World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
47
Citations
11073
World Ranking
3167
National Ranking
513

Computer Science

D-Index
49
Citations
11302
World Ranking
5826
National Ranking
770

Rong Yu publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Rong Yu sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 173 publications — 21st percentile

21% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

Rong Yu D-index placement in Electronics and Electrical Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Rong Yu sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 47 D-Index — 54th percentile

54% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Overview

Rong Yu is affiliated with Guangdong University of Technology in China. Their research primarily focuses on areas within Computer Science and Engineering, with notable contributions across several subfields, including Artificial Intelligence, Electrical and Electronic Engineering, Computer Networks and Communications, Information Systems, and Automotive Engineering.

Their work covers a range of topics such as Privacy-Preserving Technologies in Data, IoT and Edge/Fog Computing, Vehicular Ad Hoc Networks (VANETs), Blockchain Technology Applications and Security, Mobile Crowdsensing and Crowdsourcing, Stochastic Gradient Optimization Techniques, and Autonomous Vehicle Technology and Safety.

Rong Yu has published extensively in frequent venues, including IEEE Transactions on Vehicular Technology, IEEE Internet of Things Journal, arXiv (Cornell University), Journal of Physics Conference Series, and IEEE Transactions on Network Science and Engineering. These venues reflect the intersection of their work in network communication, Internet of Things, and vehicular technologies.

Some of their recent papers include:

  • "Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach," 2020, IEEE Access
  • "Toward Resource-Efficient Federated Learning in Mobile Edge Computing," 2021, IEEE Network
  • "Incentivizing Differentially Private Federated Learning: A Multidimensional Contract Approach," 2021, IEEE Internet of Things Journal
  • "DeepBAN: A Temporal Convolution-Based Communication Framework for Dynamic WBANs," 2021, IEEE Transactions on Communications
  • "Securing parked vehicle assisted fog computing with blockchain and optimal smart contract design," 2020, IEEE/CAA Journal of Automatica Sinica

Their frequent coauthors include Maoqiang Wu, Dongdong Ye, Miao Pan, Jiawen Kang, and Xumin Huang, indicating sustained collaborative efforts in their research community.

Best Publications

  • Enabling Localized Peer-to-Peer Electricity Trading Among Plug-in Hybrid Electric Vehicles Using Consortium Blockchains

    Jiawen Kang;Rong Yu;Xumin Huang;Sabita Maharjan

  • Consortium Blockchain for Secure Energy Trading in Industrial Internet of Things

    Zhetao Li;Jiawen Kang;Rong Yu;Dongdong Ye

  • Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks

    Jiawen Kang;Rong Yu;Xumin Huang;Maoqiang Wu

  • Home M2M networks: Architectures, standards, and QoS improvement

    Yan Zhang;Rong Yu;Shengli Xie;Wenqing Yao

  • Toward cloud-based vehicular networks with efficient resource management

    Rong Yu;Yan Zhang;Stein Gjessing;Wenlong Xia

  • Cognitive machine-to-machine communications: visions and potentials for the smart grid

    Yan Zhang;Rong Yu;Maziar Nekovee;Yi Liu

  • Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach

    Dongdong Ye;Rong Yu;Miao Pan;Zhu Han

  • Cognitive radio based hierarchical communications infrastructure for smart grid

    Rong Yu;Yan Zhang;S. Gjessing;Chau Yuen

  • Privacy-Preserved Pseudonym Scheme for Fog Computing Supported Internet of Vehicles

    Jiawen Kang;Rong Yu;Xumin Huang;Yan Zhang

  • Distributed Reputation Management for Secure and Efficient Vehicular Edge Computing and Networks

    Xumin Huang;Rong Yu;Jiawen Kang;Yan Zhang

  • Balancing Power Demand Through EV Mobility in Vehicle-to-Grid Mobile Energy Networks

    Rong Yu;Weifeng Zhong;Shengli Xie;Chau Yuen

  • Exploring Mobile Edge Computing for 5G-Enabled Software Defined Vehicular Networks

    Xumin Huang;Rong Yu;Jiawen Kang;Yejun He

  • A Parallel Cooperative Spectrum Sensing in Cognitive Radio Networks

    Shengli Xie;Yi Liu;Yan Zhang;Rong Yu

  • MixGroup: Accumulative Pseudonym Exchanging for Location Privacy Enhancement in Vehicular Social Networks

    Rong Yu;Jiawen Kang;Xumin Huang;Shengli Xie

  • Toward Resource-Efficient Federated Learning in Mobile Edge Computing

    Rong Yu;Peichun Li

  • Electricity Cost Minimization for a Microgrid With Distributed Energy Resource Under Different Information Availability

    Yi Liu;Chau Yuen;Naveed Ul Hassan;Shisheng Huang

  • Blockchain-based Federated Learning for Industrial Metaverses: Incentive Scheme with Optimal AoI

    Unknown

  • Optimal Resource Sharing in 5G-Enabled Vehicular Networks: A Matrix Game Approach

    Rong Yu;Jiefei Ding;Xumin Huang;Ming-Tuo Zhou

  • Incentivizing Differentially Private Federated Learning: A Multidimensional Contract Approach

    Maoqiang Wu;Dongdong Ye;Jiahao Ding;Yuanxiong Guo

  • PHEV charging and discharging cooperation in V2G networks: A coalition game approach

    Rong Yu;Jiefei Ding;Weifeng Zhong;Yi Liu

  • Cooperative Resource Management in Cloud-Enabled Vehicular Networks

    Rong Yu;Xumin Huang;Jiawen Kang;Jiefei Ding

  • Queuing-Based Energy Consumption Management for Heterogeneous Residential Demands in Smart Grid

    Yi Liu;Chau Yuen;Rong Yu;Yan Zhang

Frequent Co-Authors

Shengli Xie
Shengli Xie Guangdong University of Technology
Jiawen Kang
Jiawen Kang Guangdong University of Technology
Stein Gjessing
Stein Gjessing University of Oslo
Miao Pan
Miao Pan University of Houston
Chau Yuen
Chau Yuen Nanyang Technological University
Sabita Maharjan
Sabita Maharjan University of Oslo
Lingyang Song
Lingyang Song Peking University
Lei Shu
Lei Shu Nanjing Agricultural University
Mohsen Guizani
Mohsen Guizani Mohamed bin Zayed University of Artificial Intelligence
Kun Yang
Kun Yang University of Essex

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