Optimization Of Sugeno Fuzzy Logic Based On Wireless Sensor Network In Forest Fire Monitoring System

Forest fires are a phenomenon of natural disasters that often occur in Indonesia and are a local and global concern. Forest fires that occur today are caused by two main factors namely natural factors and uncontrolled human activity factors. Therefore in this research is to find ways to reduce fores...

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Published in2020 2nd International Conference on Industrial Electrical and Electronics (ICIEE) pp. 126 - 134
Main Authors Budiyanto, Setiyo, Medriavin Silalahi, Lukman, Artadima Silaban, Freddy, Darusalam, Ucuk, Andryana, Septi, Fajar Rahayu, I. M
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.10.2020
Subjects
Fire Detection Systems
FIS (Fuzzy Inference System) Sugeno
Forest Fires
Forestry
Fuzzy logic
Internet
Internet of Things
Monitoring
Temperature sensors
Wireless Sensor Network
Wireless sensor networks
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Summary:Forest fires are a phenomenon of natural disasters that often occur in Indonesia and are a local and global concern. Forest fires that occur today are caused by two main factors namely natural factors and uncontrolled human activity factors. Therefore in this research is to find ways to reduce forest fires that often occur today. Therefore, a fire detection system with dual sensor based wireless sensor network based with Sugeno FIS (Fuzzy Inference System) algorithm is designed that can be accessed through the Internet network. The purpose of this research is to create a forest fire monitoring system for a wide area of fire-prone areas using WSN (Wireless Sensor Network). In this study also used the FIS (Fuzzy Inference System) method as a method of decision making with mathematical calculations that can improve accuracy in the fire detection system so that the output of this method is the level of fire status. Internet of Things technology is also used so that information can be received by users in real-time through the Internet network. Based on the test results on the system that has been designed, Sugeno FIS (Fuzzy Inference System) calculations on SN1 and SN2 have 100% accuracy when compared to manual calculations. The average speed of sending data on SN1 is 1.67 seconds and on SN2 is 1.52 seconds. Testing the detection status of the fire sensor with a distance of 10 to 100 cm has results that correspond to a predetermined threshold.
DOI:10.1109/ICIEE49813.2020.9277365