Journal
SENSORS
Volume 19, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/s19092025
Keywords
fire detection; dependability; IoT; artificial intelligence; distributed MQTT; SDN
Funding
- Institute for Information & communications Technology Promotion (IITP) [2018-0-01456-002]
- National Research Foundation of Korea [2017R1A2B4010875]
- Korean government (MSIP)
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2018-0-01456-002] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2017R1A2B4010875] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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%.
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