TrashTrack

TrashTrack is an IoT-enabled waste management solution designed to improve the efficiency, hygiene, and sustainability of waste disposal systems in urban communities. The system addresses key challenges in traditional waste management, such as bin overflow and delayed collection, through the integra...

Full description

Saved in:
Bibliographic Details
Main Authors: Lam, Yong Qin, Lee, Kai Min, Calvin Ng, Wei Keong, Cheong, Kai Qi, Abdul Majid, Mohd Amizar
Format: Conference or Workshop Item
Language:en
Published: 2025
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/132374/1/132374.pdf
https://ir.uitm.edu.my/id/eprint/132374/
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:TrashTrack is an IoT-enabled waste management solution designed to improve the efficiency, hygiene, and sustainability of waste disposal systems in urban communities. The system addresses key challenges in traditional waste management, such as bin overflow and delayed collection, through the integration of smart technologies. By utilizing infrared sensors to detect human presence and ultrasonic sensors to monitor waste levels, TrashTrack automates the waste detection process and ensures touchless operation to promote public hygiene. When the bin reaches 80% capacity, the system automatically halts operation to prevent overflow. An RFID sensor logs collection details and verifies the identity of cleaning personnel through staff card scans, maintaining secure access and operational integrity. The Arduino Uno R3 microcontroller serves as the primary hardware platform, interfacing with sensors and transmitting real-time data to a web-based monitoring dashboard. The software component processes and visualizes the collected data using tables and charts for easy interpretation. Developed using the Agile methodology, TrashTrack supports iterative enhancements and responsiveness to user needs. This smart system aligns with Sustainable Development Goal (SDG) 11: Sustainable Cities and Communities by enabling data-driven, real-time decision-making and supporting scalable, eco-friendly waste management practices in modern urban environments.