Self-Sustaining Embedded Edge-Vision Node for Industrial OSH Monitoring: Fusing YOLOv8n Compliance Matrix with Active-Cooled Dual-Axis Photovoltaic Tracking

Alfarid Hendro Yuwono; Mahendra Widyartono; As'ad Shidqy Aziz; Deshinta Arrova Dewi; Usman Nurhasan; Vugar Hacimahmud Abdullayev; Ana Nuril Achadiyah; Nur Vidia Laksmi B.

doi:10.12928/biste.v8i5.17209

Authors

Alfarid Hendro Yuwono State University of Surabaya
Mahendra Widyartono State University of Surabaya
As'ad Shidqy Aziz State University of Surabaya
Deshinta Arrova Dewi INTI International University
Usman Nurhasan State Polytechnic of Malang
Vugar Hacimahmud Abdullayev Azerbaijan State Oil and Industry University
Ana Nuril Achadiyah Unisma Polytechnic of Malang
Nur Vidia Laksmi B. State University of Surabaya

DOI:

https://doi.org/10.12928/biste.v8i5.17209

Keywords:

Dual-Axis Solar Tracker, Embedded Vision, Energy-Neutral IoT, PPE Compliance, Cyber-Physical Systems Decent Job, Economic Growth, Clean Energy Technology

Abstract

Deploying edge-computing nodes for automated Occupational Safety and Health (OSH) monitoring in remote industrial zones faces bottlenecks due to grid absence and high energy demands of continuous deep learning inference. Conversely, outdoor solar infrastructures suffer from efficiency degradation caused by static positioning and thermal stress. To address these interconnected challenges, this study proposes a self-sustaining cyber-physical edge-vision framework integrating a deep learning pipeline with an intelligent renewable energy control system, advancing SDG 8 (Decent Job) and (Economic Growth) and SDG 7 (Clean Energy Technology). The processing core leverages a lightweight YOLOv8n paradigm on a Raspberry Pi 4 to track six Personal Protective Equipment (PPE) categories via strict rule-based logical conjunctions (AND gates) for real-time compliance evaluation. To ensure power autonomy, the edge node is integrated with a 100 WP polycrystalline photovoltaic array governed by an ESP32 microcontroller executing dual-axis tracking and active cooling. Empirical validations demonstrate that while the localized pipeline maintains a throughput of 8.4–11.6 FPS, its decision integrity degrades under severe occlusions, dropping to 16.6% accuracy with an 83.3% False Alarm Rate (FAR). Crucially, the dual-axis tracking and active cooling system mitigates thermal losses, elevating photovoltaic efficiency to 17.58% and delivering an average output of 179.35 Wh. This significantly outperforms static setups (15.30% efficiency, 146.83 Wh), compensating for computational load without grid dependence. This work establishes a baseline for architectural codesign in autonomous, resilient, and energy-neutral industrial IoT deployments.

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