Implementation of an IoT-Based Smart Waste Sorting System in University Campuses: A Case Study in Lafia LGA, Nasarawa State
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Background of the Study :
Efficient waste management is essential for maintaining a clean and sustainable university campus. In Lafia LGA, Nasarawa State, improper waste segregation and disposal practices have led to increased operational costs and environmental degradation. Traditional waste management methods are often manual and inefficient, resulting in mixed waste that is difficult to recycle. IoT-based smart waste sorting systems present a modern solution to these challenges by automating the segregation process at the source. This study proposes the design and implementation of an IoT-enabled waste sorting system that utilizes sensors, cameras, and machine learning algorithms to classify waste into recyclable and non-recyclable categories in real time (Ibrahim, 2023). The system will be installed in waste collection points across university campuses, where smart bins equipped with RFID and optical sensors detect and sort waste automatically. Data collected from these bins will be transmitted to a central platform for analysis, enabling facility managers to monitor waste streams and optimize collection schedules. Prior research has shown that automated waste sorting can significantly improve recycling rates and reduce landfill use (Olu, 2024). The proposed solution aims to reduce labor costs, increase recycling efficiency, and promote environmental sustainability. Field testing will be conducted to evaluate the system’s accuracy, reliability, and cost-effectiveness. The study will also address challenges such as sensor calibration, data integration, and maintenance, ensuring that the system is adaptable to the unique conditions of university campuses in Lafia LGA. Ultimately, this project seeks to provide a scalable and innovative waste management solution that supports sustainable campus operations and contributes to broader environmental conservation efforts (Adeniyi, 2025).
Statement of the Problem :
University campuses in Lafia LGA struggle with inefficient waste management due to inadequate sorting practices. Traditional methods rely on manual separation, leading to high labor costs, contamination of recyclables, and increased environmental impact. The absence of an automated system results in mixed waste, complicating recycling efforts and driving up disposal costs. Additionally, manual processes are often inconsistent and error-prone, leading to low recycling rates and wastage of valuable resources. The lack of real-time monitoring and data analytics further hampers the ability to manage waste effectively, leaving facility managers without actionable insights to optimize waste collection and processing. There is a critical need for an IoT-based smart waste sorting system that can automate the segregation of waste, ensure accurate classification, and provide real-time data for better decision-making. Such a system would not only enhance recycling efficiency and reduce environmental impact but also lower operational costs and improve campus sustainability. This study aims to address these challenges by developing and evaluating an automated waste sorting solution that leverages advanced sensor technology and machine learning algorithms. By comparing the new system with existing manual methods, the research intends to demonstrate significant improvements in waste segregation accuracy and operational efficiency, offering a scalable model for university campuses in resource-constrained settings (Ibrahim, 2023; Olu, 2024).
Objectives of the Study:
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To design an IoT-enabled waste sorting system for university campuses.
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To automate the classification of waste into recyclable and non-recyclable categories.
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To evaluate improvements in recycling efficiency and cost savings.
Research Questions:
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How effective is the IoT-based system in accurately sorting waste in real time?
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What operational benefits are observed compared to manual sorting methods?
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How can the system be scaled for wider implementation in university settings?
Significance of the Study :
This study is significant as it develops an IoT-based smart waste sorting system that enhances recycling efficiency and reduces operational costs in university campuses. The system’s automated, real-time data capabilities offer a replicable model for sustainable waste management, contributing to environmental conservation and resource optimization in educational institutions (Adeniyi, 2025).
Scope and Limitations of the Study:
The study is limited to the design, implementation, and evaluation of the waste sorting system in university campuses within Lafia LGA, Nasarawa State, and does not extend to municipal waste management.
Definitions of Terms:
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Smart Waste Sorting System: An automated system that uses IoT sensors and algorithms to segregate waste.
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RFID: Radio-frequency identification, used for automatic identification and tracking of objects.
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Real-Time Data: Immediate processing and transmission of data as events occur.
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