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%.

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

“…Advanced AI surveillance technologies, enabled by motion detection, predictive analytics, drones, and other autonomous devices, can be used to monitor urban areas, recognize threats, such as crime [72,93,100,148], fraud [109], accidents, and fire [101,123,160]. On a broader scale, AI can be used to monitor communication networks and recognize potential terrorist threats, trafficking, crime syndicates, and other illegal behavior [100].…”

“…Given the ability of AI to analyze large amounts of data, scenarios to deal with potential threats could be constructed simultaneous to the detection of threats [161]. This would give decision-makers and other authorities more time to respond to threats such as natural disasters [132], house fires [123], or other incidents. Furthermore, AI can be used to assess the extent of damage caused by these events helping authorities better respond to, and mitigate any damage caused [101,132].…”

A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

“…Other authors have carried out related relevant research, such as [35], in which fire smoke was detected from video sources; [36], which showed a fire detection system based on artificial intelligence; [37], which detected terrorist actions on videos; [38,39], that showed novel applications to object detection; [40,41], that showed an excellent tracking applications; [42] in which a Real-Time video analysis was made from several sources with interesting results in object tracking; [43] which proposed a secure framework for IoT Systems Using Probabilistic Image Encryption; [44] which showed an Edge-Computing Video Analytics system deployed in Liverpool, Australia; [45] where GPUs and Deep Learning were used for traffic prediction; [46] where a video monitor and a radiation detector in nuclear accidents were shown; [47] where an Efficient IoT-based Sensor Big Data system was detailed.…”

DM-MQTT: An Efficient MQTT Based on SDN Multicast for Massive IoT Communications

“…Emergent-MQTT identifies emergency data and reserves bandwidth, but does not consider data concentration issues. In order to solve the problem of data concentration, Park proposed Direct-MQTT [ 26 ]. Direct-MQTT has made a broker-less path to reduce transfer delays.…”