Laboratories are environments that often involve hazardous materials and sensitive equipment, making safety a top priority. Traditional hazard detection methods rely heavily on manual supervision and periodic checks, which can be slow, inefficient, and prone to human error. This project proposes the development of an IoT-based real-time monitoring system aimed at enhancing laboratory safety by continuously detecting and alerting users of potential hazards such as gas leaks, fire, smoke, and abnormal temperature or humidity levels.
The system integrates various environmental sensors (e.g., gas, temperature, humidity, smoke) with a microcontroller (such as ESP32 or NodeMCU) connected to a wireless network. Sensor data is transmitted in real time to an IoT platform where it is visualized and monitored. When critical thresholds are breached, the system instantly sends alerts via mobile applications, emails, or SMS, allowing for timely intervention. Additionally, the system supports remote access and historical data logging to improve preventive maintenance and safety analysis. The proposed solution is low-cost, scalable, and suitable for academic, research, and industrial laboratory environments, significantly improving hazard detection and response times.
Introduction
Laboratories are critical sites for scientific discovery but contain various hidden hazards such as toxic gases, flammable chemicals, heat, and electrical risks. Traditional safety methods, relying on manual checks and isolated alarms, often fail to detect dangers promptly. The advent of Internet of Things (IoT) technology enables continuous, real-time monitoring through interconnected sensors, allowing immediate detection of hazardous conditions like gas leaks or temperature spikes, with instant alerts sent remotely.
IoT systems also collect extensive data over time, helping identify patterns, improve safety protocols, and ensure regulatory compliance. However, designing an effective, adaptable, and user-friendly IoT hazard monitoring system for diverse lab environments presents challenges.
The paper proposes an IoT-based system combining gas, smoke, temperature, and humidity sensors connected to a microcontroller that sends data to a cloud platform for remote monitoring and instant alerts. Tested in a controlled lab, the system proved reliable in early hazard detection, reducing human error and enhancing safety.
The related work section reviews various IoT-based hazard detection and safety systems, including fire suppression, machine learning prediction, environmental control, and robotic automation in laboratories and industrial settings.
The system design includes layered architecture: sensor networks capturing environmental data, local processing by microcontrollers for real-time response, communication via Wi-Fi or LoRaWAN, cloud platform integration for data storage and analytics, and user interfaces for alert management. The design emphasizes modularity and scalability to adapt to different lab needs.
Mathematical models support decision-making by setting hazard thresholds, sensor data fusion, anomaly detection, probability estimation, and communication range prediction.
Conclusion
Ensuring safety in laboratories is no longer just about routine checks or relying on manual supervision—it demands intelligent, responsive systems that can monitor and react in real time. This paper presented the development of an IoT-based solution aimed at addressing that very need by continuously tracking environmental conditions and responding to potential hazards such as gas leaks, fires, and abnormal temperature levels.The system we designed integrates reliable sensors, a microcontroller for local processing, and cloud-based services for data storage, real-time updates, and user notifications. This layered approach ensures that alerts are generated instantly when danger is detected and allows users to monitor laboratory conditions remotely through a simple web or mobile interface. The result is not just a smart alert system, but a proactive safety network that reduces risks and helps safeguard both people and infrastructure.What makes this approach especially effective is its scalability and flexibility. Whether it’s a small academic lab or a larger industrial research facility, the system can be customized with different sensors or communication protocols to suit the specific risks present in each environment. Its modular structure also opens up opportunities for future enhancements, such as integrating AI for predictive analysis or blockchain for secure data records.Overall, this project demonstrates how accessible IoT technologies can be leveraged to create meaningful, real-world impact. By shifting from reactive safety practices to proactive, data-driven monitoring, we move one step closer to safer, smarter laboratory environments. This work serves as a foundation for further research and practical implementation in various sectors where real-time hazard detection is critical.
References
[1] IoT-Based Smart Laboratory Monitoring System (2021)In this work, Sharma et al. proposed an IoT-based architecture that monitored temperature, humidity, and the presence of toxic gases in research laboratories. The system leveraged Wi-Fi communication and a mobile application to send real-time alerts to lab personnel. While effective in its scope, the system lacked predictive capabilities or extended integration like cloud analytics.
[2] Hazardous Gas Detection Using Wireless Sensor Networks (2020)Kumar and Sinha developed a distributed network of gas sensors that communicated via ZigBee to detect toxic gas leaks in industrial labs. The paper emphasized sensor calibration and fault tolerance but did not focus on integrating other hazard types or remote monitoring through cloud-based dashboards.
[3] LoRa-Based Environmental Monitoring System (2022)A study by Nair et al. utilized LoRaWAN for long-range, low-power communication in environmental monitoring systems. Their findings were useful in selecting communication protocols for areas with limited connectivity, such as remote labs. The study supports adopting LoRa as a reliable alternative to Wi-Fi in some laboratory environments.
[4] Design and Implementation of a Fire and Gas Detection System (2021)Singh and Reddy presented a fire and gas detection system using Arduino and MQTT protocols. Alerts were sent via SMS and email. The research highlighted the importance of threshold tuning and user notification in real time, which is a critical aspect adopted in our system as well.
[5] Real-Time IoT Framework for Indoor Air Quality Monitoring (2023)Chakraborty et al. developed an IoT-based platform that used air quality sensors and a web dashboard to monitor CO?, PM2.5, and temperature in indoor workspaces. Their system emphasized user interaction through dashboards, an idea we expanded by integrating it with hazard alerts in laboratories.
[6] A Smart Safety System for Industrial Workplaces (2022)Gupta et al. proposed a wearable sensor system for industrial environments that tracked gas exposure and worker movement. Though not focused on laboratories, their framework provided insights into combining safety protocols and automation, which our system incorporates using motion sensors and alarms.
[7] IoT-Based Fire Detection and Suppression System (2020)Deshmukh and Patel designed a fire detection system integrated with IoT and automated extinguishing units. Though our system does not include suppression, the architecture for early detection and cloud alerts serves as a strong foundation for our design.
[8] Integration of Cloud and Edge Computing in IoT Safety Systems (2023)Recent work by Liu et al. explored a hybrid cloud-edge model to reduce latency in safety-critical IoT systems. Their results supported the idea of processing sensor data locally while still enabling cloud-based visualization, aligning well with the hybrid design of our monitoring system.