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 32 Citations 4,851 229 World Ranking 7502 National Ranking 690

Overview

What is he best known for?

The fields of study he is best known for:

  • Computer network
  • Operating system
  • The Internet

Computer network, Distributed computing, Data mining, Wireless sensor network and Radio-frequency identification are his primary areas of study. Computer network and Computer security are commonly linked in his work. His study in Distributed computing is interdisciplinary in nature, drawing from both Web service, Object, Network topology, Real-time computing and Tree network.

His Data mining research is multidisciplinary, relying on both Scalability, Pattern recognition, Cluster analysis and Parallel computing. His studies in Wireless sensor network integrate themes in fields like Attack analysis, Smart city and Network security. His study looks at the relationship between Radio-frequency identification and fields such as Protocol, as well as how they intersect with chemical problems.

His most cited work include:

  • Trust mechanisms in wireless sensor networks: Attack analysis and countermeasures (261 citations)
  • Big Data Processing in Cloud Computing Environments (190 citations)
  • How Can Heterogeneous Internet of Things Build Our Future: A Survey (158 citations)

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

His main research concerns Computer network, Distributed computing, Cloud computing, Real-time computing and Data mining. His studies deal with areas such as Wireless and The Internet as well as Computer network. His study focuses on the intersection of Distributed computing and fields such as Cache with connections in the field of Mobile computing.

His research integrates issues of Resource and Latency in his study of Cloud computing. Keqiu Li works mostly in the field of Data mining, limiting it down to topics relating to Protocol and, in certain cases, Identification, as a part of the same area of interest. His Wireless sensor network study integrates concerns from other disciplines, such as Sensor node and Key distribution in wireless sensor networks.

He most often published in these fields:

  • Computer network (35.35%)
  • Distributed computing (29.62%)
  • Cloud computing (10.51%)

What were the highlights of his more recent work (between 2017-2021)?

  • Computer network (35.35%)
  • Distributed computing (29.62%)
  • Scheduling (8.60%)

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

His primary areas of investigation include Computer network, Distributed computing, Scheduling, Cloud computing and Artificial intelligence. Computer network and The Internet are frequently intertwined in his study. His Distributed computing research includes themes of Network performance, Service, Testbed, Optimization problem and Speedup.

His studies examine the connections between Scheduling and genetics, as well as such issues in Quality of service, with regards to Queueing theory. His research investigates the link between Cloud computing and topics such as Latency that cross with problems in Workload and Stochastic modelling. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Computer vision and Pattern recognition.

Between 2017 and 2021, his most popular works were:

  • How Can Heterogeneous Internet of Things Build Our Future: A Survey (158 citations)
  • Binary Hashing for Approximate Nearest Neighbor Search on Big Data: A Survey (49 citations)
  • SIGMM: A Novel Machine Learning Algorithm for Spammer Identification in Industrial Mobile Cloud Computing (36 citations)

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

  • Computer network
  • Operating system
  • The Internet

Keqiu Li mainly focuses on Computer network, Distributed computing, Testbed, Scheduling and Wireless. His work deals with themes such as Aloha, The Internet and Robustness, which intersect with Computer network. His Distributed computing research is multidisciplinary, incorporating elements of Bandwidth management, Bandwidth, Bandwidth allocation, Service and Channel allocation schemes.

His study explores the link between Testbed and topics such as Approximation algorithm that cross with problems in Multiplexing, Optimization problem and Speedup. He has researched Wireless in several fields, including Activity recognition, Communication channel, Data collection and Human–computer interaction. Keqiu Li interconnects Network architecture, Wireless mesh network, Smart city and Home automation in the investigation of issues within Wireless sensor network.

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

Trust mechanisms in wireless sensor networks: Attack analysis and countermeasures

Yanli Yu;Keqiu Li;Wanlei Zhou;Ping Li.
Journal of Network and Computer Applications (2012)

414 Citations

Big Data Processing in Cloud Computing Environments

Changqing Ji;Yu Li;Wenming Qiu;Uchechukwu Awada.
international symposium on pervasive systems, algorithms, and networks (2012)

404 Citations

How Can Heterogeneous Internet of Things Build Our Future: A Survey

Tie Qiu;Ning Chen;Keqiu Li;Mohammed Atiquzzaman.
IEEE Communications Surveys and Tutorials (2018)

215 Citations

Optimized big data K-means clustering using MapReduce

Xiaoli Cui;Pingfei Zhu;Xin Yang;Keqiu Li.
The Journal of Supercomputing (2014)

186 Citations

Energy-efficient and high-accuracy secure data aggregation in wireless sensor networks

Hongjuan Li;Kai Lin;Keqiu Li.
Computer Communications (2011)

174 Citations

Heterogeneous ad hoc networks

Tie Qiu;Ning Chen;Keqiu Li;Daji Qiao.
ad hoc networks (2017)

168 Citations

Performance Guaranteed Computation Offloading for Mobile-Edge Cloud Computing

Xiaoyi Tao;Kaoru Ota;Mianxiong Dong;Heng Qi.
IEEE Wireless Communications Letters (2017)

158 Citations

An effective solution for trademark image retrieval by combining shape description and feature matching

Heng Qi;Keqiu Li;Yanming Shen;Wenyu Qu.
Pattern Recognition (2010)

111 Citations

Energy Consumption in Cloud Computing Data Centers

Awada Uchechukwu;Keqiu Li;Yanming Shen.
ieee international conference on cloud computing technology and science (2014)

102 Citations

Collaborative Multi-Tier Caching in Heterogeneous Networks: Modeling, Analysis, and Design

Xiuhua Li;Xiaofei Wang;Keqiu Li;Zhu Han.
IEEE Transactions on Wireless Communications (2017)

94 Citations

Best Scientists Citing Keqiu Li

Tie Qiu

Tie Qiu

Tianjin University

Publications: 42

Victor C. M. Leung

Victor C. M. Leung

Shenzhen University

Publications: 20

Zhu Han

Zhu Han

University of Houston

Publications: 18

Bin Xiao

Bin Xiao

Hong Kong Polytechnic University

Publications: 17

Dusit Niyato

Dusit Niyato

Nanyang Technological University

Publications: 17

Jie Wu

Jie Wu

Temple University

Publications: 16

Jiannong Cao

Jiannong Cao

Hong Kong Polytechnic University

Publications: 16

Jiangchuan Liu

Jiangchuan Liu

Simon Fraser University

Publications: 14

Mianxiong Dong

Mianxiong Dong

Muroran Institute of Technology

Publications: 13

Keqin Li

Keqin Li

State University of New York

Publications: 12

Sanglu Lu

Sanglu Lu

Nanjing University

Publications: 12

Shigang Chen

Shigang Chen

University of Florida

Publications: 12

Yang Xiang

Yang Xiang

Swinburne University of Technology

Publications: 11

Joel J. P. C. Rodrigues

Joel J. P. C. Rodrigues

Federal University of Piauí

Publications: 11

Kun Wang

Kun Wang

University of California, Los Angeles

Publications: 11

Kaoru Ota

Kaoru Ota

Muroran Institute of Technology

Publications: 11

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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