Synergizing Deep Learning and the Internet of Things (IoT) for Smart Waste Management (SWM)
Haneen Algethami · 2024
As the world population proliferates, smart waste management (SWM) is gaining significance through improved integration of information and communication technologies (ICTs). While deep learning (DL) and the Internet of Things (IoT) are increasingly utilized across various sectors, their synergistic application in waste management remains insufficiently explored. This study aims to fill that gap by illustrating how DL and IoT can effectively work together to enhance waste management practices. Utilizing a quantitative research approach, the study incorporates a longitudinal design with a diverse array of urban waste management organizations. Smart sensors gather real-time data on waste levels, operational patterns, and environmental conditions. This data is analyzed, and deep reinforcement learning and neural networks are employed to optimize collection routes and schedules. Consequently, the time required to collect waste was reduced by 25%, and resource utilization efficiency improved by 30%. Furthermore, enhancements to collection routes and schedules contributed to a 20% decrease in environmental impact, underscoring the promise of integrating DL and IoT into waste management. The practical implications of these findings are substantial, providing valuable knowledge and insights for waste management professionals and researchers. This study also informs the audience about the transformative potential of combining DL and IoT in the field. It provides a clear framework for practically integrating these technologies into improving waste management practices in urban areas.