D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 38 Citations 5,815 199 World Ranking 5111 National Ranking 2528

Overview

What is he best known for?

The fields of study he is best known for:

  • Computer network
  • Operating system
  • Artificial intelligence

His primary areas of study are Big data, Smart grid, Distributed computing, Energy consumption and Computer network. His research in Big data intersects with topics in Edge computing, World Wide Web and Blockchain. His Smart grid research includes themes of Software deployment, Energy management, Distributed database and Computer security, Key.

Kun Wang interconnects Overhead, Scheduling, Analytics, Server and Formal verification in the investigation of issues within Distributed computing. His Energy consumption research is multidisciplinary, relying on both Wireless sensor network, Real-time computing, Cloud computing, Efficient energy use and Reinforcement learning. In the field of Computer network, his study on Public key infrastructure overlaps with subjects such as Data collection.

His most cited work include:

  • Green Industrial Internet of Things Architecture: An Energy-Efficient Perspective (222 citations)
  • A Survey on Energy Internet: Architecture, Approach, and Emerging Technologies (171 citations)
  • Energy big data: A survey (122 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Computer network, Distributed computing, Artificial intelligence, Big data and Computer security. The study incorporates disciplines such as Wireless, Wireless ad hoc network and Wireless network in addition to Computer network. His Distributed computing research integrates issues from Quality of experience, Energy consumption, Scheduling, Cloud computing and Server.

His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Data mining, Computer vision and Pattern recognition. Kun Wang has researched Big data in several fields, including Data modeling, Information privacy, Real-time computing, Edge computing and Mobile device. The various areas that Kun Wang examines in his Computer security study include The Internet, Denial-of-service attack and Smart grid.

He most often published in these fields:

  • Computer network (21.68%)
  • Distributed computing (18.53%)
  • Artificial intelligence (14.34%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (14.34%)
  • Distributed computing (18.53%)
  • Optics (6.64%)

In recent papers he was focusing on the following fields of study:

Kun Wang mainly focuses on Artificial intelligence, Distributed computing, Optics, Antenna and Server. His work carried out in the field of Artificial intelligence brings together such families of science as Task, Computer vision and Pattern recognition. His work deals with themes such as Scheduling and Reinforcement learning, which intersect with Distributed computing.

The Server study combines topics in areas such as Energy consumption and Artificial neural network. His Energy consumption research integrates issues from Real-time computing, Computation offloading and Efficient energy use. Kun Wang focuses mostly in the field of Deep learning, narrowing it down to topics relating to Cloud computing and, in certain cases, Big data.

Between 2019 and 2021, his most popular works were:

  • Big Data Cleaning Based on Mobile Edge Computing in Industrial Sensor-Cloud (56 citations)
  • Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing (37 citations)
  • Renewable Energy-Aware Big Data Analytics in Geo-Distributed Data Centers with Reinforcement Learning (37 citations)

In his most recent research, the most cited papers focused on:

  • Computer network
  • Operating system
  • Artificial intelligence

Kun Wang focuses on Server, Distributed computing, Big data, Energy consumption and Reinforcement learning. The concepts of his Server study are interwoven with issues in Testbed, Computation and Computer security, Honeypot. Kun Wang combines subjects such as Data aggregator, Network packet, Electric power system, Smart grid and Scheduling with his study of Distributed computing.

His Big data research is multidisciplinary, relying on both Data modeling, Wireless sensor network, Information privacy and Cloud computing. His biological study spans a wide range of topics, including Load balancing, Spatial correlation, Overhead and Efficient energy use. His Quality of service study is concerned with Computer network in general.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Green Industrial Internet of Things Architecture: An Energy-Efficient Perspective

Kun Wang;Yihui Wang;Yanfei Sun;Song Guo.
IEEE Communications Magazine (2016)

299 Citations

A Survey on Energy Internet: Architecture, Approach, and Emerging Technologies

Kun Wang;Jun Yu;Yan Yu;Yirou Qian.
IEEE Systems Journal (2018)

248 Citations

Energy big data: A survey

Hui Jiang;Kun Wang;Yihui Wang;Min Gao.
IEEE Access (2016)

180 Citations

Making Big Data Open in Edges: A Resource-Efficient Blockchain-Based Approach

Chenhan Xu;Kun Wang;Peng Li;Song Guo.
IEEE Transactions on Parallel and Distributed Systems (2019)

153 Citations

Strategic Honeypot Game Model for Distributed Denial of Service Attacks in the Smart Grid

Kun Wang;Miao Du;Sabita Maharjan;Yanfei Sun.
IEEE Transactions on Smart Grid (2017)

137 Citations

Mobile big data fault-tolerant processing for ehealth networks

Kun Wang;Yun Shao;Lei Shu;Chunsheng Zhu.
IEEE Network (2016)

136 Citations

Green Resource Allocation Based on Deep Reinforcement Learning in Content-Centric IoT

Xiaoming He;Kun Wang;Huawei Huang;Toshiaki Miyazaki.
IEEE Transactions on Emerging Topics in Computing (2018)

133 Citations

Robust Big Data Analytics for Electricity Price Forecasting in the Smart Grid

Kun Wang;Chenhan Xu;Yan Zhang;Song Guo.
IEEE Transactions on Big Data (2019)

128 Citations

A Game Theory-Based Energy Management System Using Price Elasticity for Smart Grids

Kun Wang;Zhiyou Ouyang;Rahul Krishnan;Lei Shu.
IEEE Transactions on Industrial Informatics (2015)

126 Citations

Wireless Big Data Computing in Smart Grid

Kun Wang;Yunqi Wang;Xiaoxuan Hu;Yanfei Sun.
IEEE Wireless Communications (2017)

125 Citations

Best Scientists Citing Kun Wang

Nadeem Javaid

Nadeem Javaid

COMSATS University Islamabad

Publications: 42

Yan Zhang

Yan Zhang

Chinese Academy of Sciences

Publications: 24

Song Guo

Song Guo

Hong Kong Polytechnic University

Publications: 23

Tian Wang

Tian Wang

Huaqiao University

Publications: 18

Guangjie Han

Guangjie Han

Hohai University

Publications: 17

Mohsen Guizani

Mohsen Guizani

Qatar University

Publications: 16

Lei Shu

Lei Shu

Nanjing Agricultural University

Publications: 15

Victor C. M. Leung

Victor C. M. Leung

University of British Columbia

Publications: 14

Anfeng Liu

Anfeng Liu

Central South University

Publications: 13

Chunsheng Zhu

Chunsheng Zhu

Southern University of Science and Technology

Publications: 11

Sabita Maharjan

Sabita Maharjan

University of Oslo

Publications: 10

Neeraj Kumar

Neeraj Kumar

Thapar University

Publications: 9

Albert Y. Zomaya

Albert Y. Zomaya

University of Sydney

Publications: 8

Wade Trappe

Wade Trappe

Rutgers, The State University of New Jersey

Publications: 8

Xuemin Shen

Xuemin Shen

University of Waterloo

Publications: 8

Nei Kato

Nei Kato

Tohoku University

Publications: 8

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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