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Wavelet-Based Computational Intelligence for Real-Time Anomaly Detection and Fault Isolation in Embedded Systems

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Pubblicato in:Machines vol. 12, no. 9 (2024), p. 664
Autore principale: Pacheco, Jesus
Altri autori: Benitez, Victor H, Pérez, Guillermo, Brau, Agustín
Pubblicazione:
MDPI AG
Soggetti:
Microcontrollers
Accuracy
Deep learning
System reliability
Wavelet transforms
Signal processing
Discrete Wavelet Transform
Unmanned aerial vehicles
Wavelet analysis
Internet of Things
Bias
Machine learning
Data integrity
Embedded systems
Fourier transforms
Neural networks
Sensors
Support vector machines
Classification
Intelligence
Methods
Anomalies
Real time
Cybersecurity
Nuclear power plants
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Abstract:In today’s technologically advanced landscape, sensors feed critical data for accurate decision-making and actions. Ensuring the integrity and reliability of sensor data is paramount to system performance and security. This paper introduces an innovative approach utilizing discrete wavelet transforms (DWT) embedded within microcontrollers to scrutinize sensor data meticulously. Our methodology aims to detect and isolate malfunctions, misuse, or any anomalies before they permeate the system, potentially causing widespread disruption. By leveraging the power of wavelet-based analysis, we embed computational intelligence directly into the microcontrollers, enabling them to monitor and validate their outputs in real-time. This proactive anomaly detection framework is designed to distinguish between normal and aberrant sensor behaviors, thereby safeguarding the system from erroneous data propagation. Our approach significantly enhances the reliability of embedded systems, providing a robust defense against false data injection attacks and contributing to overall cybersecurity.
ISSN:2075-1702
DOI:10.3390/machines12090664
Fonte:Engineering Database