CNN-based Smart Waste Management System in Fog Computing Environment

Abstract

Data processing occurs on the central server in cloud computing. Data transfers from the node to the cloud take a long time. Fog computing has therefore been presented as a solution to these problems. The fog computing nodes handles data processing on network end only. On the other hand, if it requires further processing, it is sent to the central server to complete the task. The processing time is cut down, and the data is more effectively used. In geologically separated places where connectivity may be uneven, fog is generally beneficial. It has become more common in recent years to use ML to enhance fog computing applications and provide fog services, including efficient resource management, security, lowering latency and energy consumption, and traffic modelling. This article proposes a machine learning approach for smart waste management application of fog computing. The proposed approach is based on CNN approach that classifies provided data into different categories that can be used in future for processing waste management data. The proposed approach provides 95.83% accuracy.