Aug 2026· International Journal of Scientific Research in Engineering & Technology· pp. 167· 0 citations
TL;DR
The proposed solution provides a low-cost, scalable, and efficient approach for improving industrial safety, minimizing manual intervention, and supporting predictive monitoring applications in smart manufacturing environments.
Abstract
Industry 4.0 has accelerated the adoption of Internet of Things (IoT) technologies for intelligent industrial automation, enabling continuous monitoring of critical environmental and operational parameters. This paper presents an IoT-based Industrial Parameters Monitoring System developed using the STM32F446RE microcontroller for real-time monitoring of industrial conditions. The proposed system integrates multiple sensors, including the MQ135 gas sensor, BMP280 pressure and temperature sensor, DHT11 humidity sensor, KY-026 flame sensor, and SW-420 vibration sensor, to collect and process data related to industrial safety and environmental conditions. Sensor data are acquired through ADC, GPIO, and I²C interfaces and displayed locally on a 16×2 LCD. A WE10 Wi-Fi module enables wireless transmission of processed data to the Right Tech Cloud platform through UART communication, facilitating remote monitoring and visualization. The system also incorporates an audible alert mechanism using a buzzer to notify users when abnormal conditions such as gas leakage, fire, or excessive vibration are detected. Experimental evaluation demonstrates reliable real-time data acquisition, cloud connectivity, and continuous monitoring performance. The proposed solution provides a low-cost, scalable, and efficient approach for improving industrial safety, minimizing manual intervention, and supporting predictive monitoring applications in smart manufacturing environments.
Industrial machinery operating in manufacturing and process environments is frequently subjected to adverse operating conditions such as excessive temperature rise, abnormal current consumption, and mechanical vibrations, which may lead to performance degradation, unexpected failures, production losses, and safety hazards. To address these challenges, this paper presents the design and implementation of an Internet of Things (IoT)-enabled real-time machine health monitoring and protection system based on the ESP32 microcontroller platform. The proposed system integrates a DHT11 sensor for temperature and humidity monitoring, an ACS712 Hall-effect sensor for current measurement, and an MPU9250 inertial measurement unit (IMU) for vibration analysis. Sensor data are continuously acquired, processed, and transmitted through Wi-Fi to a cloud-based Firebase Realtime Database, enabling remote access and centralized monitoring. A responsive web dashboard hosted on GitHub Pages provides real-time visualization of machine operating parameters, status indicators, and fault notifications. To enhance operational safety and equipment reliability, threshold-based fault detection algorithms are implemented to identify abnormal operating conditions. When predefined critical limits are exceeded, the ESP32 automatically initiates protective actions by disconnecting the machine through a relay module, activating a visual alarm, and updating the fault status on the cloud platform. The dashboard additionally supports bidirectional communication, allowing authorized operators to remotely restart the machine, while a local push-button interface enables manual system recovery. Furthermore, the developed platform incorporates a browser-based logging mechanism that records timestamped sensor measurements, machine status transitions, fault events, and downloadable CSV trend data for maintenance analysis and performance evaluation. Experimental validation demonstrates reliable real-time monitoring with a data refresh interval of approximately 3 s, accurate threshold-based fault detection, dependable cloud connectivity, and effective remote supervisory control. The proposed solution offers a low-cost, scalable, and practical framework for predictive maintenance and industrial equipment condition monitoring in smart manufacturing environments.
Sudharshana, Kratika V Ulman, Kishan K Kulal et al.· 2026 International Conferenc...· 0 citations
Findings indicate that the proposed framework serves as an innovative prototype for Smart Health management within higher education institutions, aligned with the global Smart Campus paradigm.
S. Janpla, Thanakorn Uiphanit· International Journal of Int...· 0 citations
The design and implementation of an Internet of Things (IoT)-based real-time kitchen monitoring and automation system aimed at enhancing safety, efficiency, and intelligent control within kitchen environments is presented.
Simon Usiju Chagwa, P. B. Zirra· Journal of Analytical and Ap...· 0 citations
Coal mining is a high-risk industrial activity due to the presence of hazardous gases, fire hazards, and unstable environmental conditions that can endanger the lives of workers. Conventional monitoring systems often lack continuous real-time monitoring, remote accessibility, and efficient worker identification, reducing their effectiveness in preventing accidents. This paper presents an IoT-Based Coal Mine Monitoring and Alert System using the STM32F446RE microcontroller to enhance safety in mining environments. The proposed system integrates multiple sensors, including the MQ-4 methane gas sensor, MQ-7 carbon monoxide sensor, DHT11 temperature and humidity sensor, and KY-026 flame sensor, to continuously monitor environmental conditions. An EM-18 RFID reader is employed for worker identification and attendance tracking, while a DS1307 Real-Time Clock (RTC) module provides accurate event timestamping. When any monitored parameter exceeds predefined safety limits, the system activates a buzzer to alert nearby workers immediately. Furthermore, a WE10 Wi-Fi module enables real-time transmission of sensor data to a cloud-based IoT dashboard, allowing remote monitoring, data visualization, and historical analysis. Experimental results demonstrate that the proposed system effectively detects hazardous conditions, provides timely alerts, and supports remote supervision, thereby improving worker safety and operational efficiency. The proposed solution is cost-effective, scalable, and suitable as a prototype for future smart mine safety applications.
Vijaya Sri Andiboyina, Sri Chandrika Tamada Devi, Deepa Sahasra Saragadam et al.· International Journal of Sci...· 0 citations
The server room at the Class I Climatology Station of East Java requires reliable monitoring of temperature, humidity, flame detection, and electrical voltage; however, monitoring is still conducted manually without automatic electrical protection. This study aims to design and implement an Internet of Things (IoT)-based environmental condition monitoring and automatic electrical protection system using an ESP32 integrated with BME280, KY-026, and ZMPT101B sensors. The system applies a two-level threshold structure—warning and critical—to trigger notifications as well as automatic power disconnection via relays. The research methodology includes system design, sensor calibration of the BME280 and ZMPT101B using a comparative method, functional testing of the KY-026, and a 14-day field test. The test results demonstrated a temperature correction of -0.21 °C, a humidity correction of -0.80% RH, and an average voltage error of 1.05%. The system recorded two warning-status readings for humidity without reaching critical conditions and successfully transmitted and stored 114,903 data points during the testing period. The reading interval was designed to be 5 seconds, whereas the actual transmission interval was influenced by network conditions. The results indicate that the system can effectively support environmental monitoring and automatic electrical protection for server rooms.
Rizaldi Wisnu Wardana, Adi Widiatmoko Wastumirad, Benyamin Heryanto Rusanto et al.· Internet of Things and Artif...· 0 citations
This study aims to develop an Internet of Things (IoT)-based electrical energy (kWh) monitoring system using an ESP32 microcontroller integrated with a web monitoring platform. The system employs ACS712 current and ZMPT101B voltage sensors to measure voltage, current, power, and electrical energy in real time. The research method used is Research and Development (R&D) with the 4D model, consisting of define, design, development, and dissemination stages. The test results indicate that the ZMPT101B sensor has an error rate of 0.49%–0.81%, while the ACS712 sensor has an error rate of 4.0%–14.2%. Measurement data were successfully transmitted and displayed on the monitoring website through an internet connection. The developed system is capable of providing real-time electrical energy monitoring with adequate accuracy and easy accessibility for users.
