Smart Mining Helmet with Health & Safety Monitoring using ESP32 and IoT

IoT-Based Smart Safety Helmet for Real-Time Health Monitoring and Hazard Detection in Oil and Gas Field Operations

Workers in the oil and gas industry, construction, and mining are routinely exposed to life-threatening hazards that existing safety systems are too slow and too limited to address. Traditional safety approaches rely on manual reporting and passive physical protection, leaving critical gaps in real-time detection and emergency response. This paper presents the design, development, and evaluation of an Internet of Things (IoT) powered smart safety helmet, named the BEYOND HELMET, built specifically for oil and gas field workers in Nigeria. The system integrates an ESP32 microcontroller, an MPU-6050 inertial measurement unit for fall detection, an MQ-7 gas sensor for carbon monoxide monitoring, a DHT22 temperature and humidity sensor, a SEN-11574 pulse rate sensor for heart rate monitoring, a NEO-6M GPS module for precise location tracking, a SIM800L GSM module for SMS-based emergency alerts, an ESP32-CAM camera for visual confirmation, and a 16x2 LCD display for local status output. All sensor data are processed onboard and transmitted in real time to supervisors through an IoT monitoring platform structured in JavaScript Object Notation (JSON) format. Testing results indicate that the system achieves fall detection accuracy of approximately 95%, carbon monoxide hazard detection accuracy of approximately 90%, and abnormal heart rate detection accuracy of approximately 92%. Emergency alerts are dispatched in under 10 seconds compared to the 10 to 20 minutes typical of manual reporting systems, representing a response time reduction of 70 to 85%. The prototype was assembled at a total cost of approximately 43,400 Nigerian Naira, making it highly affordable and scalable for large industrial deployments. The results demonstrate that the BEYOND HELMET offers a comprehensive, cost-effective, and proactive safety solution for workers in hazardous environments.

IOT-Based Coal Mine Monitoring and Alert System using STM32F446RE

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.

Industrial Parameters Monitoring 4.0

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.

DEVELOPMENT OF AN IOT-BASED GAS MONITORING SYSTEM USING ARTIFICIAL NEURAL NETWORKS ON ESP32 FOR INDUSTRIAL SAFETY APPLICATIONS

IoT-Based Smart Flood Monitoring and Early Warning System

Floods pose a significant hazard in India, causing severe damage to life, property, and the economy. Existing flood monitoring systems often suffer from delayed response, limited coverage, and high costs. The objective of this study is to design and implement a low-cost, real-time IoT-based smart flood monitoring and early warning system that can improve prediction accuracy and provide timely alerts to minimize flood impacts The proposed system integrates multiple sensors-ultrasonic for water level, water flow sensors, and DHT22 for temperature and humidity-with an Arduino Uno microcontroller. Data is transmitted to the ThingSpeak cloud platform using the ESP8266 Wi-Fi module and visualized via the ThingView mobile application. A GSM module sends SMS alerts to authorities and residents when threshold conditions are detected. The system was simulated using Proteus Professional to verify performance, and individual modules were tested for accuracy and responsiveness. The proposed system overcomes limitations of traditional flood monitoring approaches by enabling automated, continuous, and low-cost sensing with cloud-based data access. Multisensor integration reduces false alarms compared to single-parameter systems. While the Wi-Fi + GSM approach provides effective coverage for urban and semi-urban areas, rural deployments may require extended-range communication protocols. Security enhancements and machine learning integration are recommended for predictive analytics and robust performance in div This IoT-based flood monitoring and early warning system provides a scalable, affordable, and effective solution for real-time flood risk management. By integrating multiple environmental parameters, cloud storage, and multi-channel alerts, it significantly improves upon existing methods. The architecture offers a strong foundation for future enhancements, including AIdriven prediction models and secure data transmission protocols, to further strengthen disaster preparedness and response.

Design and Implementation of a Real-time Smart Saline Monitoring, Detection, and GSM-based Emergency Notification System for Modern Healthcare Environments

In modern healthcare systems, continuous patient monitoring plays a vital role in ensuring safety and timely medical intervention. One of the common challenges in hospitals is the manual monitoring of intravenous (IV) saline bottles, which often leads to human error, delayed response, and potential health risks such as blood backflow and air embolism. To address these issues, this paper presents a Smart Saline Monitoring System using a load cell and GSM module for real-time and automated saline level monitoring. The proposed system employs a load cell sensor to accurately measure the weight of the saline bottle, which directly corresponds to the fluid level. The sensed data is processed using a microcontroller, where it is continuously compared with predefined threshold values. When the saline level falls below the critical limit, the system automatically triggers an alert mechanism and sends an SMS notification to the medical staff through the GSM module, ensuring immediate attention without the need for constant supervision. The system is designed to be cost-effective, reliable, and easy to integrate into existing hospital infrastructure. Experimental evaluation demonstrates that the system provides accurate measurements and quick response times under various conditions. By reducing manual workload and enhancing monitoring efficiency, the proposed solution significantly improves patient safety and operational efficiency in healthcare environments. This paper presents a Smart Saline Monitoring System based on a load cell sensor and GSM communication to achieve accurate and real-time monitoring of intravenous (IV) fluid levels. The system measures the weight of the saline bottle using a load cell interfaced with an HX711 precision amplifier. The system operates with a response time of less than 5 seconds and achieves measurement accuracy within ±2% after calibration. Experimental validation confirms reliable performance under varying load conditions. The proposed system significantly reduces manual intervention, enhances patient safety, and provides a low-cost, scalable solution suitable for real-time healthcare monitoring applications.