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Figure from:A Fuzzy Decision in Smart Fire and Home Security System

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Looking at the hardware aspect of the system, the Gas Sensor MQ-2, temperature sensor (TMP-36) and PIR sensor are used as input devices to the system. These sensors monitor and continuously give feedback to the micro-controller (Arduino Uno R3) for processing. The output side is the warning systems which include by the means of SMS and voice calls. Firstly, the operational logic is calibrated using fuzzy logic considering the inputs to the system which are gas/smoke concentration, temperature and motion detection as well as the output alarm variable is defined with appropriate range of values. Figure | shows the fuzzy system modeling.
Looking at the hardware aspect of the system, the Gas Sensor MQ-2, temperature sensor (TMP-36) and PIR sensor are used as input devices to the system. These sensors monitor and continuously give feedback to the micro-controller (Arduino Uno R3) for processing. The output side is the warning systems which include by the means of SMS and voice calls. Firstly, the operational logic is calibrated using fuzzy logic considering the inputs to the system which are gas/smoke concentration, temperature and motion detection as well as the output alarm variable is defined with appropriate range of values. Figure | shows the fuzzy system modeling.
Fig. 4. SIMULINK model of the system IOI OIE IIE Fig. 3 shows the output from the system bases on input values of gas and temperature. The yellow shaded region is where the alarm is triggered. For the gas and smoke, the alarm is triggered when the sensor value pass 3. Likewise for temperature, the alarm is triggered when the temperature value increases above 28 degrees Celsius. The curvature of the graph shows how the fuzzy system determines output based on the rules. Fig. 4 shows the modeling diagram of the entire system including fuzzy controller in the loop. The modeling result examines the overall system performance with various conditions and results the decision for alarm.
Fig. 4. SIMULINK model of the system IOI OIE IIE Fig. 3 shows the output from the system bases on input values of gas and temperature. The yellow shaded region is where the alarm is triggered. For the gas and smoke, the alarm is triggered when the sensor value pass 3. Likewise for temperature, the alarm is triggered when the temperature value increases above 28 degrees Celsius. The curvature of the graph shows how the fuzzy system determines output based on the rules. Fig. 4 shows the modeling diagram of the entire system including fuzzy controller in the loop. The modeling result examines the overall system performance with various conditions and results the decision for alarm.
The sensitivity range of the PIR sensor in a Wall mount unit is much better than a Ceiling mount unit as the detecting zone distance decreases. According to the PIR sensor datasheet, maximum distance of detecting zone from ceiling is just 2.4m. A wall mount unit can detect in a range of 5 to 6 meters and is very reliable. Gas and smoke are less dens¢ than air so they would definitely rise up towards the ceiling. The system should be mounted in such a way on the wal! so that it can detect gas/smoke together with motion. Fig. 5 shows the actual system built for proposed objectives anc the SMS message that will be sent during the warning alert. Apart from this SMS, a voice call would be made on the resister mobile number. Once the person receives the call, a sound (speech) will be played, for example, “Some intrudet at home” for the person to hear. Therefore, the person can take appropriate action and inform the local authorities remotely. The simulated results are similar to the results obtained during prototyping phase. The system has behavec accordingly, matching the results of simulations. Some minor technical issues were noticed. One was related to PIR motion sensor range of detection. At some instances the sensor remained detected motion and remained high for some time even if there was no motion present. When the alarm is sounded, GSM is activated and it does its task of sending SMS and making voice call to family members. The actual time of event is recoded using real-time clock feature: embedded in proposed smart device.
The sensitivity range of the PIR sensor in a Wall mount unit is much better than a Ceiling mount unit as the detecting zone distance decreases. According to the PIR sensor datasheet, maximum distance of detecting zone from ceiling is just 2.4m. A wall mount unit can detect in a range of 5 to 6 meters and is very reliable. Gas and smoke are less dens¢ than air so they would definitely rise up towards the ceiling. The system should be mounted in such a way on the wal! so that it can detect gas/smoke together with motion. Fig. 5 shows the actual system built for proposed objectives anc the SMS message that will be sent during the warning alert. Apart from this SMS, a voice call would be made on the resister mobile number. Once the person receives the call, a sound (speech) will be played, for example, “Some intrudet at home” for the person to hear. Therefore, the person can take appropriate action and inform the local authorities remotely. The simulated results are similar to the results obtained during prototyping phase. The system has behavec accordingly, matching the results of simulations. Some minor technical issues were noticed. One was related to PIR motion sensor range of detection. At some instances the sensor remained detected motion and remained high for some time even if there was no motion present. When the alarm is sounded, GSM is activated and it does its task of sending SMS and making voice call to family members. The actual time of event is recoded using real-time clock feature: embedded in proposed smart device.