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Solar-Powered IoT Smart Bin Deployment in Lagos: A Scalable Model for Sustainable Waste Management in Developing Cities Adeoye, Yussuff; Igwe, Ismail
JURNAL EDUNITRO Jurnal Pendidikan Teknik Elektro Vol. 5 No. 2 (2025): October Issue
Publisher : Department of Electrical Engineering Education, Faculty of Engineering, State University of Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/edunitro.v5i2.12709

Abstract

This study details the design and implementation of a solar-powered Internet of Things (IoT)-based smart waste monitoring system, specifically developed to mitigate the persistent waste management challenges in Lagos, Nigeria. The primary goal was to create an automated, energy-efficient system capable of real-time monitoring of waste bin levels and environmental conditions. The developed prototype utilizes a NodeMCU ESP8266 microcontroller, integrating an ultrasonic sensor for waste level detection, an MQ-135 gas sensor for air quality assessment, and a DHT22 sensor for temperature and humidity measurement. The system is sustainably powered by solar energy, maintaining an average charging efficiency of 88% to ensure continuous operation. Real-time data is transmitted to a Firebase cloud database and displayed on a web dashboard, allowing waste management operators to proactively address bin overflows and harmful gas accumulation. Experimental results demonstrated that the system achieved an average accuracy rate of 95% in waste level detection. The findings confirm that IoT integration substantially enhances waste management efficiency, reduces the need for manual collection, and promotes sustainability through renewable energy use. The research concludes that combining IoT and solar technology offers a reliable, low-cost, and eco-friendly solution for urban waste management in energy-limited settings. The successful deployment in Lagos underscores its high scalability potential for other developing cities, contributing directly to the relevant Sustainable Development Goals (SDGs). Future work is recommended to incorporate machine learning for predictive collection optimization.