AI-Driven Predictive Waste Management with IoT-Enabled Monitoring for Smart Cities

Abstract

With an increase of the world’s population and rapid urbanization, the amount of municipal solid waste also increases. Traditional waste management focuess on specific collection time tables, manual inspections, and basic algorithms, which creates inefficiencies in routing and increases the chances of overflowing the bins. These issues also do not increase response time and slow down routing. Current methods that use algorithms to resolve static shortest-path routing or basic rule-based scheduling lack the ability to adjust to real time changes that a smart system integrates. This system is the first of its kind to incorporate cloud technology with AI and IoT. The system begins with smart waste bins with ultrasonic and other environmental sensors that continuously transmit data to the cloud. The first of two algorithms, IntelliFillNet, is a novel method of processing unstructured data from a sensor stream, focusing on data cleaning and anomaly detection, along with spatiotemporal prediction of sensor fill levels to generate dynamic prioritization scores for bins and predict overflows in the near future. The second new algorithm, EcoRouteSync, incorporates outputs from IntelliFillNet and, through reinforcement learning, optimizes the real-time collection and routing of vehicles to minimize service delays and fuel costs. The whole processing pipeline is linear, from the acquisition of sensor data, through predictive analytics, to adaptive routing optimization. For example, in the experimental assessment, EcoRoutesync demonstrated predicted accuracy, minimized unnecessary collection trips and operational costs, and improved responsiveness over the Smart Bin Insights Dataset (available via Mendeley Data). This validates the proposed architecture’s effectiveness and scalability in the smart city waste management domain.