{"title":"Distributed $H_\\infty$ Secure Fusion Estimation for Energy-Constrained Multi-Sensor Systems Under Hybrid Attacks","authors":"Haiyu Song;Linyi Chen;Bo Chen;Wen-An Zhang;Li Yu","doi":"10.1109/TSIPN.2025.3594164","DOIUrl":null,"url":null,"abstract":"This paper investigates the distributed <inline-formula><tex-math>$H_{\\infty }$</tex-math></inline-formula> secure fusion estimation problem for energy-constrained multi-sensor systems subject to hybrid attacks. Given the limited energy supply, sensor nodes operate in two modes: high-energy mode, which ensures robust security during information transmission, and low-energy mode, which makes transmissions more vulnerable to hybrid attacks. The phenomenon of hybrid attacks is described as the stochastic occurrence of false data injection (FDI) and denial-of-service (DoS) attacks in the communication channels from sensors to local estimators. To handle these challenges, we propose a novel distributed <inline-formula><tex-math>$H_{\\infty }$</tex-math></inline-formula> secure fusion estimation model designed specifically for energy-constrained multi-sensor systems under hybrid attacks scenarios. Subsequently, sufficient conditions are derived to ensure that the secure fusion estimation error system achieves exponential mean-square stability and <inline-formula><tex-math>$H_{\\infty }$</tex-math></inline-formula> performance level. Additionally, the design of optimal fusion weight matrices is addressed. Finally, the effectiveness of the proposed distributed <inline-formula><tex-math>$H_{\\infty }$</tex-math></inline-formula> secure fusion estimation method is demonstrated through an illustrative example.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"994-1004"},"PeriodicalIF":3.0000,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Signal and Information Processing over Networks","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11109059/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
This paper investigates the distributed $H_{\infty }$ secure fusion estimation problem for energy-constrained multi-sensor systems subject to hybrid attacks. Given the limited energy supply, sensor nodes operate in two modes: high-energy mode, which ensures robust security during information transmission, and low-energy mode, which makes transmissions more vulnerable to hybrid attacks. The phenomenon of hybrid attacks is described as the stochastic occurrence of false data injection (FDI) and denial-of-service (DoS) attacks in the communication channels from sensors to local estimators. To handle these challenges, we propose a novel distributed $H_{\infty }$ secure fusion estimation model designed specifically for energy-constrained multi-sensor systems under hybrid attacks scenarios. Subsequently, sufficient conditions are derived to ensure that the secure fusion estimation error system achieves exponential mean-square stability and $H_{\infty }$ performance level. Additionally, the design of optimal fusion weight matrices is addressed. Finally, the effectiveness of the proposed distributed $H_{\infty }$ secure fusion estimation method is demonstrated through an illustrative example.
期刊介绍:
The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.