Development of a Smart Waste Management System with Automatic Bin Lid Control for Smart City Environment

Abbas Abdullahi; Ameer Mohammed; Mathias Usman Bonet; Abdussalam El-Suleiman; Rabiu B. Ahmad; Teng David Chollom

doi:10.4108/eetsc.4385

Authors

Abbas Abdullahi Air Force Institute of Technology, Kaguna https://orcid.org/0009-0009-2937-7876
Ameer Mohammed Air Force Institute of Technology, Kaguna
Mathias Usman Bonet Air Force Institute of Technology, Kaguna
Abdussalam El-Suleiman Air Force Institute of Technology, Kaguna
Rabiu B. Ahmad Air Force Institute of Technology, Kaguna
Teng David Chollom Air Force Institute of Technology, Kaguna

DOI:

https://doi.org/10.4108/eetsc.4385

Keywords:

Smart Cities, Smart Bin, Internet of Things (IOT), Smart Waste Management System, Machine Learning

Abstract

As cities worldwide transform into smart, interconnected urban environments, the management of municipal waste emerges as a pressing challenge. This paper, titled "Development of a Smart Waste Management System with Automatic Bin Lid Control," offers a sophisticated solution that integrates seamlessly within the concept of smart cities. This system harnesses the power of the Internet of Things (IoT) to optimize waste collection and enhance urban cleanliness. The primary aim of this project is to create a smart waste management system that extends beyond traditional waste bins. It introduces a network of intelligent waste containers equipped with automatic lid control mechanisms that operate based on real-time waste level data. When approaching these bins, the lid control mechanism automatically opens the bin only if the waste level is not full, thereby facilitating convenient waste disposal. In the context of smart cities, this innovative approach presents several advantages. It optimizes waste collection efficiency by prioritizing bins in need of immediate attention and ensures that waste containers are not prematurely emptied, reducing unnecessary waste disposal trips. Moreover, the system enables city authorities to gain insights into waste level trends, fostering data-driven and proactive waste management strategies for a cleaner, more sustainable urban environment.

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