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.
Highlights
The internet plays a very significant role in the current era as technology advances
We conducted a novel study by utilizing machine learning and an Internet of Things (IoT)-based mechanism to effectively handle smart garbage management systems, and our system showed an improved accuracy compared to traditional garbage collection systems
We propose a system that identifies the levels of garbage along with real-time air monitoring by utilizing machine learning and deep learning approaches
Summary
The internet plays a very significant role in the current era as technology advances. Current evolutions in modern technology, like the Internet of Things (IoT), have had a remarkable effect on our daily life. IoT technologies have improved the quality of life by understanding the surrounding environment through modernization. This has introduced the concept of smart cities by simplifying the communication between humans and things. The expansion of the IoT significantly contributes to the boosting of our life in different sectors like smart homes, smart cities, smart grids, smart wearables, and smart industries. IoT architecture performs a key role in communication devices. The core interest of researchers towards the IoT is the current, overwhelming increase in smart device utilization
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have