What is he best known for?
The fields of study he is best known for:
- Computer network
- Artificial intelligence
- The Internet
Lei Yang focuses on Distributed computing, Real-time computing, Computer network, Mobile cloud computing and Server.
His studies in Distributed computing integrate themes in fields like Dynamic programming, Information privacy, Smart grid and System monitoring.
His work deals with themes such as Exploit, Smart meter, Tracking and Mobile RFID, which intersect with Real-time computing.
His research in Mobile RFID intersects with topics in Object, Focus and Task.
His Computer network research incorporates elements of Radio-frequency identification, Throughput, Cognitive radio, Communication channel and Stackelberg competition.
His Mobile computing course of study focuses on Mobile broadband and Scalability.
His most cited work include:
- Tagoram: real-time tracking of mobile RFID tags to high precision using COTS devices (460 citations)
- A framework for partitioning and execution of data stream applications in mobile cloud computing (321 citations)
- Multi-User Computation Partitioning for Latency Sensitive Mobile Cloud Applications (136 citations)
What are the main themes of his work throughout his whole career to date?
Lei Yang mainly investigates Distributed computing, Computer network, Artificial intelligence, Scheduling and Real-time computing.
His Distributed computing research also works with subjects such as
- Mobile device that intertwine with fields like Mobile computing,
- Edge device that intertwine with fields like Edge computing.
Lei Yang combines subjects such as Wireless, Radio-frequency identification, Scalability, Communication channel and Nash equilibrium with his study of Computer network.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Computer vision.
The study incorporates disciplines such as Wireless network and Flow network in addition to Scheduling.
Lei Yang has researched Real-time computing in several fields, including Embedded system and Smart grid.
He most often published in these fields:
- Distributed computing (21.46%)
- Computer network (14.15%)
- Artificial intelligence (12.20%)
What were the highlights of his more recent work (between 2019-2021)?
- Distributed computing (21.46%)
- Edge device (4.88%)
- Computer network (14.15%)
In recent papers he was focusing on the following fields of study:
Lei Yang spends much of his time researching Distributed computing, Edge device, Computer network, Edge computing and Enhanced Data Rates for GSM Evolution.
The various areas that Lei Yang examines in his Distributed computing study include Overhead, Home automation, Wireless sensor network, Cluster analysis and Swarm intelligence.
The concepts of his Edge device study are interwoven with issues in Bandwidth and Emerging technologies.
His Computer network research is multidisciplinary, incorporating perspectives in Wireless, Scalability and Distributed database.
His Edge computing research includes themes of Scheduling, Network congestion, The Internet and Flow network.
As a member of one scientific family, Lei Yang mostly works in the field of Enhanced Data Rates for GSM Evolution, focusing on Benchmark and, on occasion, Mobile device, Resource and Deep learning.
Between 2019 and 2021, his most popular works were:
- Blockchain for Future Smart Grid: A Comprehensive Survey (32 citations)
- Intelligent Edge: Leveraging Deep Imitation Learning for Mobile Edge Computation Offloading (12 citations)
- Restoring Connectivity of Damaged Sensor Networks for Long-Term Survival in Hostile Environments (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- The Internet
Lei Yang mainly focuses on Distributed computing, Wireless sensor network, Edge computing, Data modeling and Leverage.
His work on Distributed algorithm as part of general Distributed computing research is often related to Distributed learning, thus linking different fields of science.
His Wireless sensor network study introduces a deeper knowledge of Computer network.
He has included themes like Swarm intelligence, Ant colony optimization algorithms, Cluster analysis and Cache in his Edge computing study.
His Data modeling research includes elements of Algorithm and Online algorithm.
His work carried out in the field of Leverage brings together such families of science as Real Time Digital Simulator, Optimal control and Reinforcement learning.
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