Paper Info
Title
Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework.
Abstract
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%.
Year
DOI
Venue
2019
10.3390/s19092025
SENSORS
Keywords
Field
DocType
fire detection,dependability,IoT,artificial intelligence,distributed MQTT,SDN
Dependability,Data transmission,Latency (engineering),Fuzzy logic,Internet of Things,Minification,Artificial intelligence,MQTT,Engineering,Fire detection
Journal
Volume
Issue
ISSN
19
9
1424-8220
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Jun Hong Park100.34
Seunggi Lee200.34
Seongjin Yun300.34
Hanjin Kim4587.15
Won Tae Kim56112.27