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Probability-Guaranteed Distributed Secure Estimation for Nonlinear Systems over Sensor Networks under Deception Attacks on Innovations
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
Citation information: DOI10.1109/TSIPN.2021.3097217, IEEE Transactions on Signal and Information Processing over Networks10.13039/501100004608-Natural Science Foundation of Jiangsu Province (Grant Number: BK20190021);
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61773209, 61973102, 61873148, 61933007 and 71801196); 10.13039/501100010014-Six Talent Peaks Project in Jiangsu Province (Grant Number: XYDXX-033); Alexander von Humboldt Foundation of Germany
Adaptive Distributed Outlier Detection for WSNs
The paradigm of pervasive computing is gaining more and more attention nowadays, thanks to the possibility of obtaining precise and continuous monitoring. Ease of deployment and adaptivity are typically implemented by adopting autonomous and cooperative sensory devices; however, for such systems to be of any practical use, reliability and fault tolerance must be guaranteed, for instance by detecting corrupted readings amidst the huge amount of gathered sensory data. This paper proposes an adaptive distributed Bayesian approach for detecting outliers in data collected by a wireless sensor network; our algorithm aims at optimizing classification accuracy, time complexity and communication complexity, and also considering externally imposed constraints on such conflicting goals. The performed experimental evaluation showed that our approach is able to improve the considered metrics for latency and energy consumption, with limited impact on classification accuracy