Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Abstract

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

“…The use of IoT systems for waste management has been observed in the works of [106] who utilize IoT systems to help reduce energy wastage in waste collection by municipalities. Hussain et al [107] develop a waste management system that not only determines if bins are full and need collecting (using data from various sensors placed in the bin) but also predicts the air quality around it using RNNs. The sensing modalities in each of these applications is pretty similar in that they indicate to whether a waste bin is full or not which is then used for route planning.…”

Integration of Discrete Wavelet Transform, DBSCAN, and Classifiers for Efficient Content Based Image Retrieval

“…The machine learning-based classifier SVM and Decision Tree give better results for the classification of images. SVM has been extensively utilized for classification and other various learning purposes and achieves notable accuracy in results with less computational cost [11]. The data points in the classes can be separated based on the chosen hyperplane [12].…”

“…Machine Learning (ML) combines with multiple statistical approaches in various ways, and many techniques have been developed to perform ML tasks, like SVM, Decision Tree, Naïve Bayes, Neural Networks, and Random Forest [11]. These approaches have been used to perform multiple tasks, including image retrieval, speech recognition, pattern recognition, texture classification, and biometric identification.…”

“…These approaches have been used to perform multiple tasks, including image retrieval, speech recognition, pattern recognition, texture classification, and biometric identification. SVM has been extensively utilized for classification and other various learning purposes and gives good accuracy in results with less computational cost [11]. The training data in SVM is represented by points in space that are dissected into classes, with an obvious gap between classes.…”

“…The training data in SVM is represented by points in space that are dissected into classes, with an obvious gap between classes. The data points in the classes are separated based on the chosen hyperplane [11,12]. The main goal is to choose a hyperplane with a maximum margin between the data points of different classes.…”