What is he best known for?
The fields of study he is best known for:
- Computer network
- Artificial intelligence
- The Internet
His primary scientific interests are in Computer network, Wireless sensor network, Distributed computing, Network topology and Robustness.
His Computer network research is multidisciplinary, incorporating perspectives in Wireless, Wireless ad hoc network and Real-time computing.
Tie Qiu combines subjects such as Smart city, Scheduling, Job shop scheduling and Heterogeneous network with his study of Wireless sensor network.
While the research belongs to areas of Distributed computing, Tie Qiu spends his time largely on the problem of Collision avoidance system, intersecting his research to questions surrounding Artificial intelligence.
The concepts of his Network topology study are interwoven with issues in Key distribution in wireless sensor networks, Complex network, Crossover and Multi population.
His work carried out in the field of Network packet brings together such families of science as Queue, Throughput and Shortest path problem.
His most cited work include:
- Survey on fog computing (316 citations)
- How Can Heterogeneous Internet of Things Build Our Future: A Survey (158 citations)
- EABS: An Event-Aware Backpressure Scheduling Scheme for Emergency Internet of Things (129 citations)
What are the main themes of his work throughout his whole career to date?
Computer network, Distributed computing, Wireless sensor network, Network packet and Node are his primary areas of study.
His Computer network study deals with Wireless ad hoc network intersecting with Mobile ad hoc network.
His work deals with themes such as Network topology and Robustness, which intersect with Distributed computing.
In most of his Network topology studies, his work intersects topics such as Crossover.
His studies examine the connections between Wireless sensor network and genetics, as well as such issues in Algorithm, with regards to Chaotic.
The Network packet study which covers Scheduling that intersects with Throughput.
He most often published in these fields:
- Computer network (45.21%)
- Distributed computing (23.40%)
- Wireless sensor network (21.81%)
What were the highlights of his more recent work (between 2019-2021)?
- Computer network (45.21%)
- Edge computing (6.38%)
- Robustness (11.70%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Computer network, Edge computing, Robustness, Distributed computing and Network topology.
His Computer network study frequently intersects with other fields, such as The Internet.
His Edge computing study combines topics from a wide range of disciplines, such as Application layer and Mesh networking.
The Robustness study combines topics in areas such as Quality, Reliability engineering, Support vector machine and Location-based service.
His Distributed computing research integrates issues from Wireless sensor network, Server and Mobile computing.
His Network topology research includes themes of Crossover and Reinforcement learning.
Between 2019 and 2021, his most popular works were:
- Recurrent Broad Learning Systems for Time Series Prediction (47 citations)
- A Review on Intelligence Dehazing and Color Restoration for Underwater Images (32 citations)
- Mobile Edge Computing Enabled 5G Health Monitoring for Internet of Medical Things: A Decentralized Game Theoretic Approach (25 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Artificial intelligence
- The Internet
Tie Qiu focuses on Computer network, The Internet, Edge computing, Load balancing and Artificial intelligence.
His Computer network research incorporates themes from Cover, Internet Protocol and Internet protocol suite.
He has included themes like Wireless, Smart contract and Mobile edge computing in his The Internet study.
His research in Edge computing intersects with topics in Systems engineering, Systems architecture, Application layer and Routing protocol.
Tie Qiu has researched Load balancing in several fields, including Prognostics, Latency, Analytics and Motion planning.
His work on Deep learning and Feature is typically connected to Terrain, Scattering and Artificial light as part of general Artificial intelligence study, connecting several disciplines of science.
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