V1I6P8

Digital Transformation of Urban Waste Systems: An IoT and AI-Driven Approach to Sustainable Cities

Jelil  Olaoye^1*, Ifeanyi Kingsley Egbun²,  Idris Opeyemi Aransi³, Taiwo Bakare-Abidola⁴

Abstract

The rapid growth of urban populations has led to significant challenges in waste management, particularly in the context of sustainable development and efficient resource use. Smart cities aim to address these challenges by leveraging advanced technologies such as the Internet of Things (IoT) and machine learning (ML) to create intelligent, data-driven waste management systems. This review explores the integration of IoT and ML in enhancing waste collection, sorting, and recycling processes within smart cities. IoT-based solutions, including smart bins, waste tracking sensors, and automated collection systems, enable real-time data collection, which is further analyzed by machine learning algorithms to optimize waste management operations, predict waste generation, and enhance recycling efficiency. The article provides an overview of current applications, real-world case studies, and future trends in IoT and ML for waste management. Additionally, it discusses the challenges and limitations of these technologies, such as data privacy concerns, high implementation costs, and the need for robust infrastructure. The review highlights the potential of IoT and ML to revolutionize urban waste management and contribute to more sustainable, eco-friendly cities.

Keywords: Smart cities, waste management, Internet of Things (IoT), machine learning, predictive analytics, waste sorting, recycling, real-time data, smart bins, sustainable development, urban sustainability, automated waste collection, case studies, IoT applications, machine learning applications