DOI: https://doi.org/10.63345/ijrmeet.org.v10.i9.1
Prof.(Dr.) Vishwadeepak Singh Baghela
Galgotias University
Greater Noida, India
Vishwadeepak.Baghela@galgotiasuniversity.edu.in
Abstract
Smart waste management has evolved into a pivotal element of sustainable urban planning, responding to spiraling volumes of municipal solid waste (MSW) alongside mounting environmental and economic pressures. Traditional collection methods, bound by fixed timetables, often lead to resource wastage, unnecessary vehicle emissions, and suboptimal route planning. This study introduces a comprehensive smart waste management prototype that synergizes Internet of Things (IoT) sensor technologies with advanced machine learning (ML) algorithms to deliver dynamic, data-driven collection strategies. Utilizing ultrasonic HC-SR04 sensors for volumetric fill-level measurements, coupled with Raspberry Pi Camera Module v2 for visual waste-type categorization, the system transmits real-time data via a LoRaWAN network to a central server. A convolutional neural network (CNN) trained on a 2021 waste-image dataset performs on-edge classification into four primary categories: plastic, paper, metal, and organic. Over a six-month deployment across 50 municipal bins, system performance was rigorously evaluated: volumetric sensing accuracy averaged 96.8%, classification accuracy reached 87.4%, and route optimization efforts reduced weekly collection trips by 25%. Detailed statistical analyses, encompassing both descriptive and inferential measures, underscore the system’s robustness under variable environmental conditions. Findings indicate a potential 30% reduction in fuel consumption and a monthly cost saving of USD 2,000, highlighting scalability and replicability within pre‑2023 technological constraints. The integrated framework offers municipalities a pragmatic pathway toward smarter sanitation services, aligning with engineering best practices and urban sustainability objectives.
Keywords
IoT, Machine Learning, Smart Waste Management, LoRaWAN, Ultrasonic Sensors, Convolutional Neural Network
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