Article’s

Industrial waste management faces critical challenges due to increasing waste volumes, hazardous materials, and stringent environmental regulations. Traditional waste collection and disposal methods are often inefficient and unable to cope with the dynamic nature of industrial waste generation. This paper proposes a comprehensive IoT-based smart waste management system tailored for industrial environments. The system integrates sensors on waste bins and transport vehicles, GPS tracking for real-time location data, and machine learning (ML) for intelligent analytics. The architecture comprises sensor-equipped smart bins that monitor fill levels and waste composition, edge devices and connectivity modules (e.g. LoRaWAN, NB-IoT), cloud-based data processing, and a user interface for operators. GPS devices on collection trucks enable dynamic routing and monitoring. Machine learning is applied for tasks such as waste classification, fill-level prediction, route optimization, and anomaly detection. Figures illustrate the system architecture, data flows, and sensor network. We evaluate performance through literature case studies and simulations: IoT enabled routing can reduce collection distance by ~21%, while ML classifiers achieve >95% accuracy. Key benefits include reduced fuel use and emissions, timely waste pickups, and improved recycling. We discuss challenges of scalability, energy efficiency (e.g. low-power sensors), and data privacy, and suggest future directions such as edge AI and robust security. This work demonstrates that an integrated IoT+ML platform can greatly enhance industrial waste management effectiveness.