An integrated approach of IoT and WSN using wavelet transform and machine learning for the solid waste image classification in smart cities

Abstract

AbstractSolid waste management is one of the utmost vital issues allied with the smart city, which harms our society's health and the environment. Solid waste management has been a significant hurdle in developing smart cities, impacting the community's living standards. Sustainability is the circumstances that help to achieve natural and social development simultaneously in a prolific accord. We can allege that sustainable growth brings out stability in the necessities of the environment. It helps to preserve resources available for the coming generations. Waste management is also a concern for many authorities; researchers have started focusing on this area. This research article presents a unique method for waste management by employing internet of things for waste monitoring, a wireless sensor network for data communication and discrete wavelet transforms, image processing, and machine learning for image categorization. Implementing machine learning for image categorization has gained attention in the research area of artificial intelligence. The paper presents a way to classify waste images into different Classes such as domestic waste, e‐waste, and medical waste. The input image is down‐sampled, and channels are split to extract the color moments for minimal processing time. We have created a convolution neural network (CNN) trained using pre‐processed data for feature extraction and classification. We have used K‐nearest neighbor classifier (KNN) as another classifier to compare the results obtained by the proposed methodology. The findings demonstrate that, compared to the KNN, the suggested model shortens the processing time and achieves a high accuracy rate. The paper presents a novel approach for Solid waste classification by using pre‐processed image data for image processing and CNN for waste image classification, for the sake of proving the efficiency of the proposed method we have compared the results with the KNN classifier and found that the accuracy of classification is better in proposed method than the KNN classifier.