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Advancements in Fire Detection Technologies: Innovations for Extreme Environments
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Advancements in Fire Detection Technologies: Innovations for Extreme Environments

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3746

Special Issue Editor

Dr. Hamid Omidvarborna

E-Mail Website
Guest Editor
1. Mobile Source Laboratory Division, California Air Resources Board, 4001 Iowa Avenue, Riverside, CA 92507, USA
2. Department of Civil and Environmental Engineering, University of Toledo, Toledo, OH 43606, USA
Interests: air quality monitoring; sensors; air pollution; environmental monitoring; prediction modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fire detection in extreme conditions presents unique challenges that require specialized sensor technologies capable of operating effectively under harsh environmental circumstances, such as in high temperatures, dense smoke, fluctuating humidity, and with volatile chemical compositions. Traditional fire sensors often struggle in these demanding settings, making the development of advanced, robust detection mechanisms essential. To address these challenges, cutting-edge sensing technologies are being integrated into early fire detection systems, particularly in environments prone to rapid fire spread and limited accessibility. Extreme conditions, such as remote forested areas, complex urban infrastructures, industrial facilities, underground tunnels, offshore platforms, and high-risk sites like nuclear plants or spacecraft, necessitate highly sensitive and resilient detection solutions. These systems must incorporate advanced sensor modules, distributed heat sensors, and chemical detectors designed to withstand intense environmental stress while ensuring consistent and accurate performance. Additionally, fire detection algorithms must be refined to minimize false alarms while accurately identifying genuine fire threats. A key focus of modern fire detection research is the deployment of real-time monitoring solutions using cost-effective, yet highly accurate, air pollution sensors, which can enhance early warning capabilities and provide timely alerts in hazardous conditions. This Special Issue aims to explore the latest advancements in fire detection technologies, highlighting innovations tailored to extreme environments and discussing the future trajectory of intelligent, resilient, and affordable fire sensing solutions.

Dr. Hamid Omidvarborna
Guest Editor

Manuscript Submission Information

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Keywords

  • fire detection
  • extreme conditions
  • advanced sensor technologies
  • harsh environments
  • early warning systems
  • real-time monitoring
  • advanced sensing
  • resilient detection solutions
  • air pollution sensors

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Published Papers (3 papers)

