What is he best known for?
The fields of study he is best known for:
- Computer network
- Ecology
- Climate change
Guiling Wang focuses on Climatology, Wireless sensor network, Computer network, Real-time computing and Vegetation.
His Climatology study combines topics from a wide range of disciplines, such as Land cover, Climate change, Climate model and Precipitation.
The concepts of his Wireless sensor network study are interwoven with issues in Distributed algorithm, Key distribution in wireless sensor networks, Mobile wireless sensor network and Mobile computing.
His work on Mobile radio as part of general Computer network research is frequently linked to Voronoi diagram, thereby connecting diverse disciplines of science.
Guiling Wang interconnects Duration, Convolutional neural network and Reinforcement learning in the investigation of issues within Real-time computing.
His Vegetation course of study focuses on Sea surface temperature and Land degradation, Regime shift, Alternative stable state, Holocene and Paleoclimatology.
His most cited work include:
- Movement-assisted sensor deployment (745 citations)
- Movement-assisted sensor deployment (520 citations)
- Sensor relocation in mobile sensor networks (472 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Climatology, Precipitation, Climate model, Computer network and Wireless sensor network.
His Climatology research is multidisciplinary, incorporating perspectives in Land cover, Atmosphere, Climate change and Vegetation.
His Precipitation study combines topics in areas such as Evapotranspiration, Surface runoff, Atmospheric sciences and Water content.
His work carried out in the field of Climate model brings together such families of science as Monsoon, Downscaling and Greenhouse gas.
The study incorporates disciplines such as Wireless network, Distributed computing and Vehicular ad hoc network in addition to Computer network.
His research in Wireless sensor network intersects with topics in Energy consumption, Key distribution in wireless sensor networks, Mobile wireless sensor network, Real-time computing and Scheduling.
He most often published in these fields:
- Climatology (40.38%)
- Precipitation (27.70%)
- Climate model (20.66%)
What were the highlights of his more recent work (between 2018-2021)?
- Climatology (40.38%)
- Blockchain (4.69%)
- Protocol (6.10%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Climatology, Blockchain, Protocol, Computer network and Precipitation.
In his study, Environmental planning and Atmospheric sciences is inextricably linked to Climate change, which falls within the broad field of Climatology.
His work in Protocol addresses issues such as Verifiable secret sharing, which are connected to fields such as Task.
His Computer network study integrates concerns from other disciplines, such as Variety, Database transaction and Upload.
His studies deal with areas such as Training set, Vegetation, Deciduous and Representative Concentration Pathways as well as Precipitation.
His Vegetation research includes elements of Atmosphere, Biosphere, Climate model, Leaf area index and Evapotranspiration.
Between 2018 and 2021, his most popular works were:
- A Deep Reinforcement Learning Network for Traffic Light Cycle Control (90 citations)
- A Deep Learning Model for Transportation Mode Detection Based on Smartphone Sensing Data (9 citations)
- Impact of planting time soil moisture on cereal crop yield in the Upper Blue Nile Basin: A novel insight towards agricultural water management (8 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Ecology
- Artificial intelligence
His main research concerns Artificial intelligence, Water content, Agronomy, Crop and Sorghum.
His research ties Real-time computing and Artificial intelligence together.
Guiling Wang combines subjects such as Data modeling and Systems design with his study of Real-time computing.
His Water content research includes themes of Soil science, Agriculture, Precipitation and Sowing.
Agronomy is represented through his DSSAT, Crop yield, Yield and Growing season research.
The various areas that Guiling Wang examines in his Deep learning study include Control system and Real-time Control System.
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