International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Modern urban waste management systems tend to make use of artificial intelligence (AI) and big data technologies through the collection of multimodal Internet of Things (IoT) data to manage operational inefficiencies and unsustainability’s. Scope of a broad-stroke synthesis of AI and big data features and applications for smart urban waste management, alongside the relevance of established AI and smart city concepts, with conclusions that point to critical pathways for application and research. Actionable real-world conclusions naturally arise from a deeper understanding of a broad stroke thinking on smart waste as well as the interrelation between AI capabilities and urban waste management drivers. A smart waste management system encompasses the entire urban waste lifecycle, from generation and collection to recycling and reprocessing, focusing on the generation, collection, sorting, and recycling steps; and processes driven by data fusion and artificial intelligence. Urban waste systems logically collect heterogeneous data to inform operation. The potential of modern smart waste concepts rests on Internet of Things (IoT) and data-driven technologies applied to waste systems.
The overwhelming amount of novel sensing devices, capable of gathering information about waste fill levels and additional smartness features provide the ability to create real-time fill level forecasts. Apart from the sensing on bins, smart containers, capable of providing additional information (e.g., temperature, smoke) have also been deployed. Twofold analysis improves fill level forecasting through anomalies detection and resolution. All those novelties create a need for a transversal analysis of all the innovations, elements, and data-enabled technologies proposed through a smart waste concept.

Keywords: Smart Urban Waste Management, AI-Driven Waste Systems, Big Data In Waste Management, IoT-Enabled Waste Collection, Multimodal Waste Sensing, Waste Lifecycle Analytics, Real-Time Fill Level Forecasting, Smart Waste Containers, Waste Anomaly Detection, Data Fusion For Waste Systems, Urban Sustainability Technologies, Intelligent Waste Collection Optimization, Smart City Waste Solutions, Waste Sorting And Recycling Analytics, Sensor-Based Waste Monitoring, Predictive Waste Management, Heterogeneous Urban Data Integration, Operational Efficiency In Waste Systems, AI Applications In Smart Cities, Data-Driven Urban Sustainability.

Downloads: | DOI: 10.17148/IJARCCE.2025.1412157