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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%.

Large-scale industrial IoT services appear in smart factory domains such as factory clouds which integrate distributed small factories into a large virtual factory with dynamic combination based on orders of consumers. A smart factory has so many industrial elements including various sensors/actuators, gateways, controllers, application servers, and IoT clouds. Since there are complex connections and relations, it is hard to handle them in point-to-point manner. In addition, many duplicated traffics are exchanged between them through the Internet. Multicast is believed as an effective many-to-many communication mechanism by establishing multicast trees between sources and receivers. ere are, however, some issues for adopting multicast to large-scale industrial IoT services in terms of QoS. In this paper, we propose a novel software-defined network multicast based on group shared tree which includes near-receiver rendezvous point selection algorithm and group shared tree switching mechanism. As a result, the proposed multicast mechanism can reduce the packet loss by 90% compared to the legacy methods under severe congestion condition. GST switching method obtains to decreased packet delay effect, respectively, 2%, 20% better than the previously studied multicast and the legacy SDN multicast.

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