What is he best known for?
The fields of study he is best known for:
- Computer network
- Artificial intelligence
- Statistics
Fan Wu mainly focuses on Computer network, Incentive, Wireless, Distributed computing and Data quality.
Computer network connects with themes related to Game theory in his study.
His Incentive study integrates concerns from other disciplines, such as Service provider, Common value auction and Profit.
His work carried out in the field of Wireless brings together such families of science as Radio spectrum and Beamforming.
His Distributed computing research includes elements of Provisioning and Routing protocol.
He combines subjects such as Overhead, Data mining and k-anonymity with his study of Data quality.
His most cited work include:
- Spatial Reusability-Aware Routing in Multi-Hop Wireless Networks (166 citations)
- Pay as How Well You Do: A Quality Based Incentive Mechanism for Crowdsensing (147 citations)
- Millimeter-Wave Wireless Communications for IoT-Cloud Supported Autonomous Vehicles: Overview, Design, and Challenges (121 citations)
What are the main themes of his work throughout his whole career to date?
Fan Wu spends much of his time researching Computer network, Distributed computing, Wireless, Computer security and Wireless network.
His studies deal with areas such as Channel allocation schemes, Communication channel and Throughput as well as Computer network.
His research investigates the connection with Distributed computing and areas like Static routing which intersect with concerns in Destination-Sequenced Distance Vector routing.
Wireless is closely attributed to Radio spectrum in his research.
His work on Nash equilibrium expands to the thematically related Wireless network.
In his study, White spaces and Energy consumption is inextricably linked to Real-time computing, which falls within the broad field of Wireless sensor network.
He most often published in these fields:
- Computer network (35.02%)
- Distributed computing (22.74%)
- Wireless (19.13%)
What were the highlights of his more recent work (between 2018-2021)?
- Wireless (19.13%)
- Distributed computing (22.74%)
- Operations research (5.05%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Wireless, Distributed computing, Operations research, Artificial intelligence and Overhead.
He studies Wireless network which is a part of Wireless.
His Federated learning study in the realm of Distributed computing interacts with subjects such as Software deployment.
The study incorporates disciplines such as Incentive, Opportunity cost, Competitive analysis and Mechanism design in addition to Operations research.
His Incentive study combines topics from a wide range of disciplines, such as Data quality, Budget constraint and Risk analysis.
Fan Wu usually deals with Mobile relay and limits it to topics linked to Computer network and Privacy preserving.
Between 2018 and 2021, his most popular works were:
- Unsupervised Fake News Detection on Social Media: A Generative Approach (65 citations)
- Design and analysis of authenticated key agreement scheme in cloud-assisted cyber–physical systems (34 citations)
- Achieving Data Truthfulness and Privacy Preservation in Data Markets (17 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Artificial intelligence
- Statistics
His primary areas of study are Distributed computing, Operations research, Differential privacy, Leverage and Upload.
His Distributed computing study incorporates themes from Missing data and Minification.
His work in Differential privacy addresses subjects such as Scalability, which are connected to disciplines such as Scheduling, Algorithm, Distributed algorithm, Approximation algorithm and Mobile sensor networks.
He has researched Leverage in several fields, including Generative grammar, Fake news and Data science.
He combines topics linked to Profit with his work on Computer security.
He has included themes like Wireless and Mathematical optimization in his Robustness study.
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