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.
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