{"title":"State saturated recursive filtering for nonlinear complex networks with energy harvesting sensors and false data injection attacks","authors":"Long Xu, Xueer Bian, Hui Yu, Ling Hou","doi":"10.1002/acs.3803","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>The state saturated recursive filtering issue is researched in this article for nonlinear complex networks with energy harvesting sensors and false data injection (FDI) attacks. In communication networks, the energy of the sensors is used to transmit data from sensors to remote filters. Due to ample energy being a prerequisite for the transmission of data, the energy harvesting technology is provided. During the process of data transmission, the measurement signals may be attacked by the false data. Thereinto, the Bernoulli random variables are used to depict FDI attacks. The primary goal is to devise a filter that minimizes the upper bound for the filtering error covariance. Subsequently, a discussion is shown for the proposed filtering bounded analysis for the filtering error. Finally, a numerical simulation experiment is carried out to demonstrate the applicability and effectiveness for the proposed novel filtering algorithm.</p>\n </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":null,"pages":null},"PeriodicalIF":3.9000,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Adaptive Control and Signal Processing","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/acs.3803","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The state saturated recursive filtering issue is researched in this article for nonlinear complex networks with energy harvesting sensors and false data injection (FDI) attacks. In communication networks, the energy of the sensors is used to transmit data from sensors to remote filters. Due to ample energy being a prerequisite for the transmission of data, the energy harvesting technology is provided. During the process of data transmission, the measurement signals may be attacked by the false data. Thereinto, the Bernoulli random variables are used to depict FDI attacks. The primary goal is to devise a filter that minimizes the upper bound for the filtering error covariance. Subsequently, a discussion is shown for the proposed filtering bounded analysis for the filtering error. Finally, a numerical simulation experiment is carried out to demonstrate the applicability and effectiveness for the proposed novel filtering algorithm.
期刊介绍:
The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material.
Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include:
Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers
Nonlinear, Robust and Intelligent Adaptive Controllers
Linear and Nonlinear Multivariable System Identification and Estimation
Identification of Linear Parameter Varying, Distributed and Hybrid Systems
Multiple Model Adaptive Control
Adaptive Signal processing Theory and Algorithms
Adaptation in Multi-Agent Systems
Condition Monitoring Systems
Fault Detection and Isolation Methods
Fault Detection and Isolation Methods
Fault-Tolerant Control (system supervision and diagnosis)
Learning Systems and Adaptive Modelling
Real Time Algorithms for Adaptive Signal Processing and Control
Adaptive Signal Processing and Control Applications
Adaptive Cloud Architectures and Networking
Adaptive Mechanisms for Internet of Things
Adaptive Sliding Mode Control.