A5040687697- Distributed systems and fault tolerance
- Blockchain Technology Applications and Security
- Age of Information Optimization
- Energy Load and Power Forecasting
- Power Systems and Renewable Energy
- IoT and Edge/Fog Computing
- Advanced Neural Network Applications
- Autonomous Vehicle Technology and Safety
- Smart Grid and Power Systems
- Vehicle License Plate Recognition
- Vehicular Ad Hoc Networks (VANETs)
- Optimization and Search Problems
University of Electronic Science and Technology of China
2023-2024
North China Electric Power University
2023
University of Glasgow
2020-2023
Practical Byzantine Fault Tolerance (PBFT) consensus mechanism shows a great potential to break the performance bottleneck of Proof-of-Work (PoW)-based blockchain systems, which typically support only dozens transactions per second and require minutes hours for transaction confirmation. However, due frequent inter-node communications, PBFT has poor node scalability thus it is adopted in small networks. To enable large systems such as massive Internet Things (IoT) ecosystems blockchain, this...
Vital societal and industrial autonomous components are increasingly interconnected through communication networks to complete critical tasks cooperatively. However, as the reliability trust requirements for connected systems continue grow, centralized decision approaches that in use today reaching their limits. Focusing on driving applications, this paper proposes a resilient trustworthy framework wireless distributed consensus networks, where links less reliable or even presence of...
This paper investigates the multi-valued fault-tolerant distributed consensus problem that pursues exact output. To this end, voting validity, which requires output of non-faulty nodes to be plurality input nodes, is investigated. Considering a specific distribution votes, we first give impossibility results and tight lower bound system tolerance achieving agreement, termination validity. A practical algorithm satisfies validity in Byzantine fault model proposed subsequently. ensure...
In order to improve the accuracy of wind power prediction, a prediction method based on time series decomposition and error correction is proposed in this paper. Firstly, maximum information coefficient (MIC) used select features with strong correlation power, so as reduce complexity original data; Then, according non-stationary characteristics decomposed into several stationary subsequences by using adaptive noise complete set empirical mode (CEEMDAN); Finally, convolution network (TCN)...