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Article

Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

Computer Science and Engineering, Koreatech University, Cheonan-si 31253, Korea
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Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 2025; https://doi.org/10.3390/s19092025
Received: 31 January 2019 / Revised: 24 April 2019 / Accepted: 27 April 2019 / Published: 30 April 2019
(This article belongs to the Special Issue Smart IoT Sensing)
A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%. View Full-Text
Keywords: fire detection; dependability; IoT; artificial intelligence; distributed MQTT; SDN fire detection; dependability; IoT; artificial intelligence; distributed MQTT; SDN
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MDPI and ACS Style

Park, J.H.; Lee, S.; Yun, S.; Kim, H.; Kim, W.-T. Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework. Sensors 2019, 19, 2025. https://doi.org/10.3390/s19092025

AMA Style

Park JH, Lee S, Yun S, Kim H, Kim W-T. Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework. Sensors. 2019; 19(9):2025. https://doi.org/10.3390/s19092025

Chicago/Turabian Style

Park, Jun H., Seunggi Lee, Seongjin Yun, Hanjin Kim, and Won-Tae Kim. 2019. "Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework" Sensors 19, no. 9: 2025. https://doi.org/10.3390/s19092025

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