An Intelligent IoT Based Landfill Fire Prediction And Prevention System

An Intelligent IoT Based Landfill Fire Prediction And Prevention System | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Intelligent IoT Based Landfill Fire Prediction And Prevention System GAYATRI SAKYA, Vrattica Yadav, Saumya Shukla, Aditi Gupta, Rajeev K Shakya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3006654/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Dec, 2024 Read the published version in Wireless Personal Communications → Version 1 posted 5 You are reading this latest preprint version Abstract This work focuses on India’s tallest heap of garbage at Ghazipur landfill site in New Delhi. About 2000 tonnes of garbage is being dumped at Ghazipur every day and it causes poisonous gases. Fire, sparked by methane gas coming from the garbage and takes days to extinguish. Methane belching from garbage can cause even more toxic gases. The dumping of garbage needs to be stopped for the betterment of people and the environment. The research helps detect the gases released from garbage and the importance of the project increasing due to the growth of solid waste. The detection of the risk of fire and the communication of this situation to the people could reduce the number of fires. The system also helps with automatic detection of fire in garbage-dumped areas and alerts to the authorities. This research work presents a low cost network based on Long Range(LoRa) network which is able to assess automatically the level of risk of catching fire in the solid waste. The system consists of several LoRa nodes with sensors to scale temperature, relative humidity and increase in methane level. The data coming from LoRa nodes is stored and processed in the server that is The Things Network and sends data finally to a website or an application for the analysis of the collected data using machine learning . This helps in reducing environmental pollution which results in health hazards for the people residing in the vicinity.The Simulation of the Lora Network is done in NS-3 first ,for finding the throughput and latency with different scenarios of lora nodes and gateways. The deployin stretegies are fixed after ns-3 simulation. The ns-3 simulation is done in ns-3.36.1 using lorawan module.Thereafter the prototype model is prepared using LoRA hardware and different sensors. The real time data is collected on the server for data analysis. The work have used machine learning algorithms to analyse real-time sensor data received on a network server through the LoRa Gateway to detect abnormal patterns indicative of a potential fire outbreak and to justify the ―intelligence‖ of our system. The wireless LoRa communication eliminates extensive wiring, while intelligent data analysis enhances accuracy and minimizes false alarms. Experimental evaluations demonstrate the system's effectiveness in timely fire detection and response, minimizing environmental impact and ensuring community safety. The proposed system presents a promising solution for efficient landfill fire early detection, prevention, and mitigation. LoRa IoT Machine learning sensors NS-3 Full Text Cite Share Download PDF Status: Published Journal Publication published 21 Dec, 2024 Read the published version in Wireless Personal Communications → Version 1 posted Editorial decision: Accept 05 Dec, 2024 Reviewers agreed at journal 31 Jul, 2023 Reviewers invited by journal 30 Jul, 2023 Editor assigned by journal 05 Jun, 2023 First submitted to journal 04 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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The system consists of several LoRa nodes with sensors to scale temperature, relative humidity and increase in methane level. The data coming from LoRa nodes is stored and processed in the server that is The Things Network and sends data finally to a website or an application for the analysis of the collected data using machine learning . This helps in reducing environmental pollution which results in health hazards for the people residing in the vicinity.The Simulation of the Lora Network is done in NS-3 first ,for finding the throughput and latency with different scenarios of lora nodes and gateways. The deployin stretegies are fixed after ns-3 simulation. The ns-3 simulation is done in ns-3.36.1 using lorawan module.Thereafter the prototype model is prepared using LoRA hardware and different sensors. The real time data is collected on the server for data analysis. 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