World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
7683
World Ranking
5437
National Ranking
729

De-gan Zhang publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where De-gan Zhang sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 115 publications — 13th percentile

13% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

De-gan Zhang D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where De-gan Zhang sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 51 D-Index — 63rd percentile

63% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

De-gan Zhang is affiliated with Tianjin University of Technology in China and has an extensive research portfolio primarily in computer science and engineering. Their work focuses largely on computer networks and communications, electrical and electronic engineering, information systems, and artificial intelligence, with significant contributions to applied topics in IoT and edge/fog computing.

Their scholarly output includes numerous papers published in prominent venues, addressing various aspects of networking protocols, edge computing, and vehicular networks. Notable recent publications include:

  • A Multi-Path Routing Protocol Based on Link Lifetime and Energy Consumption Prediction for Mobile Edge Computing (2020, IEEE Access)
  • Novel method of mobile edge computation offloading based on evolutionary game strategy for IoT devices (2020, AEU - International Journal of Electronics and Communications)
  • Task offloading method of edge computing in internet of vehicles based on deep reinforcement learning (2022, Cluster Computing)
  • A new algorithm of clustering AODV based on edge computing strategy in IOV (2021, Wireless Networks)
  • Novel Edge Caching Approach Based on Multi-Agent Deep Reinforcement Learning for Internet of Vehicles (2023, IEEE Transactions on Intelligent Transportation Systems)

Their research covers multiple interrelated topics, including:

  • IoT and Edge/Fog Computing
  • Caching and Content Delivery
  • Blockchain Technology Applications and Security
  • Vehicular Ad Hoc Networks (VANETs)
  • Age of Information Optimization
  • Opportunistic and Delay-Tolerant Networks
  • Energy Efficient Wireless Sensor Networks

Frequent collaborators in Zhang's research include Jie Zhang, Ting Zhang, and Yu-ya Cui, reflecting ongoing partnerships that have contributed to multiple joint publications.

Their work has appeared repeatedly in specific journals and conferences that demonstrate consistent engagement with communities in computing and communications, such as:

  • Cluster Computing
  • AEU - International Journal of Electronics and Communications
  • Ad Hoc Networks
  • IEEE Access
  • Wireless Networks

Zhang's research often intersects with engineering challenges and the application of advanced computational models including deep reinforcement learning and game-theoretic approaches targeted at optimizing task offloading and routing in dynamic network environments. This approach addresses both energy consumption and latency optimization in mobile edge and vehicular networks.

Best Publications

  • An Energy-Balanced Routing Method Based on Forward-Aware Factor for Wireless Sensor Networks

    Degan Zhang;Guang Li;Ke Zheng;Xuechao Ming

  • New Multi-Hop Clustering Algorithm for Vehicular Ad Hoc Networks

    Degan Zhang;Hui Ge;Ting Zhang;Yu-Ya Cui

  • A new constructing approach for a weighted topology of wireless sensor networks based on local-world theory for the Internet of Things (IOT)

    Unknown

  • Novel unequal clustering routing protocol considering energy balancing based on network partition & distance for mobile education

    De-gan Zhang;Si Liu;Ting Zhang;Ting Zhang;Zhao Liang

  • Novel self-adaptive routing service algorithm for application in VANET

    Degan Zhang;Degan Zhang;Ting Zhang;Ting Zhang;Xiaohuan Liu;Xiaohuan Liu

  • Design and implementation of embedded un-interruptible power supply system EUPSS for web-based mobile application

    De-gan Zhang;Xiao-dan Zhang

  • Capacity of Cooperative Vehicular Networks with Infrastructure Support: Multiuser Case

    Jieqiong Chen;Guoqiang Mao;Changle Li;Weifa Liang

  • A novel multicast routing method with minimum transmission for WSN of cloud computing service

    De-Gan Zhang;Ke Zheng;Ting Zhang;Xiang Wang

  • A Low Duty Cycle Efficient MAC Protocol Based on Self-Adaption and Predictive Strategy

    De-gan Zhang;De-gan Zhang;Shan Zhou;Ya-meng Tang

  • A kind of effective data aggregating method based on compressive sensing for wireless sensor network

    De-gan Zhang;Ting Zhang;Jie Zhang;Yue Dong

  • A new method of data missing estimation with FNN-based tensor heterogeneous ensemble learning for internet of vehicle

    Ting Zhang;De-gan Zhang;Hao-ran Yan;Jian-ning Qiu

  • Novel optimized link state routing protocol based on quantum genetic strategy for mobile learning

    De-gan Zhang;De-gan Zhang;Ting Zhang;Yue Dong;Xiao-huan Liu

  • Novel approach of distributed & adaptive trust metrics for MANET

    De-gan Zhang;De-gan Zhang;Jin-xin Gao;Xiao-huan Liu;Ting Zhang

  • Novel dynamic source routing protocol (DSR) based on genetic algorithm-bacterial foraging optimization (GA-BFO)

    De-gan Zhang;De-gan Zhang;Si Liu;Xiao-huan Liu;Ting Zhang

  • Novel Quick Start (QS) method for optimization of TCP

    De-Gan Zhang;Ke Zheng;De-Xin Zhao;Xiao-Dong Song

  • Novel PEECR-based clustering routing approach

    De-gan Zhang;Hong-li Niu;Si Liu

  • Optimal Base Station Antenna Downtilt in Downlink Cellular Networks

    Junnan Yang;Ming Ding;Guoqiang Mao;Zihuai Lin

  • A Multi-Path Routing Protocol Based on Link Lifetime and Energy Consumption Prediction for Mobile Edge Computing

    De-Gan Zhang;Lu Chen;Jie Zhang;Jie Chen

  • A new clustering routing method based on PECE for WSN

    De-gan Zhang;Xiang Wang;Xiao-dong Song;Ting Zhang

  • Dynamic Analysis for the Average Shortest Path Length of Mobile Ad Hoc Networks Under Random Failure Scenarios

    Si Liu;De-Gan Zhang;Xiao-huan Liu;Ting Zhang

  • New Method of Energy Efficient Subcarrier Allocation Based on Evolutionary Game Theory

    De-gan Zhang;De-gan Zhang;Chen Chen;Yu-ya Cui;Ting Zhang

  • A new approach and system for attentive mobile learning based on seamless migration

    Unknown

  • Capacity of Cooperative Vehicular Networks with Infrastructure Support: Multi-user Case

    Jieqiong Chen;Guoqiang Mao;Changle Li;Weifa Liang

Frequent Co-Authors

Guoqiang Mao
Guoqiang Mao Xidian University
Jie Zhang
Jie Zhang Nanyang Technological University
Weifa Liang
Weifa Liang City University of Hong Kong
Zihuai Lin
Zihuai Lin University of Sydney
Ming Ding
Ming Ding Commonwealth Scientific and Industrial Research Organisation
Tom H. Luan
Tom H. Luan Xidian University

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