Smart Fire Detection and Deterrent System for Human Savior by Using Internet of Things (IoT)

Abstract

Fire monitoring systems have usually been based on a single sensor such as smoke or flame. These single sensor systems have been unable to distinguish between true and false presence of fire, such as a smoke from a cigarette which might cause the fire alarm to go off. Consuming energy all day long and being dependent on one sensor that might end with false alert is not efficient and environmentally friendly. We need a system that is efficient not only in sensing fire accurately, but we also need a solution which is smart. In order to improve upon the results of existing single sensor systems, our system uses a combination of three sensors to increase the efficiency. The result from the sensor is then analyzed by a specified rule-set using an AI-based fuzzy logic algorithm; defined in the purposed research, our system detects the presence of fire. Our system is designed to make smart decisions based on the situation; it provides feature updated alerts and hardware controls such as enabling a mechanism to start ventilation if the fire is causing suffocation, and also providing water support to minimize the damage. The purposed system keeps updating the management about the current severity of the environment by continually sensing any change in the environment during fire. The purposed system proved to provide accurate results in the entire 15 test performed around different intensities of a fire situation. The simulation work for the SMDD is done using MATLAB and the result of the experiments is satisfactory.

Enhancing Healthcare Efficacy Through IoT-Edge Fusion: A Novel Approach for Smart Health Monitoring and Diagnosis

“…The paper [24]describe "A novel Energy Efficient Message scheduling algorithm EAAFTMS (An Energy-Aware Available and Fault-Tolerant System with Message Scheduling in IoT)" Researchers [25]design an edge node group based on task arrivals and use a decentralized approach to execute tasks in parallel mode in order to finish execution and the IoT based smart fire detection and deterrent system [26]. This study examines the development of AI-enhanced data technologies and how they have affected computing systems [27].This study [28], [29] explores into the topic of Mobile Edge Computing, or MEC, networks, which are comprised of a large number of mobile devices that send their tasks for computing to a combination of edge servers and a central cloud node.IoT networks that use ML to solve security and privacy problems [30], [31] and give the concept of efficient, privacy-preserving medical diagnostic solution [32].…”

An IoT-based Fire Safety Management System for Educational Buildings: A Case Study

“…The authors proposed a future improvement that would use videos rather than photos. The authors in [18] proposed a system that uses multiple sensors to collect readings. An artificial intelligence -based algorithm analyzes and processes the gathered data.…”

Design of a prototype for sending fire notifications in homes using fuzzy logic and internet of things

“…As well as the development of an algorithm with fuzzy logic using images for the detection of forest fires to avoid false alarms [16], [20]. On the other hand, improving the data obtained with sensors and by means of a set of rules using fuzzy logic to detect the presence of fire [17]. The studies mentioned in [15]- [18] argue that better results are obtained by applying fuzzy logic in projects related to IoT with 95% assertiveness, since with this we avoid false alarms and better results are obtained by defining a set of rules that will help to determine if a fire is occurring [21].…”