Aug 2026· SPE Nigeria Annual International Conference and Exhibition· 0 citations· 29 references
Abstract
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.
Mining remains one of the most hazardous industries due to the continuous exposure of workers to toxic gases, extreme environmental conditions, and health-related risks. This paper presents an IoT-enabled Smart Mining Helmet with Health and Safety Monitoring designed to improve worker protection through real-time environmental and physiological monitoring. The proposed system is built around the ESP32 microcontroller and integrates an MQ135 gas sensor for hazardous gas detection, an LM35/DS18B20 temperature sensor for temperature monitoring, a pulse sensor for heart rate measurement, and an IR eye-blink sensor for detecting worker fatigue or unconsciousness. The collected sensor data are processed by the ESP32 and transmitted wirelessly via Wi-Fi to a centralized monitoring system for continuous supervision. Whenever any monitored parameter exceeds predefined safety thresholds, the system immediately activates a buzzer and sends alert notifications, enabling rapid response to potential emergencies. Experimental evaluation demonstrates that the proposed system accurately monitors environmental and physiological conditions while providing reliable real-time alerts under both normal and hazardous scenarios. Compared with conventional mining safety systems, the proposed solution offers integrated health monitoring, continuous remote monitoring, and enhanced emergency response capabilities at a low implementation cost. The developed smart helmet provides an effective, scalable, and reliable solution for improving occupational safety in mining environments and can be extended to other hazardous industries such as construction, oil and gas, and chemical manufacturing.
kalpana V. Yojana rupa, Naga Durga Sahith Jonnalagadda, Harshita Boddepalli et al.· International Journal of Sci...· 0 citations
Workers at landfills have serious occupational risks due to gases emitted from the anaerobic degradation of waste, such as methane (CH₄) and hydrogen sulfide (H₂S), and potential exposure to ionizing radiation from poorly discarded radioactive materials [6][7]. Particle filtering masks do not detect or alert for gas hazards, rather they simply provide protection from particulate emissions. This paper develops a low-cost smart safety mask using an Internet of Things (IoT), wearable platform with an ESP32 microcontroller containing an MQ-2 gas sensor for methane detection and a Geiger-Müller tube for radiation detection. Gas and radiation data are sent wirelessly to a Blynk IoT dashboard and can be accessed and monitored in real-time by Work Supervisors on their mobile devices. A buzzer is activated if either the Methane concentration exceeds 10,000 ppm or if the Radiation dose rate exceeds 0.8 µSv/hr; the buzzer will operate independent of network connectivity. Preliminary prototype testing has demonstrated the adequacy of gas and radiation sensors, threshold detection at the set point, and the ability of the system to remotely alert to workplace hazards. There is currently no low-cost, wearable solution that combines gas detection, radiation detection, local alerting capabilities or IoT connectivity that is appropriate for landfill workers – this indicates a significant gap in the existing literature regarding the safety of workers in the landfill environment [1][2][3]. This system supports United Nations Sustainable Development Goals (SDG) 3, 8, 11, and 12.
N. Neeraj, S. Parihar· Journal of Dynamics and Cont...· 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
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
Sewage workers encounter life-threatening exposures to dangerous gases such as hydrogen sulfide (H 2 S), methane (CH 4), ammonia (NH 3), and carbon monoxide (CO) when performing routine maintenance and cleaning work. Conventional gas detecting systems are based on handheld tools, where active participation of the workers is necessary, thus excluding safety during inactivity or when the workers are busy performing their tasks. This article demonstrates an IoT-Embedded Smart Textile System (IEST) that paves its way into protective clothing that has miniaturized gas sensors built into it so that the concentration of dangerous gases in the air can be continuously monitored in real-time. The suggested system includes a system of electrochemical and metal-oxide semiconductor sensors built in multi-layered fabric, a low-energy Bluetooth Low Energy (BLE) communication chip to transmit data, and a cloud-based data analytics platform to monitor and raise alerts centrally. Another adaptive threshold system modulates the alarm levels according to exposure time and the physiological condition of the workers, minimizing the false alarms and ensuring safety. Experimental testing under simulated sewage conditions has shown that IEST has a detection accuracy of 96.8% to H₂S concentrations as low as 1ppm in sewage with a response time of less than 3 seconds and can be used continuously up to 72 hours on a single battery charge. Six-month field deployment of 150 sewage workers had no gas-related deaths and a 94.2 percent decrease in emergency medical actions over baseline. The system offers sewage workers a non-obtrusive, 24/7 safety system that allows filling the gap between environmental monitoring and personal protective equipment.
S. Mohamed, K. K· 2026 7th International Confe...· 0 citations
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.
N. Benni, S. S, A. G. et al.· International Journal of Sen...· 0 citations
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