Yinren Long

Guangdong University of Technology

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Hongru Ren

Guangdong University of Technology

Renquan Lu

Guangdong University of Technology

Zhijian Cheng

Guangdong University of Technology

Hui Ma

Guangdong University of Technology

Qunyao Yuan

Guangdong University of Technology

Yang Lan

Northwest Normal University

Hongyi Li

Southwest University

Yiyong Xiao

Shanghai Changzheng Hospital

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Guangdong University of Technology

Zhejiang Sci-Tech University

University of Science and Technology of China

Peking University

Fudan University

Huaibei Normal University

Hangzhou Dianzi University

Key Laboratory of Guangdong Province

Shanghai Medical College of Fudan University

Fudan University Shanghai Cancer Center

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Distributed Consensus Estimation for Networked Multi-Sensor Systems under Hybrid Attacks and Missing Measurements

Sensors (2024)

Zhijian Cheng Yang Lan Qunyao Yuan Yinren Long Hongru Ren

Cyber-security research on networked multi-sensor systems is crucial due to the vulnerability to various types of cyberattacks. For the development of effective defense measures, attention is required to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker’s perspective. This paper aims to tackle the problem of distributed consensus estimation for networked multi-sensor systems subject to hybrid attacks and missing measurements. To account for both random denial of service (DoS) attacks and false data injection (FDI) attacks, a hybrid attack model on the estimator-to-estimator communication channel is presented. The characteristics of missing measurements are defined by random variables that satisfy the Bernoulli distribution. Then a modified consensus-based distributed estimator, integrated with the characteristics of hybrid attacks and missing measurements, is presented. For reducing the computational complexity of the optimal distributed estimation method, a scalable suboptimal distributed consensus estimator is designed. Sufficient conditions are further provided for guaranteeing the stability of the proposed suboptimal distributed estimator. Finally, a simulation experiment on aircraft tracking is executed to validate the effectiveness and feasibility of the proposed algorithm.

Cyber-physical system

10.3390/s24134071

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Citations (2)

Distributed Group Coordination of Random Communication Constrained Cyber-Physical Systems Using Cloud Edge Computing

IEEE Transactions on Industrial Cyber-Physical Systems (2024)

Hongru Ren Yinren Long Hui Ma Hongyi Li

Cyber-physical system

10.1109/ticps.2024.3419756

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Manufacturing Big Data Modeling Algorithm Based on GM (1,1) - LSTM and Its Application in Sales Forecasting

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) (2023)

Yinren Long Yiyong Xiao Hongru Ren Renquan Lu

It is a new period for the development of automobile industry, the economic situation is complex and changing, and the policies of automobile industry are frequently issued, so accurate prediction of automobile sales is extremely important and practical for both government and enterprises. In this paper, the GM(1,1) model and the long short-term memory (LSTM) neural network model are combined and optimized, and the sales of a brand of cars from January 2019 to September 2022 are used as sample data, and the car sales in the next three months are predicted by two single models and linear combination forecasting models, respectively. The experimental results show that the linear combined forecasting model outperforms the other two single models in terms of forecasting results and has better resistance to the interference of external factors.

Retail sales

Sample (material)

Economic forecasting

10.1109/ddcls58216.2023.10166094

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