What is he best known for?
The fields of study he is best known for:
- Computer network
- The Internet
- Gene
The scientist’s investigation covers issues in Computer network, Wireless, Cognitive radio, Wireless network and Communication channel.
His Computer network research is multidisciplinary, relying on both Telecommunications and Throughput.
Ping Wang has included themes like Network architecture, Electric power, Wireless sensor network and Transmission in his Wireless study.
The concepts of his Cognitive radio study are interwoven with issues in Transmitter, Energy harvesting, Energy and Common value auction.
His work carried out in the field of Wireless network brings together such families of science as Relay and Transmitter power output.
His Mobile computing research integrates issues from Cloudlet and Mobile telephony.
His most cited work include:
- A survey of mobile cloud computing: architecture, applications, and approaches (1701 citations)
- Wireless Networks With RF Energy Harvesting: A Contemporary Survey (1643 citations)
- Wireless Charging Technologies: Fundamentals, Standards, and Network Applications (466 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Computer network, Wireless, Wireless network, Service provider and Cognitive radio.
His Computer network research includes elements of Throughput and Communication channel.
His work in Wireless addresses issues such as Electronic engineering, which are connected to fields such as Transmission.
His Service provider research incorporates themes from Stackelberg competition, Incentive, Computer security and Operations research.
Ping Wang has researched Cognitive radio in several fields, including Scheduling and Mathematical optimization.
His work in Wireless sensor network covers topics such as Key distribution in wireless sensor networks which are related to areas like Wi-Fi array.
He most often published in these fields:
- Computer network (33.95%)
- Wireless (23.40%)
- Wireless network (14.49%)
What were the highlights of his more recent work (between 2019-2021)?
- Incentive (7.66%)
- Stackelberg competition (9.73%)
- Vibration (3.52%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Incentive, Stackelberg competition, Vibration, Wireless and Service provider.
His work in Stackelberg competition addresses subjects such as Backward induction, which are connected to disciplines such as Social network.
His Vibration study combines topics from a wide range of disciplines, such as Frequency response, Energy harvesting and Automotive engineering.
Ping Wang combines subjects such as Relay, Electronic engineering, Computer network and Task with his study of Wireless.
His studies in Computer network integrate themes in fields like Service delivery framework and Game theory.
His studies deal with areas such as Profit and Artificial intelligence as well as Service provider.
Between 2019 and 2021, his most popular works were:
- Influence of specific second phases on corrosion behaviors of Mg-Zn-Gd-Zr alloys (41 citations)
- Influence of specific second phases on corrosion behaviors of Mg-Zn-Gd-Zr alloys (41 citations)
- Dynamic modeling and experimental investigation of self-powered sensor nodes for freight rail transport (31 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- The Internet
- Gene
Ping Wang spends much of his time researching Vibration, Random vibration, Systems architecture, Edge computing and Rationality.
The Random vibration study combines topics in areas such as Generator, Mechanical engineering and Electric power.
His Edge computing research is multidisciplinary, incorporating perspectives in Edge device, Backward induction and Distributed computing.
His Distributed computing study integrates concerns from other disciplines, such as Service, Enhanced Data Rates for GSM Evolution, Hash function, Resource and Protocol.
Among his Rationality studies, you can observe a synthesis of other disciplines of science such as Wireless, Reinforcement learning, Data science, Federated learning and Social Welfare.
His work is dedicated to discovering how Wireless, Incentive compatibility are connected with Profit and other disciplines.
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