What is he best known for?
The fields of study he is best known for:
- Computer network
- Artificial intelligence
- The Internet
Computer network, Distributed computing, Algorithm, Data mining and Throughput are his primary areas of study.
The Internet is closely connected to Cognitive network in his research, which is encompassed under the umbrella topic of Computer network.
His work on Distance Vector Multicast Routing Protocol, Pragmatic General Multicast and Multicast as part of general Distributed computing research is frequently linked to Energy consumption, bridging the gap between disciplines.
His studies deal with areas such as Network management and Spline interpolation, Interpolation as well as Algorithm.
His Data mining research includes themes of Traffic generation model, Feature extraction and Cellular network.
His study focuses on the intersection of Throughput and fields such as Wireless sensor network with connections in the field of Network packet.
His most cited work include:
- Collaborative Multi-hop Routing in Cognitive Wireless Networks (127 citations)
- A Joint Multi-Criteria Utility-Based Network Selection Approach for Vehicle-to-Infrastructure Networking (123 citations)
- A New Deep-Q-Learning-Based Transmission Scheduling Mechanism for the Cognitive Internet of Things (120 citations)
What are the main themes of his work throughout his whole career to date?
Dingde Jiang mainly investigates Computer network, Distributed computing, Algorithm, Traffic generation model and Data mining.
Dingde Jiang works mostly in the field of Computer network, limiting it down to topics relating to Wireless and, in certain cases, Communication channel and Mobile computing, as a part of the same area of interest.
His work on Xcast, Source-specific multicast and Multicast as part of general Distributed computing study is frequently linked to Energy consumption, bridging the gap between disciplines.
The Algorithm study combines topics in areas such as Simulation, Internet traffic and Artificial intelligence.
He has researched Traffic generation model in several fields, including Network traffic simulation, Anomaly detection, Traffic engineering, Network management and Compressed sensing.
While the research belongs to areas of Data mining, Dingde Jiang spends his time largely on the problem of Artificial neural network, intersecting his research to questions surrounding Robustness.
He most often published in these fields:
- Computer network (36.65%)
- Distributed computing (27.95%)
- Algorithm (27.95%)
What were the highlights of his more recent work (between 2019-2021)?
- Software-defined networking (11.80%)
- Distributed computing (27.95%)
- Computer network (36.65%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Software-defined networking, Distributed computing, Computer network, Service and Edge computing.
His research in Software-defined networking intersects with topics in Forwarding plane, Network topology, Computer architecture and Real-time computing.
In his study, Algorithm is strongly linked to Flexibility, which falls under the umbrella field of Forwarding plane.
His Computation study in the realm of Algorithm interacts with subjects such as Dimension.
His study looks at the relationship between Distributed computing and fields such as Energy, as well as how they intersect with chemical problems.
Dingde Jiang integrates several fields in his works, including Computer network and Scalability.
Between 2019 and 2021, his most popular works were:
- A Compressive Sensing-Based Approach to End-to-End Network Traffic Reconstruction (89 citations)
- Rethinking Behaviors and Activities of Base Stations in Mobile Cellular Networks Based on Big Data Analysis (76 citations)
- Big Data Analysis Based Network Behavior Insight of Cellular Networks for Industry 4.0 Applications (54 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Artificial intelligence
- The Internet
His primary areas of investigation include Software-defined networking, Distributed computing, Big data, Service and Data modeling.
The concepts of his Software-defined networking study are interwoven with issues in World Wide Web, Thesaurus, Search engine, Network measurement and Hidden Markov model.
His work is dedicated to discovering how Distributed computing, Energy are connected with Distributed generation and The Internet and other disciplines.
His Big data study is focused on Data mining in general.
His research integrates issues of Feature and Traffic flow in his study of Data mining.
His Service research is multidisciplinary, relying on both Network architecture and Network emulation.
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