronic Engineering. According to data from OpenAlex, Jun‐Hong Cui has authored 215 papers receiving a total of 6.6k indexed citations (citations by other indexed papers that have themselves been cited), including 169 papers in Ocean Engineering, 150 papers in Computer Networks and Communications and 138 papers in Electrical and Electronic Engineering. Recurrent topics in Jun‐Hong Cui's work include Underwater Vehicles and Communication Systems (169 papers), Energy Efficient Wireless Sensor Networks (93 papers) and Energy Harvesting in Wireless Networks (69 papers). Jun‐Hong Cui is often cited by papers focused on Underwater Vehicles and Communication Systems (169 papers), Energy Efficient Wireless Sensor Networks (93 papers) and Energy Harvesting in Wireless Networks (69 papers). Jun‐Hong Cui collaborates with scholars based in United States, China and Italy. Jun‐Hong Cui's co-authors include Shengli Zhou, Zhong Zhou, Zheng Peng, Mário Gerla, Peng Xie, Zhijie Shi, Jiejun Kong, Jun Liu, Michael Zuba and Shuguang Cui and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Signal Processing and IEEE Access.

In The Last Decade

Jun‐Hong Cui

208 papers receiving 6.3k citations

Hit Papers

align trajectories sort by overperformance

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.

The challenges of building scalable mobile underwater wireless sensor networks for aquatic applications

2006 595 citations Jun‐Hong Cui, Mário Gerla et al. profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Jun‐Hong Cui	5.0k	4.4k	4.3k	887	782	215	6.6k
Luiz F. M. Vieira	2.3k 0.5×	2.3k 0.5×	2.6k 0.6×	503 0.6×	330 0.4×	142	3.8k
Shu Fu	749 0.2×	843 0.2×	1.7k 0.4×	114 0.1×	27 0.0×	82	2.3k
Xin Su	327 0.1×	431 0.1×	797 0.2×	79 0.1×	92 0.1×	131	1.6k
Fernando J. Álvarez	509 0.1×	270 0.1×	1.2k 0.3×	39 0.0×	112 0.1×	151	1.8k
Deshi Li	278 0.1×	749 0.2×	672 0.2×	51 0.1×	82 0.1×	112	1.5k
Yuzhou Li	266 0.1×	764 0.2×	1.3k 0.3×	25 0.0×	128 0.2×	77	1.8k
Haixin Sun	647 0.1×	170 0.0×	656 0.2×	36 0.0×	498 0.6×	189	2.0k
Zhao Zhao	386 0.1×	407 0.1×	380 0.1×	144 0.2×	52 0.1×	135	934
Shuai Han	228 0.0×	660 0.1×	1.3k 0.3×	18 0.0×	254 0.3×	210	2.2k
Basem Shihada	227 0.0×	1.1k 0.3×	1.7k 0.4×	68 0.1×	6 0.0×	173	2.5k

Countries citing papers authored by Jun‐Hong Cui

Since Specialization

Citations

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

Fields of papers citing papers by Jun‐Hong Cui

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun‐Hong Cui. 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 Jun‐Hong Cui. The network helps show where Jun‐Hong Cui may publish in the future.

Co-authorship network of co-authors of Jun‐Hong Cui

This figure shows the co-authorship network connecting the top 25 collaborators of Jun‐Hong Cui. A scholar is included among the top collaborators of Jun‐Hong Cui 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 Jun‐Hong Cui. Jun‐Hong Cui 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

Peng, Zheng, et al.. (2025). Hamiltonian based AUV navigation using adaptive finite-time trajectory tracking control. Ocean Engineering. 320. 120329–120329. 1 indexed citations

Song, Shanshan, et al.. (2025). A Digital Twin-Based Intelligent Network Architecture for Underwater Acoustic Sensor Networks. IEEE Transactions on Mobile Computing. 24(9). 8196–8213.

