World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
55
Citations
11909
World Ranking
2177
National Ranking
372

Computer Science

D-Index
57
Citations
12361
World Ranking
3860
National Ranking
517

Kan Zheng 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 Kan Zheng 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: 302 publications — 58th percentile

58% of scientists in this discipline score the same or lower.

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

Kan Zheng 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 Kan Zheng 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: 55 D-Index — 69th percentile

69% of scientists in this discipline score the same or lower.

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

Overview

Kan Zheng is affiliated with Beijing University of Posts and Telecommunications in China. Their research spans multiple fields primarily focused on Engineering and Computer Science, with significant contributions to Electrical and Electronic Engineering, Computer Networks and Communications, Control and Systems Engineering, Mechanical Engineering, and Information Systems.

The scientist has a notable record of publications addressing a variety of technical topics. Their main research interests include:

  • IoT and Edge/Fog Computing
  • Vehicular Ad Hoc Networks (VANETs)
  • Advanced MIMO Systems Optimization
  • Blockchain Technology Applications and Security
  • Soil Mechanics and Vehicle Dynamics
  • Traffic control and management
  • Age of Information Optimization

Kan Zheng has authored or co-authored papers published in several prominent venues, including:

  • arXiv (Cornell University)
  • IEEE Internet of Things Journal
  • IEEE Transactions on Vehicular Technology
  • IEEE Access
  • IEEE Network

Some of their recent publications include:

  • "Resource Allocation Based on Deep Reinforcement Learning in IoT Edge Computing" (2020) published in IEEE Journal on Selected Areas in Communications
  • "Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges" (2020) published in IEEE Communications Surveys & Tutorials
  • "Intelligent Radio Access Network Slicing for Service Provisioning in 6G: A Hierarchical Deep Reinforcement Learning Approach" (2021) published in IEEE Transactions on Communications
  • "A driving intention prediction method based on hidden Markov model for autonomous driving" (2020) published in Computer Communications
  • "An intelligent self-sustained RAN slicing framework for diverse service provisioning in 5G-beyond and 6G networks" (2020) published in Intelligent and Converged Networks

Collaborations play a considerable role in their work, with frequent coauthors including Lei Lei, Haojun Yang, Lu Hou, Kuan Zhang, and Jie Mei. These collaborations have contributed to a substantial publication record.

In addition to journal articles, Kan Zheng has published a book through Springer Nature titled Blockchain-Based Internet of Things (2024), which addresses the intersection of blockchain technologies and IoT systems.

Best Publications

  • Blockchain-Based Decentralized Trust Management in Vehicular Networks

    Zhe Yang;Kan Yang;Lei Lei;Kan Zheng

  • Heterogeneous Vehicular Networking: A Survey on Architecture, Challenges, and Solutions

    Kan Zheng;Qiang Zheng;Periklis Chatzimisios;Wei Xiang

  • An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare

    Zhe Yang;Qihao Zhou;Lei Lei;Kan Zheng

  • Radio resource allocation in LTE-advanced cellular networks with M2M communications

    Kan Zheng;Fanglong Hu;Wenbo Wang;Wei Xiang

  • Big data-driven optimization for mobile networks toward 5G

    Kan Zheng;Zhe Yang;Kuan Zhang;Periklis Chatzimisios

  • Survey of Large-Scale MIMO Systems

    Kan Zheng;Long Zhao;Jie Mei;Bin Shao

  • 5G Mobile Communications

    Wei Xiang;Kan Zheng;Xuemin Shen

  • Resource Allocation Based on Deep Reinforcement Learning in IoT Edge Computing

    Xiong Xiong;Kan Zheng;Lei Lei;Lu Hou

  • An SMDP-Based Resource Allocation in Vehicular Cloud Computing Systems

    Kan Zheng;Hanlin Meng;Periklis Chatzimisios;Lei Lei

  • Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

    Lei Lei;Yue Tan;Kan Zheng;Shiwen Liu

  • Low power wide area machine-to-machine networks: key techniques and prototype

    Xiong Xiong;Kan Zheng;Rongtao Xu;Wei Xiang

  • Massive MIMO Channel Models: A Survey

    Kan Zheng;Suling Ou;Xuefeng Yin

  • Challenges on wireless heterogeneous networks for mobile cloud computing

    Lei Lei;Zhangdui Zhong;Kan Zheng;Jiadi Chen

  • Design and Implementation of LPWA-Based Air Quality Monitoring System

    Kan Zheng;Shaohang Zhao;Zhe Yang;Xiong Xiong

  • Challenges of massive access in highly dense LTE-advanced networks with machine-to-machine communications

    Kan Zheng;Suling Ou;Jesus Alonso-Zarate;Mischa Dohler

  • A blockchain-based reputation system for data credibility assessment in vehicular networks

    Zhe Yang;Kan Zheng;Kan Yang;Victor C. M. Leung

  • Internet of Things Cloud: Architecture and Implementation

    Lu Hou;Shaohang Zhao;Xiong Xiong;Kan Zheng

  • Federated reinforcement learning: techniques, applications, and open challenges

    Jiaju Qi;Qihao Zhou;Lei Lei;Kan Zheng

  • Soft-defined heterogeneous vehicular network: architecture and challenges

    Kan Zheng;Lu Hou;Hanlin Meng;Qiang Zheng

  • A Latency and Reliability Guaranteed Resource Allocation Scheme for LTE V2V Communication Systems

    Jie Mei;Kan Zheng;Long Zhao;Yong Teng

  • A Survey of Collaborative Filtering-Based Recommender Systems for Mobile Internet Applications

    Zhe Yang;Bing Wu;Kan Zheng;Xianbin Wang

  • Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

    Lei Lei;Yue Tan;Kan Zheng;Shiwen Liu

Frequent Co-Authors

Wenbo Wang
Wenbo Wang Beijing University of Posts and Telecommunications
Wei Xiang
Wei Xiang La Trobe University
Periklis Chatzimisios
Periklis Chatzimisios International Hellenic University
Xianbin Wang
Xianbin Wang University of Western Ontario
Kuan Zhang
Kuan Zhang University of Nebraska–Lincoln
Mischa Dohler
Mischa Dohler King's College London
Xuemin Shen
Xuemin Shen University of Waterloo
Yi Qian
Yi Qian University of Nebraska–Lincoln
Victor C. M. Leung
Victor C. M. Leung Shenzhen University
Lajos Hanzo
Lajos Hanzo University of Southampton

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