Comprehensive Mix-Method Approach for Efficient Waste Management using IoT-Driven Solutions

Escalating the global waste management crisis calls for innovative technological interventions transforming the age-old practice of traditional waste handling. The present system remains mostly inefficient, labor-intensive, and environmentally unsustainable even when rising levels of urbanization and consumption of resources increase waste production. This study examines the transformative potential of IoT technologies in developing intelligent data-driven solutions for waste management to solve critical infrastructure challenges in urban centers. This study developed IoT-enabled smart waste collection systems in three metropolitan areas using a comprehensive mixed-method approach that includes quantitative sensor data analysis, geospatial mapping, and longitudinal municipal waste tracking. Advanced sensor networks and associated machine learning algorithms were used to monitor volume, composition, and efficient collections of wastes. Predictive analytics and the edge computing framework were adapted to optimize routes for better waste collection, reduce associated operational costs, and hence also minimize the environmental footprint. The key results are possible savings of 37% in waste collection costs, an increase of 42% in route optimization for collections, and a reduction of several percent in greenhouse gas emissions due to smart routing and scheduling. IoT-led strategy outperformed conventional strategies for waste management with better operational efficiency, further signifying the revolutionary changes brought by digital technologies in managing urban infrastructure.