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Dergiler / Bitlis Eren Üniversitesi Fen Bilimleri Dergisi / 2026 / Volume: 15 Issue: 2

Lightweight Real-Time Energy Anomaly Detection And Rule-Based Causal Reasoning Using Multi-Tier Edge Computing

Abdulkadir Gozuoglu(TOKAT GAZIOSMANPASA UNIVERSITY, ELECTRICAL AND ELECTRONIC ENGINEERING PR.)0000-0002-6968-379X
Muhammed Elhattab(TOKAT GAZIOSMANPASA UNIVERSITY, ELECTRICAL AND ELECTRONIC ENGINEERING (MASTER))
Sayfa
—
Yayın Tarihi
2026-06-30
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The proposed TinyML-based model achieved a validation accuracy of 94.8% and a precision of 95.2%, demonstrating robust performance in anomaly classification under real-time edge-deployment conditions.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This study uses a multi-tier edge computing architecture to present a lightweight, real-time anomaly detection and Rule-Based Causal reasoning framework for energy systems.
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Temel Bulgular & Veri:The system combines ultra-low-power microcontrollers (ESP32/STM32) at Tier 1 for sensing and on-device TinyML-based anomaly inference, a local RISC-V-based embedded host (Lichee RV Dock) at Tier 2 for Rule-Based Causal analysis and dashboard visualization, and optional cloud platforms (Firebase, ThingsBoard, AWS IoT) at Tier 3 for extended services.
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Sonuç & Katkı:The proposed TinyML-based model achieved a validation accuracy of 94.8% and a precision of 95.2%, demonstrating robust performance in anomaly classification under real-time edge-deployment conditions.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Edge Computing · TinyML · ESP32

Abstract

This study uses a multi-tier edge computing architecture to present a lightweight, real-time anomaly detection and Rule-Based Causal reasoning framework for energy systems. The system combines ultra-low-power microcontrollers (ESP32/STM32) at Tier 1 for sensing and on-device TinyML-based anomaly inference, a local RISC-V-based embedded host (Lichee RV Dock) at Tier 2 for Rule-Based Causal analysis and dashboard visualization, and optional cloud platforms (Firebase, ThingsBoard, AWS IoT) at Tier 3 for extended services. Real-time voltage, current, and temperature data are collected, processed locally, and interpreted using rule-based Causal logic. Experimental results demonstrate the system’s low-latency performance (~0.2s), high anomaly detection precision (95.2%), and effective interpretability in edge deployments. The proposed TinyML-based model achieved a validation accuracy of 94.8% and a precision of 95.2%, demonstrating robust performance in anomaly classification under real-time edge-deployment conditions.

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Bu Makaleye Atıf Yap

Gozuoglu, A., Elhattab, M. (2026). Lightweight Real-Time Energy Anomaly Detection And Rule-Based Causal Reasoning Using Multi-Tier Edge Computing. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Volume: 15 Issue: 2. https://doi.org/10.17798/bitlisfen.1748636

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