IoT-Enabled Mist Fire Suppression Systems: A Comprehensive Review of Modeling Techniques and Real-Time Applications

Abstract

With ongoing urban development and modernization of industries, the risk of fire hazards is becoming more complicated and widespread. Traditional approaches to fire suppression systems have become obsolete in a world ever more complex, moving at the speed of modern industry and urban environments that change in seconds. Such systems tend to respond slowly and are ineffective in adaptive and responsive environments. On the other hand, IoT-based systems are promising because they can detect, suppress fires, and respond in real time through automated control and intelligent feedback. The purpose of this paper is to review IoT-based automated mist fire suppression systems. This is a review paper that integrates the automated mist fire suppression system with IoT technologies. This paper tells how speed, accuracy and detection improve on using advanced sensor data, microcontroller logic and network detection. Like Vasily Novozhilov\'s foundational work in spray dynamics and computational fluid dynamics (CFD) modeling [1], it also covers the theoretical framework of suppression mechanism - specifically including Eulerian-Lagrangian and Eulerian-Eulerian approaches. The challenges are also discussed related to mist optimization, system scalability with the implementation assumptions and preparatory requirements. In conclusion, this paper highlights the importance of transformative potential of merging fluid dynamic principles with IoT automation to create predictive, adaptive, and efficient fire suppression strategies in smart environments.

Introduction

1. Challenges of Modern Fire Safety

Modern infrastructure like skyscrapers and industrial plants presents complex fire safety challenges due to advanced technologies, sensitive equipment, and intricate building designs. Traditional fire suppression methods, such as sprinklers and chemical agents, struggle with delayed activation, excessive water damage, and environmental risks.

2. Rise of IoT in Fire Suppression

The integration of Internet of Things (IoT) has transformed fire detection and suppression:

• Real-time monitoring

• Automated adaptive response

• Distributed sensor networks

• Data-driven analytics to predict fire behavior
  IoT systems can distinguish between fire types, adjust responses based on occupancy or asset value, and work with other building systems.

3. Water Mist Technology

Water mist systems release very fine droplets (<1000 microns) that:

• Absorb heat efficiently

• Displace oxygen via steam generation

• Attenuate thermal radiation
  These systems minimize collateral damage compared to traditional sprinklers and are more environmentally friendly.

4. Theoretical and Computational Foundations

• Key mechanisms of mist suppression: heat absorption, oxygen displacement, and thermal radiation attenuation.

• Vasily Novozhilov’s work in Computational Fluid Dynamics (CFD) has advanced nozzle design and mist dynamics understanding.

• CFD modeling approaches:

  • Eulerian-Lagrangian: tracks individual droplets, offers precision but is computationally expensive.

  • Eulerian-Eulerian: treats both mist and air as continuous phases, suited for large-scale modeling with less detail.

5. IoT and Smart Sensor Networks

IoT systems for fire safety rely on:

• Smart sensor placement

• Data fusion techniques like Bayesian inference, Kalman filters

• Machine learning algorithms for adaptive decision-making

• Integration with building systems for dynamic, situational responses

6. Machine Learning in Fire Detection

Machine learning enables accurate fire detection and response by:

• Reducing false alarms via pattern recognition

• Using algorithms like SVMs, Random Forests, Neural Networks

• Applying deep learning (e.g., CNNs) for image-based fire detection

• Leveraging ensemble methods for better accuracy

7. Edge Computing & Real-Time Processing

To meet strict response times:

• Edge computing processes data locally

• Devices must balance power efficiency and computational capacity

• Distributed processing across edge devices enables fast, reliable decision-making

8. Predictive Analytics & Anomaly Detection

Advanced analytics help predict fire risks and detect system anomalies using:

