Classification of Garbage Waste in Smart Cities Using Convolutional Neural Network

Conventional smart city management modules make use of sensors or Internet of Things (IoT) devices in conjunction with intelligent traffic systems (ITS) to either secure the routes to schedule the times at which EV charging and smart energy distribution will take place. This is done so that electric vehicles can have their charging sessions scheduled and intelligent energy distribution can take place (ITS). This research proposes a novel Smart City Management System (MS) that adopts and integrates three immersive technologies to better manage garbage disposal in electric automobiles. At the beginning, monitoring the current condition of the garbage cans was accomplished using devices that were connected to the Internet of Things. Second, the garbage payloads, weather conditions, and the distance between garbage collection and disposal are just some of the data that may be used by the ITS in conjunction with convolutional neural networks (CNNs) to manage the GDs for efficient traffic management and speed monitoring. Other data that may be used include the distance between garbage collection and disposal. The distance between the locations of waste collection and disposal is another type of data that might be employed. The experimental validation of the proposed MS with CNN-ITS and the protected blockchain architecture have both contributed to improvements in the system energy efficiency, transmission speed, and overall level of security. These improvements have been made possible because of the combination of these two factors.