Secure Reduced-Dimensional Coding Scheme for Distributed Estimation With Communication Constraints

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-08-28 DOI:10.1109/TSIPN.2025.3603723

Longyu Li;Wen Yang;Yanfang Mo;Wenjie Ding;Jie Wang;Yang Tang

引用次数: 0

Abstract

This paper addresses the problem of secure state estimation in distributed sensor networks with communication constraints. We propose a reduced-dimensional coding scheme based on the PredVAR model, which extracts dynamics from high-dimensional measurements while enhancing communication efficiency and privacy. A distributed estimator is developed under the proposed coding framework, and the impact of dimensionality reduction on estimation performance is analyzed. To defend against adversarial inference, we explicitly model a subspace-based eavesdropper and introduce a lightweight, time-varying perturbation strategy using orthogonal transformations. Simulation results demonstrate the effectiveness of our framework in balancing estimation accuracy, communication efficiency, and resilience against eavesdropping attacks.

查看原文本刊更多论文

具有通信约束的分布式估计安全降维编码方案

研究了具有通信约束的分布式传感器网络的安全状态估计问题。提出了一种基于PredVAR模型的降维编码方案，在提高通信效率和保密性的同时，从高维测量数据中提取动态信息。在该编码框架下开发了分布式估计器，并分析了降维对估计性能的影响。为了防止对抗性推理，我们明确地建立了一个基于子空间的窃听器模型，并使用正交变换引入了一个轻量级的时变摄动策略。仿真结果证明了该框架在平衡估计精度、通信效率和抵御窃听攻击方面的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

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.