• Time-series models like ARIMA and LSTM

• Unsupervised learning for detecting unknown anomalies

• Statistical control methods to monitor system drift or changes

9. Challenges and Future Directions

• Data scarcity: Fire events are rare, making ML training difficult

• Interpretability: ML systems must be explainable and trustworthy

• Cybersecurity: Vulnerabilities to hacking and adversarial attacks

• Regulatory barriers: Standards need to evolve for AI-based fire systems

• Resource constraints: Edge devices have limited computing power

Conclusion

The adoption of advanced sensing technologies, intelligent control algorithms, and advanced suppression mechanisms adds remarkable value to fire safety, as highlighted in the recent review of the IoT-based fire suppression system. The convergence of Internet of Things capabilities with established water mist suppression principles creates unprecedented opportunities for developing adaptive, efficient, and environmentally responsible fire protection systems that address many limitations of conventional approaches. The theoretical foundations established through computational fluid dynamics modeling, particularly building upon Vasily Novozhilov\'s seminal work in spray dynamics, provide essential understanding of mist suppression mechanisms that enable optimization of system performance. The integration of Eulerian-Lagrangian and Eulerian-Eulerian modeling approaches with real-time sensor data creates opportunities for predictive and adaptive suppression strategies that were previously impossible with conventional systems. Machine learning and deep learning algorithms offer powerful tools for improving fire detection accuracy, reducing false alarms, and optimizing suppression strategies. However, the application of these technologies in safety-critical systems requires careful consideration of reliability, explainability, and security concerns. The ability to control complex systems is a not fully explored area with a lot of potential, especially when discussing the algorithm frameworks with proven resilience for fire emergency scenarios. The striking inequality laid out in the literature calls for further exploration and works. Real time GPS data alignment with computed flow dynamics (CFD), gaps in standards and systems integration, prediction of system failure over time, and evaluation of the expenditures like cost, market value, and demand. Filling these gaps will be vital for the practical deployment of IoT-powered fire suppression systems in the field. The proposed approach provides a framework for developing comprehensive IoT-enabled mist fire suppression systems that address the key challenges and limitations identified in the literature review. The merging of sophisticated processing algorithms, intelligent mechanisms for adaptive suppression, and diverse sensor networks improves fire protection capabilities while still being cost-efficient and meeting regulatory compliance. IoT-enabled fire mist suppression systems can positively impact the economic returns with reduced fire damage, lower insurance costs, and improved operational efficiency, and especially in high-value situations where conventional systems can lead to significant collateral damage. Although, resolving successful results needs system design, installation, and system-wide reliable maintenance throughout the lifecycle. Combined with the efficiency IoT systems provide, the environmental advantages of water mist systems aid in the reduction of negative environmental impact as well as global sustainability objectives. These systems, as an alternative to traditional suppression systems, can greatly aid in the reduction of environmental costs and help appease rising concerns of climate change and resource conservation. Bridging these gaps will be important for realizing real-world IoT-based applications of fire suppression systems. From the literature review, the most decisive IoT-enabled mist fire suppression systems gaps have been addressed, forming the basis of the presented approach. A diverse range of sensor networks, intelligent processing algorithms, and adaptive suppression systems in the IoT mists fire suppression systems offer the opportunity for better fire protection at a reasonable cost and with compliance to regulations. Significant benefits also include reduced fire damage, decreased insurance cost, and increased operational efficiency. For high-value applications that suffer high collateral damage cost during conventional suppression, IoT-enabled mist suppression systems gain a positive return on investment. Achieving these benefits depends on the thorough system design, attention to the installation process, and systematic maintenance for enduring reliable function during the operational lifecycle. Considering the integration of broader smart city infrastructures with IoT devices and their related systems poses an emerging challenge which when achieved, will coordination of smart resource management, emergency management, and associated utilities management. Such incorporation must still be evaluated in the scopes of cybersecurity, privacy, and other wide problem domains of system design and implementation strategies. Further in-depth studies must be conducted to enhance the understanding of the theoretical frameworks concerning the working mechanisms alongside mist suppression systems. Especially in the context of economically viable real-world technology systems. The adoption of the modern fire suppression systems will require the development of the regularized protocols, additional fire-dealing cybersecurity systems, rigorous testing protocols, and validation process frameworks. The synergy of automation and the fluid dynamic principles IoT merges these devices together can serve wider purposes more/building safety and multilevel economization of environmental controls. The ongoing efforts and the accomplished fire and emergency suppression systems will ensure that these solutions will always serve the greater purposes of life and safety, increase system operational and life cycle efficiency while protect the environment. Focused investigations together with diligent application and continuous improvement make it possible to realize the vision of intelligent, adaptive and efficient fire protection that IoT-enabled mist fire suppression systems provide, responding to changing conditions in real time while conserving resources and collateral damage. The unique interplay between well-known principles of fire suppression and IoT technologies provides new possibilities for enhancing fire safety within our complex and interconnected infrastructure.

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