D-Index & Metrics Best Publications
Research.com 2022 Rising Star of Science Award Badge

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 35 Citations 4,586 160 World Ranking 6091 National Ranking 579
Rising Stars D-index 35 Citations 4,683 200 World Ranking 822 National Ranking 295

Research.com Recognitions

Awards & Achievements

2022 - Research.com Rising Star of Science Award

Overview

What is he best known for?

The fields of study he is best known for:

  • Computer network
  • The Internet
  • Artificial intelligence

His main research concerns Mobile device, Distributed computing, Edge computing, Scalability and Computation offloading. His research investigates the connection between Mobile device and topics such as Mobile service that intersect with problems in Dimension, Enhanced Data Rates for GSM Evolution, Computer security and Mobile computing. His Distributed computing research incorporates themes from Quality of service and Job shop scheduling.

His Scalability research is multidisciplinary, incorporating elements of Collaborative filtering and World Wide Web. Lianyong Qi combines subjects such as Services computing and Locality-sensitive hashing with his study of Collaborative filtering. His studies in Computation offloading integrate themes in fields like Genetic algorithm, Computer network and The Internet.

His most cited work include:

  • A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data (128 citations)
  • A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment (116 citations)
  • A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment (116 citations)

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

Lianyong Qi mainly investigates Edge computing, Collaborative filtering, Distributed computing, Web service and World Wide Web. His studies examine the connections between Edge computing and genetics, as well as such issues in Computer network, with regards to Mobile device. His work deals with themes such as Mobile service and Mobile computing, which intersect with Mobile device.

His Collaborative filtering study integrates concerns from other disciplines, such as Locality-sensitive hashing and Service. His Distributed computing study deals with Cloudlet intersecting with Mobile cloud computing. His study looks at the relationship between Web service and topics such as Quality of service, which overlap with Database and Data mining.

He most often published in these fields:

  • Edge computing (21.51%)
  • Collaborative filtering (26.88%)
  • Distributed computing (16.67%)

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

  • Edge computing (21.51%)
  • Enhanced Data Rates for GSM Evolution (15.59%)
  • Computer security (16.13%)

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

Edge computing, Enhanced Data Rates for GSM Evolution, Computer security, Computer network and Recommender system are his primary areas of study. His Edge computing research includes themes of Distributed computing, Resource allocation, The Internet and Server. In his study, which falls under the umbrella issue of Distributed computing, Cloudlet and Mobile device is strongly linked to Scheduling.

His work in the fields of Collaborative filtering overlaps with other areas such as Popularity. He interconnects Locality-sensitive hashing and Artificial intelligence in the investigation of issues within Collaborative filtering. His study in Locality-sensitive hashing is interdisciplinary in nature, drawing from both Quality of service and Web service.

Between 2019 and 2021, his most popular works were:

  • A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems (112 citations)
  • BeCome: Blockchain-Enabled Computation Offloading for IoT in Mobile Edge Computing (103 citations)
  • Joint Optimization of Offloading Utility and Privacy for Edge Computing Enabled IoT (70 citations)

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

  • Computer network
  • The Internet
  • Artificial intelligence

The scientist’s investigation covers issues in Quality of service, Edge computing, Enhanced Data Rates for GSM Evolution, Collaborative filtering and Recommender system. His work carried out in the field of Quality of service brings together such families of science as Database, Service quality, Cloud testing and Scheduling, Job shop scheduling. His Edge computing research integrates issues from Distributed computing, Evolutionary algorithm, The Internet and Server.

His research investigates the link between Enhanced Data Rates for GSM Evolution and topics such as TOPSIS that cross with problems in Data transmission, Multiple-criteria decision analysis, Mobile device and Data mining. He combines topics linked to Locality-sensitive hashing with his work on Collaborative filtering. In his work, Data-driven is strongly intertwined with World Wide Web, which is a subfield of Locality-sensitive hashing.

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

BeCome: Blockchain-Enabled Computation Offloading for IoT in Mobile Edge Computing

Xiaolong Xu;Xuyun Zhang;Honghao Gao;Yuan Xue.
IEEE Transactions on Industrial Informatics (2020)

171 Citations

A computation offloading method over big data for IoT-enabled cloud-edge computing

Xiaolong Xu;Xiaolong Xu;Qingxiang Liu;Yun Luo;Kai Peng.
Future Generation Computer Systems (2019)

168 Citations

A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems

Lianyong Qi;Yi Chen;Yuan Yuan;Shucun Fu.
World Wide Web (2020)

165 Citations

A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data

Lianyong Qi;Xuyun Zhang;Wanchun Dou;Qiang Ni.
IEEE Journal on Selected Areas in Communications (2017)

154 Citations

Privacy-Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment

Wenwen Gong;Lianyong Qi;Lianyong Qi;Yanwei Xu.
Wireless Communications and Mobile Computing (2018)

154 Citations

An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles

Xiaolong Xu;Xiaolong Xu;Yuan Xue;Lianyong Qi;Yuan Yuan.
Future Generation Computer Systems (2019)

151 Citations

A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment

Lianyong Qi;Lianyong Qi;Xuyun Zhang;Wanchun Dou;Chunhua Hu.
Future Generation Computer Systems (2018)

145 Citations

Combining Local Optimization and Enumeration for QoS-aware Web Service Composition

Lianyong Qi;Ying Tang;Wanchun Dou;Jinjun Chen.
international conference on web services (2010)

117 Citations

Deep Learning Models for Real-time Human Activity Recognition with Smartphones

Shaohua Wan;Shaohua Wan;Lianyong Qi;Xiaolong Xu;Chao Tong.
Mobile Networks and Applications (2020)

112 Citations

An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks

Xiaolong Xu;Xiaolong Xu;Yuancheng Li;Tao Huang;Yuan Xue.
Journal of Network and Computer Applications (2019)

107 Citations

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