Energy Efficient IoT-Based Forest Fire Detection Using LoRaWAN and AI
Abstract
Forest fires remain a global problem that has a major impact on the economy and health. Indonesia suffered losses of up to Rp. 72.95 trillion due to forest fires in 2019. Internet of Things (IoT) technology can be used for early detection of forest fires, but is constrained by limited network infrastructure and high energy consumption. This study aims to design a smart mitigation device and application for early detection of forest fires using LoRaWAN technology, which does not require an internet connection from the node to the gateway. In addition, an Artificial Intelligence method with adaptive sampling is applied, namely adaptive sampling threshold modeling and reinforcement Q-learning on the gateway to optimize energy use. The method used is Research and Development (R&D), with testing of the effectiveness of the design and descriptive statistical analysis to compare the energy efficiency between LoRaWAN devices with AI and conventional smart mitigation devices. The results of the study show that LoRa-based mitigation devices can cover the entire Jompie Botanical Garden area with a transmission distance of up to 3 kilometers and are 105% more energy efficient than conventional mitigation devices.
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