Optimizing Waste Management through IoT and Analytics: A Case Study Using the Waste Management Optimization Test

Abstract

This research examines how Internet of Things (IoT) technology and advanced analytics may be integrated into trash management. The results show a notable improvement in waste collection efficiency, cost savings, and environmental sustainability. Significant operational cost reductions were achieved by reducing the number of overfilled trash cans by 20% and the frequency of collections by 15% as a consequence of real-time data capture using IoT sensors. Additionally, a 25% reduction in trip distance was made possible by data-driven route optimization, which also resulted in a 10% drop in fuel use and a decrease in carbon emissions. The data-driven strategy also found areas for recycling, which increased the amount of recyclables collected by 15%. These findings highlight the promise that data-driven trash management has for improving both environmental and economic sustainability while tackling the problems associated with urban garbage.

Keywords

• iot
• waste management
• data-driven
• analytics
• sustainability