Guo, Jiani, Shanshan Song, Jun Liu, et al.. (2024). Exploring Applicable Scenarios and Boundary of MAC Protocols: A MAC Performance Analysis Framework for Underwater Acoustic Networks. IEEE Transactions on Mobile Computing. 23(12). 12717–12730. 4 indexed citations

Zhang, Tong, et al.. (2024). Traffic Load-Aware Resource Management Strategy for Underwater Wireless Sensor Networks. IEEE Transactions on Mobile Computing. 24(1). 243–260. 3 indexed citations

Zhang, Tong, et al.. (2024). Joint Link Scheduling and Power Allocation in Imperfect and Energy-Constrained Underwater Wireless Sensor Networks. IEEE Transactions on Mobile Computing. 23(10). 9863–9880. 11 indexed citations

Zhang, Tong, et al.. (2023). Achieving Fair-Effective Communications and Robustness in Underwater Acoustic Sensor Networks: A Semi-Cooperative Approach. IEEE Transactions on Mobile Computing. 23(5). 5722–5739. 9 indexed citations

Cui, Jun‐Hong, et al.. (2023). VISS-CF: Visual-Inertial Odometry and Sonar Fused SLAM Framework with Enhanced Corner Feature Matching for Underwater Environment. 1–5.

Guo, Jiani, et al.. (2023). An Adaptive Mobile Access MAC Protocol for Underwater Sensor Networks. 1–5. 1 indexed citations

Peng, Zheng, et al.. (2023). Multi-Agent Modeling Based Analysis for Dissipative Discrete Underwater Energy Network System. 1–5. 1 indexed citations

10.

Peng, Zheng, et al.. (2023). Development and Test of a Moored Underwater Docking System for Resident UUVs. 1–2. 1 indexed citations

11.

Song, Shanshan, et al.. (2022). A Segmentation Framework for Acoustic Sidescan Sonar Images Using Improved Smallest Of Constant False Alarm Rate and MAP-MRF. 1–7.

12.

Zhang, Tong, et al.. (2020). DeepOcean: A General Deep Learning Framework for Spatio-Temporal Ocean Sensing Data Prediction. IEEE Access. 8. 79192–79202. 29 indexed citations

13.

Xu, Xiaoka, Shengli Zhou, Kaleel Mahmood, Wei Li, & Jun‐Hong Cui. (2013). Study of class-D power amplifiers for underwater acoustic OFDM transmissions. 2013 OCEANS - San Diego. 1–3. 4 indexed citations

14.

Su, Yishan, et al.. (2013). A versatile lab testbed for Underwater Sensor Networks. 2013 OCEANS - San Diego. 1–5. 3 indexed citations

15.

Zuba, Michael, Zaihan Jiang, T. C. Yang, Yishan Su, & Jun‐Hong Cui. (2013). An advanced channel framework for improved Underwater Acoustic Network simulations. 2013 OCEANS - San Diego. 1–8. 14 indexed citations

16.

Li, Wei, Zheng Peng, Hao Zhou, et al.. (2013). Long Island Sound testbed and experiments. 2013 OCEANS - San Diego. 1–6. 16 indexed citations

17.

Xie, Peng, Zhong Zhou, Zheng Peng, et al.. (2009). Aqua-Sim: An NS-2 based simulator for underwater sensor networks. 1–7. 240 indexed citations

18.

Xie, Peng & Jun‐Hong Cui. (2007). R-MAC: An Energy-Efficient MAC Protocol for Underwater Sensor Networks. 187–198. 112 indexed citations

19.

Kong, Jiejun, Jun‐Hong Cui, Dapeng Wu, & Mário Gerla. (2006). Building Underwater Ad-Hoc Networks and Sensor Networks for Large Scale Real-Time Aquatic Applications. 1–7. 121 indexed citations

20.

Verma, Shekhar, et al.. (2005). SACA: SCM-based adaptive clustering algorithm. 271–279. 5 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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