Daowei Bi

685 citations
26 papers · 561 · h-index 12

Impact in

Papers in

Daowei Bi

24 papers receiving 517 citations

Peers

Daowei Bi
Comparison fields: 5 of 70
  • Computer Networks and Communications 396
  • Electrical and Electronic Engineering 277
  • Computer Vision and Pattern Recognition 84
  • Ocean Engineering 41
  • Artificial Intelligence 90
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Thomas Clouqueur United States
Jay Warrior India
Deyu Lin China
T. Shankar India
Selçuk Ökdem Türkiye
Azlan Awang Malaysia
James Reich United States
Li Cui China
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Citations per year

Countries citing papers authored by Daowei Bi

Since Specialization
Citations

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

Fields of papers citing papers by Daowei Bi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daowei Bi. 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 Daowei Bi. The network helps show where Daowei Bi may publish in the future.

Co-authors

The 11 scholars most cited alongside Daowei Bi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daowei Bi Line = papers co-authored together Daowei Bi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200978
2 200978
3 200776
4 200764
5 200744
6 200736
7 200927
8 200727
9 200727
10 200725
11 200720
12 200713
13 200710
14 20097
15 20075
16 20075
17 20074
18 20083
19 20122
20 20082

About Daowei Bi

Daowei Bi is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence and Control and Systems Engineering, having authored 26 papers that have together received 561 indexed citations. Recurring topics across this work include Energy Efficient Wireless Sensor Networks (23 papers), Indoor and Outdoor Localization Technologies (12 papers), Video Surveillance and Tracking Methods (8 papers), Distributed Sensor Networks and Detection Algorithms (8 papers), Distributed Control Multi-Agent Systems (4 papers), Energy Harvesting in Wireless Networks (3 papers), Security in Wireless Sensor Networks (3 papers) and Fault Detection and Control Systems (3 papers). The work is most often cited by research in Computer Networks and Communications (396 citations), Electrical and Electronic Engineering (277 citations), Computer Vision and Pattern Recognition (84 citations), Ocean Engineering (41 citations) and Artificial Intelligence (90 citations). Daowei Bi has collaborated with scholars based in China and Russia. Frequent co-authors include Xue Wang, Sheng Wang, Junjie Ma, Sheng Wang, Ding Liang, Sheng Wang, Liang Ding, Dong‐Ling Xu, Zheng You and Sheng Wang. Their work appears in journals such as Sensors, IEEE Transactions on Mobile Computing, IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), IEEE Sensors Journal and IEEE Transactions on Instrumentation and Measurement.

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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