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%.
Highlights
Dependable fire detection systems with high accuracy and promptness are essential for the safety of smart city services
Since the existing fire detection systems use rule-based algorithms or algorithms, there are low detection accuracy problems caused by different environmental conditions, simple artificial intelligence (AI) algorithms, there are low detection accuracy problems caused by different environmental in addition to little usage of heterogeneous sensors
The legacy systems have satisfying promptness requirements because they do not consider the transfer delays of fire events difficulty satisfying promptness requirements because they do not consider the transfer delays of fire caused by traffic congestion on a specific node or a server
Summary
Dependable fire detection systems with high accuracy and promptness are essential for the safety of smart city services. The existing fire detection systems use rule-based algorithms with static parameters that make it difficult to accurately detect fire in dynamic situations [7]. Fire detection systems require suitable thresholds with specific parameters depending on the system’s installation environment. Sensors 2019, 19, 2025 applying rule-based algorithms, but it is impossible for normal users to calculate these thresholds and apply them to their systems. Decision delays depend on the hardware performance and the complexity of the fire decision algorithms installed on the fire detection systems. Combined and analyzed complex fire sensor data using multi-functional AI framework to improve fire detection accuracy. The conclusion and future works regarding the fire detection system are provided
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