An End-to-End Architecture for Smart Waste Management: Integrating Real-Time Data Processing, IoT, and Machine Learning

This paper presents an end-to-end architecture for smart waste management, leveraging real-time data, IoT, AI, and machine learning to optimize operational efficiency and decision-making processes. The architecture is designed for both near real-time and batch data processing, ensuring continuous optimization and adaptation of waste collection routes and resource allocation. Machine learning models are employed to predict possible bad adverse scenarios and optimize operational plans. Additionally, business intelligence is utilized for data analysis and reporting, providing actionable insights based on real-time and historical data. The presented system is implemented on a scalable Kubernetes infrastructure, supporting the increasing data volumes and processing demands while maintaining system responsiveness and efficiency. This integrated approach demonstrates significant improvements in resource utilization, operational efficiency, and service delivery, highlighting the potential for smarter and more sustainable waste management practices. This research addresses the gap in combining IT architectures with AI models and IoT, paving the way for future advancements in smart waste management systems.