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Related Experiment Video

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LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems.

Alexios Papaioannou^1,2, Asimina Dimara^1,2,3, Charalampos S Kouzinopoulos¹

¹Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece.

Sensors (Basel, Switzerland)

|April 13, 2024

Summary

This study introduces an AI malfunction detection module for low-power embedded systems. The novel approach significantly enhances fault tolerance and maintenance, achieving over 98% performance in real-world tests.

Keywords:

IoT anomaly detection lightweight machine learning low-power embedded systems malfunction detection prescriptive maintenance resources’ optimization

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Area of Science:

Computer Engineering
Embedded Systems
Artificial Intelligence

Background:

Low-power embedded systems are crucial for data collection and exchange, but face maintenance challenges due to fault prediction and monitoring limitations.
Existing resource management frameworks lack specific protocols for low-power embedded system failures.
Identifying failures and implementing customized reaction mechanisms remain complex.

Purpose of the Study:

To address the maintenance and fault detection gaps in low-power embedded systems.
To propose a trilateral framework including stakeholder guidance, automated control, and AI-driven malfunction detection.
To enhance the reliability and fault tolerance of these systems.

Main Methods:

Development of a trilateral framework with periodic stakeholder prescriptions and automated control mechanisms.
Implementation of a backup AI malfunction detection module.
Design and development of three novel autonomous embedded systems using ARM Cortex cores for evaluation.

Main Results:

The AI malfunction detection module demonstrated outstanding performance in real-life testing.
Evaluation metrics consistently exceeded 98% across the developed embedded systems.
The approach proved effective in enhancing fault tolerance and maintenance capabilities.

Conclusions:

The proposed AI malfunction detection module significantly improves the reliability of low-power embedded systems.
The trilateral framework offers a comprehensive solution for system maintenance and failure prevention.
The developed approach is highly effective and reliable for critical embedded applications.