IoT-Integrated AI Framework Using Genetic Algorithms for Waste Management in Smart Campuses

Effective waste management is critical in developing smart campuses, prioritising sustainability and operational efficiency. Traditional methods often face inefficiencies such as poorly optimized collection routes, inadequate bin placement, and delayed responses to overflow. These limitations increase operational costs, resource wastage, and environmental concerns. The paper proposes IoTGAWMS, an IoT-integrated AI framework leveraging Genetic Algorithms (GAs) to optimize waste management strategies (WMS) and improve resource utilization in smart campuses. The proposed framework employs IoT sensors deployed in waste bins to monitor fill levels, categorize waste types, and transmit real-time data to a centralized platform. A GA optimises waste collection routes, scheduling, and bin placement by considering dynamic constraints such as waste generation patterns and campus traffic. Reinforcement Learning enhances the framework by adapting to real-time changes and learning from historical data, enabling predictive and responsive decision-making. Experimental results demonstrate a 35% improvement in waste collection efficiency and a 25% reduction in fuel consumption for waste transportation compared to traditional methods. Additionally, the framework achieves over 90% accuracy in waste type classification, significantly enhancing recycling initiatives. In conclusion, the proposed IoTintegrated AI framework effectively overcomes the limitations of traditional methods, providing a sustainable and efficient solution for waste management in smart campuses