| Issue |
E3S Web Conf.
Volume 714, 2026
2026 4th International Forum on Clean Energy Engineering (FCEE2026)
|
|
|---|---|---|
| Article Number | 04002 | |
| Number of page(s) | 13 | |
| Section | Data-Driven Prediction and Monitoring for Intelligent Energy Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202671404002 | |
| Published online | 08 June 2026 | |
Real-Time IoT Monitoring and Automatic Cut-Off System for Battery Storage in Public Emergency Lighting
Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Surabaya, 60231, Indonesia
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Existing emergency lighting systems lack remote diagnostic capability and network-resilient monitoring mechanisms. Prior studies have not addressed key aspects such as data persistence, automated cut-off response, and IoT reliability under fluctuating connectivity conditions. This research introduces an IoT-based battery monitoring architecture for emergency lighting to support Affordable and Clean Energy and Sustainable Cities and Communities through enhanced reliability, efficiency, and intelligent energy management. The proposed system employs an ESP32 microcontroller and INA219 voltage sensor, programmed using C++ and integrated with the Blynk IoT platform for real-time visualization. To mitigate unstable network scenarios, a retry transmission algorithm and EEPROM-based data buffering were implemented to ensure continuity of reporting and prevent measurement loss. System performance was evaluated based on voltage precision—benchmarked against a calibrated multimeter—and communication robustness under varying bandwidth and latency profiles. Experimental results indicate stable measurement performance, with deviations ranging between 0.15–0.30 V, confirming suitability for battery-state assessment. The prototype successfully enables uninterrupted remote monitoring, improves operational safety, and contributes to sustainable energy supervision. Future research will address system scalability, automated response functions, and advanced anomaly detection for broader deployment.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

