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Title Smart Trash Bins: Integration Of Iot With Ai For Efficient Waste Management
ID_Doc 51626
Authors Gonzalez G.E.; Escobar E.R.; Carvajal I.I.; Bento A.C.
Year 2024
Published 2024 7th IEEE International Humanitarian Technologies Conference, IHTC 2024
DOI http://dx.doi.org/10.1109/IHTC61819.2024.10855147
Abstract In modern urban environments, efficient waste management is a significant challenge. This paper presents the design and implementation of an IoT-based smart trash bin system that integrates ultrasonic sensors, weight sensors (simulated), and artificial intelligence (AI) to optimize waste collection processes. The system utilizes ESP32 microcontrollers and ESP32-CAM modules to monitor the fill levels and types of waste. Ultrasonic sensors measure the distance to the waste surface, while potentiometers simulate the weight of the trash. An AI algorithm processes images captured by the ESP32-CAM to classify the waste, enhancing the accuracy of the monitoring system. Data from the sensors and AI analysis are transmitted via Wi-Fi to a centralized server, where it is stored and visualized using Oracle APEX dashboards. The sensor's software can update its status to "full"based on the weight and level readings; a mobile application developed with MIT App Inventor also allows users to update the status of the trash bins via a mobile device. This enables city waste management authorities to make informed decisions, reducing operational costs and improving environmental sustainability. The proposed system demonstrates significant potential in enhancing urban waste management efficiency through real-time data acquisition, processing, and intelligent decision-making. This project aligns with the UN's Sustainable Development Objective number 11: Sustainable Cities and Communities. © 2024 IEEE.
Author Keywords artificial intelligence; higher education; IoT; professional education; smart city; Smart Trash Bins


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