World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
43
Citations
6591
World Ranking
3950
National Ranking
186

Computer Science

D-Index
43
Citations
6686
World Ranking
8061
National Ranking
324

Lian Zhao 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 Lian Zhao 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: 204 publications — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Lian Zhao 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 Lian Zhao 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: 43 D-Index — 45th percentile

45% of scientists in this discipline score the same or lower.

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

Overview

Lian Zhao is affiliated with Toronto Metropolitan University in Canada and has made extensive contributions in the fields of engineering and computer science. Their published work spans various subfields including electrical and electronic engineering, computer networks and communications, aerospace engineering, artificial intelligence, and computer vision and pattern recognition.

Their research primarily focuses on topics related to the Internet of Things (IoT) and edge/fog computing, advanced wireless communication technologies, advanced MIMO systems optimization, satellite communication systems, age of information optimization, vehicular ad hoc networks (VANETs), and UAV applications and optimization.

Frequent collaborators in their research include Jie Gao, Mushu Li, Xuemin Shen, Xiaohuan Lu, and Haibo Zhou.

Lian Zhao has published extensively in prominent venues such as:

  • IEEE Transactions on Vehicular Technology
  • IEEE Internet of Things Journal
  • IEEE Transactions on Wireless Communications
  • arXiv (Cornell University)
  • IEEE Transactions on Mobile Computing

Several recent papers authored or co-authored by Lian Zhao include:

  • "Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things" (2020, IEEE Transactions on Industrial Informatics)
  • "Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks" (2020, IEEE Transactions on Cognitive Communications and Networking)
  • "Energy-Efficient Joint Task Offloading and Resource Allocation in OFDMA-Based Collaborative Edge Computing" (2021, IEEE Transactions on Wireless Communications)
  • "DeepNOMA: A Unified Framework for NOMA Using Deep Multi-Task Learning" (2020, IEEE Transactions on Wireless Communications)
  • "A Survey of Decentralizing Applications via Blockchain: The 5G and Beyond Perspective" (2021, IEEE Communications Surveys & Tutorials)

In addition to journal articles, Lian Zhao has contributed to book publications. One such work is "Intelligent Computing and Communication for the Internet of Vehicles," published by Springer Nature in 2023.

Best Publications

  • Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization

    Mushu Li;Nan Cheng;Jie Gao;Yinlu Wang

  • Performance Analysis and Enhancement of the DSRC for VANET's Safety Applications

    Khalid Abdel Hafeez;Lian Zhao;Bobby Ma;Jon W. Mark

  • Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization

    Mushu Li;Nan Cheng;Jie Gao;Yinlu Wang

  • Partial Offloading Scheduling and Power Allocation for Mobile Edge Computing Systems

    Zhufang Kuang;Linfeng Li;Jie Gao;Lian Zhao

  • Cooperative Edge Caching in User-Centric Clustered Mobile Networks

    Shan Zhang;Peter He;Katsuya Suto;Peng Yang

  • Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things

    Ying Chen;Zhiyong Liu;Yongchao Zhang;Yuan Wu

  • Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks

    Mushu Li;Jie Gao;Lian Zhao;Xuemin Shen

  • Water-Filling: A Geometric Approach and its Application to Solve Generalized Radio Resource Allocation Problems

    P. He;Lian Zhao;Sheng Zhou;Zhisheng Niu

  • Energy-Efficient Joint Task Offloading and Resource Allocation in OFDMA-based Collaborative Edge Computing

    Lin Tan;Zhufang Kuang;Lian Zhao;Anfeng Liu

  • Synergism of INS and PDR in Self-Contained Pedestrian Tracking With a Miniature Sensor Module

    Chengliang Huang;Zaiyi Liao;Lian Zhao

  • Impact of Mobility on VANETs' Safety Applications

    Khalid Abdel Hafeez;Lian Zhao;Zaiyi Liao;Bobby Ngok-Wah Ma

  • DeepNOMA: A Unified Framework for NOMA Using Deep Multi-Task Learning

    Neng Ye;Xiangming Li;Hanxiao Yu;Lian Zhao

  • A Survey of Decentralizing Applications via Blockchain: The 5G and Beyond Perspective

    Kaifeng Yue;Yuanyuan Zhang;Yanru Chen;Yang Li

  • Distributed Multichannel and Mobility-Aware Cluster-Based MAC Protocol for Vehicular Ad Hoc Networks

    Khalid Abdel Hafeez;Lian Zhao;Jon W. Mark;Xuemin Shen

  • A fuzzy-logic-based cluster head selection algorithm in VANETs

    Khalid Abdel Hafeez;Lian Zhao;Zaiyi Liao;Bobby Ngok-Wah Ma

  • Performance Analysis of Broadcast Messages in VANETs Safety Applications

    Khalid Abdel Hafeez;Lian Zhao;Zaiyi Liao;Bobby Ngok-Wah Ma

  • Efficient Resource Allocation in Device-to-Device Communication Using Cognitive Radio Technology

    Ajmery Sultana;Lian Zhao;Xavier Fernando

  • Joint Unmanned Aerial Vehicle (UAV) Deployment and Power Control for Internet of Things Networks

    Shu Fu;Yujie Tang;Ning Zhang;Lian Zhao

  • Collaborative Multi-Resource Allocation in Terrestrial-Satellite Network Towards 6G

    Shu Fu;Jie Gao;Lian Zhao

  • Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems

    S. Jassar;Z. Liao;L. Zhao

  • Integrated Resource Management for Terrestrial-Satellite Systems

    Shu Fu;Jie Gao;Lian Zhao

  • The Design of Dynamic Probabilistic Caching with Time-Varying Content Popularity

    Jie Gao;Shan Zhang;Lian Zhao;Xuemin Shen

  • SMDP-Based Coordinated Virtual Machine Allocations in Cloud-Fog Computing Systems

    Qizhen Li;Lianwen Zhao;Jie Gao;Hongbin Liang

  • Cooperative Edge Caching in User-Centric Clustered Mobile Networks

    Shan Zhang;Peter He;Katsuya Suto;Peng Yang

Frequent Co-Authors

Xuemin Shen
Xuemin Shen University of Waterloo
Jon W. Mark
Jon W. Mark University of Waterloo
Shan Zhang
Shan Zhang Beihang University
Zhisheng Niu
Zhisheng Niu Tsinghua University
Sheng Zhou
Sheng Zhou Tsinghua University
Alagan Anpalagan
Alagan Anpalagan Toronto Metropolitan University
Hongwei Li
Hongwei Li University of Electronic Science and Technology of China
Bala Venkatesh
Bala Venkatesh Toronto Metropolitan University
Xiaohu Tang
Xiaohu Tang Southwest Jiaotong University
Nan Cheng
Nan Cheng Xidian University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students interested in Electronics and Electrical Engineering, exploring related online degrees can expand career opportunities. Many programs offer accelerated online degree programs designed specifically for working adults, enabling faster completion while balancing professional commitments.

Instructional design is another growing field closely connected with technology education. Pursuing an instructional design degree online equips professionals with skills to develop effective training and learning materials, a valuable asset in tech-driven industries.

Competency-based learning is gaining traction as a flexible way to earn advanced qualifications. Understanding what is a competency based masters degree can help students tailor their education to focus on mastering specific skills at their own pace.

Military spouses and dependents often face unique challenges in continuing education. Fortunately, many institutions cater to these needs, and the military spouse online college options provide flexibility and support to help overcome deployment and relocation hurdles.

Best Scientists Citing Lian Zhao

Trending Scientists

Recently Published Articles