Work

Design and Implementation of an IoT and ML-based Smart Waste Management System

Year: 2024

Type: conference-paper

Abstract: This paper discusses the impact of population growth and increased consumption on waste production, highlighting the challenges of managing waste efficiently. It proposes an IoT-based smart bin system that uses a deep learning model and vision sensors to classify waste into five categories: paper, plastic, metal, cigarettes, and organic waste, achieving 96% accuracy. The system segregates waste into appropriate bins, monitors bin conditions such as toxic gas levels and temperature, and includes fire-extinguishing capabilities. Additionally, a web interface provides real-time data from the sensors, aiming to streamline the recycling process and enhance waste management sustainability.

DOI: 10.1145/3718391.3718431

Authors: Ahmed Abul Hussain, Fadia ElHamami, Lamya Al Qahtani, Munirah Aldossary, Ahmed Abul Hasanaath, Hiren K Mewada

Institutions: Prince Mohammad bin Fahd University, King Fahd University of Petroleum and Minerals

Language: English

Topic: Waste Management and Recycling

Subfield: Waste Management and Disposal

Field: Environmental Science

Domain: Physical Sciences

SDGs: Responsible consumption and production, Industry, innovation and infrastructure

Open Access status: gold

OpenAlex ID: W4411747139

FWCI:

Cites: 10

Cited by:

Related to: 10