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Research

37 pages, 7330 KB  
Article
A LoRa-Based Multi-Node System for Laboratory Safety Monitoring and Intelligent Early-Warning: Towards Multi-Source Sensing and Heterogeneous Networks
by Haiting Qin, Chuanshuang Jin, Ta Zhou and Wenjing Zhou
Sensors 2025, 25(21), 6516; https://doi.org/10.3390/s25216516 - 22 Oct 2025
Viewed by 400
Abstract
Laboratories are complex and dynamic environments where diverse hazards—including toxic gas leakage, volatile solvent combustion, and unexpected fire ignition—pose serious threats to personnel safety and property. Traditional monitoring systems relying on single-type sensors or manual inspections often fail to provide timely warnings or [...] Read more.
Laboratories are complex and dynamic environments where diverse hazards—including toxic gas leakage, volatile solvent combustion, and unexpected fire ignition—pose serious threats to personnel safety and property. Traditional monitoring systems relying on single-type sensors or manual inspections often fail to provide timely warnings or comprehensive hazard perception, resulting in delayed response and potential escalation of incidents. To address these limitations, this study proposes a multi-node laboratory safety monitoring and early warning system integrating multi-source sensing, heterogeneous communication, and cloud–edge collaboration. The system employs a LoRa-based star-topology network to connect distributed sensing and actuation nodes, ensuring long-range, low-power communication. A Raspberry Pi-based module performs real-time facial recognition for intelligent access control, while an OpenMV module conducts lightweight flame detection using color-space blob analysis for early fire identification. These edge-intelligent components are optimized for embedded operation under resource constraints. The cloud–edge–app collaborative architecture supports real-time data visualization, remote control, and adaptive threshold configuration, forming a closed-loop safety management cycle from perception to decision and execution. Experimental results show that the facial recognition module achieves 95.2% accuracy at the optimal threshold, and the flame detection algorithm attains the best balance of precision, recall, and F1-score at an area threshold of around 60. The LoRa network maintains stable communication up to 0.8 km, and the system’s emergency actuation latency ranges from 0.3 s to 5.5 s, meeting real-time safety requirements. Overall, the proposed system significantly enhances early fire warning, multi-source environmental monitoring, and rapid hazard response, demonstrating strong applicability and scalability in modern laboratory safety management. Full article
(This article belongs to the Special Issue Advancements in Fire Detection Technologies: Innovations for Extreme Environments)
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16 pages, 6847 KB  
Article
Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2
by Dilshod Sharobiddinov, Hafeez Ur Rehman Siddiqui, Adil Ali Saleem, Gerardo Mendez Mezquita, Debora Libertad Ramírez Vargas and Isabel de la Torre Díez
Sensors 2025, 25(20), 6419; https://doi.org/10.3390/s25206419 - 17 Oct 2025
Viewed by 329
Abstract
Forest fires pose significant threats to ecosystems, human life, and the global climate, necessitating rapid and reliable detection systems. Traditional fire detection approaches, including sensor networks, satellite monitoring, and centralized image analysis, often suffer from delayed response, high false positives, and limited deployment [...] Read more.
Forest fires pose significant threats to ecosystems, human life, and the global climate, necessitating rapid and reliable detection systems. Traditional fire detection approaches, including sensor networks, satellite monitoring, and centralized image analysis, often suffer from delayed response, high false positives, and limited deployment in remote areas. Recent deep learning-based methods offer high classification accuracy but are typically computationally intensive and unsuitable for low-power, real-time edge devices. This study presents an autonomous, edge-based forest fire and smoke detection system using a lightweight MobileNetV2 convolutional neural network. The model is trained on a balanced dataset of fire, smoke, and non-fire images and optimized for deployment on resource-constrained edge devices. The system performs near real-time inference, achieving a test accuracy of 97.98% with an average end-to-end prediction latency of 0.77 s per frame (approximately 1.3 FPS) on the Raspberry Pi 5 edge device. Predictions include the class label, confidence score, and timestamp, all generated locally without reliance on cloud connectivity, thereby enhancing security and robustness against potential cyber threats. Experimental results demonstrate that the proposed solution maintains high predictive performance comparable to state-of-the-art methods while providing efficient, offline operation suitable for real-world environmental monitoring and early wildfire mitigation. This approach enables cost-effective, scalable deployment in remote forest regions, combining accuracy, speed, and autonomous edge processing for timely fire and smoke detection. Full article
(This article belongs to the Special Issue Advancements in Fire Detection Technologies: Innovations for Extreme Environments)
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17 pages, 3595 KB  
Article
Sensor-Based Monitoring of Fire Precursors in Timber Wall and Ceiling Assemblies: Research Towards Smarter Embedded Detection Systems
by Kristian Prokupek, Chandana Ravikumar and Jan Vcelak
Sensors 2025, 25(15), 4730; https://doi.org/10.3390/s25154730 - 31 Jul 2025
Viewed by 2748
Abstract
The movement towards low-emission and sustainable building practices has driven increased use of natural, carbon-based materials such as wood. While these materials offer significant environmental advantages, their inherent flammability introduces new challenges for timber building safety. Despite advancements in fire protection standards and [...] Read more.
The movement towards low-emission and sustainable building practices has driven increased use of natural, carbon-based materials such as wood. While these materials offer significant environmental advantages, their inherent flammability introduces new challenges for timber building safety. Despite advancements in fire protection standards and building regulations, the risk of fire incidents—whether from technical failure, human error, or intentional acts—remains. The rapid detection of fire onset is crucial for safeguarding human life, animal welfare, and valuable assets. This study investigates the potential of monitoring fire precursor gases emitted inside building structures during pre-ignition and early combustion stages. The research also examines the sensitivity and effectiveness of commercial smoke detectors compared with custom sensor arrays in detecting these emissions. A representative structural sample was constructed and subjected to a controlled fire scenario in a laboratory setting, providing insights into the integration of gas sensing technologies for enhanced fire resilience in sustainable building systems. Full article
(This article belongs to the Special Issue Advancements in Fire Detection Technologies: Innovations for Extreme Environments)
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