scholarly journals Research on the Urban Garbage Collection System Based on Multi Sensors

2016 ◽  
Author(s):  
Yan Li ◽  
Yulei Huang
Keyword(s):  
Garbage Collection ◽  
Collection System

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

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3930 ◽  
Author(s):  
Ayaz Hussain ◽  
Umar Draz ◽  
Tariq Ali ◽  
Saman Tariq ◽  
Muhammad Irfan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Air Pollutants ◽  
Garbage Collection ◽  
Air Pollutant ◽  
Real Time Monitoring ◽  
Collection System ◽  
The Real ◽  
The Status

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.

scholarly journals Practical Aspects for a Working Compile Time Garbage Collection System for Mercury

2001 ◽  
pp. 105-119 ◽  
Author(s):  
Nancy Mazur ◽  
Peter Ross ◽  
Gerda Janssens ◽  
Maurice Bruynooghe
Keyword(s):  
Garbage Collection ◽  
Collection System

BREATHE SAFE: A Smart Garbage Collection System for Dhaka City

2018 ◽  
Author(s):  
Md. Junayed Siddique ◽  
Mohammad Aynul Islam ◽  
Fernaz Narin Nur ◽  
Nazmun Nessa Moon ◽  
Mohd. Saifuzzaman
Keyword(s):  
Garbage Collection ◽  
Dhaka City ◽  
Collection System

Development of underwater garbage collection system by jellyfish soft robot

2019 ◽  
Vol 2019 (0) ◽  
pp. 1P2-G04
Author(s):  
Tatsuki OHNO ◽  
Hironari TANIGUCHI ◽  
Yusuke INOUE ◽  
Kazunori HOSOTANI
Keyword(s):  
Garbage Collection ◽  
Soft Robot ◽  
Collection System

scholarly journals Plant System Expansion: “Getting More From Alternative Revenue Sources”

2001 ◽  
Author(s):  
Damon M. K. Taam
Keyword(s):  
Garbage Collection ◽  
Regulatory Compliance ◽  
Solid Wastes ◽  
Hazardous Wastes ◽  
Environmental Concerns ◽  
System Expansion ◽  
Due Diligence ◽  
Collection System ◽  
Extra Effort ◽  
Revenue Sources

Abstract “Alternative Revenue Sources” are tipping fee revenues received from the combustion of non-traditional waste. Such alternative solid wastes are not hazardous wastes and are not typically delivered by the normal garbage collection system. Alternative Revenue Wastes (ARW) need a due diligence review showing that disposal of the waste will not violate any laws, ordinances and/or permit conditions. Disposal of ARW will need coordination and additional special handling for final disposal ARW generators do pay a tipping fee greater than the solid waste tipping fee in order to compensate the owner/operator for the extra effort. ARW wastes are derived from the following special considerations: 1) Liability concerns from disposal of such waste. 2) Sensitive security. 3) Legal/Regulatory compliance. 4) Environmental concerns. 5) Resource recovery. 6) Infectious Wastes.

scholarly journals Inequalities in the access to healthy urban structure and housing: an analysis of the Brazilian census data

2021 ◽  
Vol 37 (6) ◽  
Author(s):  
Antonio Fernando Boing ◽  
Alexandra Crispim Boing ◽  
S. V. Subramanian
Keyword(s):  
Garbage Collection ◽  
Social Inequalities ◽  
Census Data ◽  
Urban Structure ◽  
Lower Income ◽  
Collection System ◽  
Census Tracts ◽  
The North ◽  
Piped Water ◽  
Government Planning

Abstract: This study aims (1) to test the association between access to basic sanitation/hygiene services in Brazilian households with their householders’ socioeconomic and demographic characteristics; (2) to analyze the distribution of urban health-relevant elements in the census tracts according to their income, education and race/color composition. The information come from the 2010 Brazilian Demographic Census, which collected data regarding both household conditions and urban structure of the census tracts. Prevalence ratios were calculated using crude and adjusted Poisson regression models. The proportional distribution of the census-tract urban structure was performed, according to the deciles of the exploratory variables, and the ratios and the absolute differences between the extreme deciles were calculated. Around 4.8% of the households had no piped water, 34.7% had no sewage collection system, 9.8% had no garbage collection and 39% were considered inadequate. Families whose householders were black, indigenous or brown had lower income and educational level, and lived in the North, Northeast, and Central West regions. They were more likely to be considered inappropriate for not having piped water, sewage collection system, and garbage collection. Moreover, sectors where the majority of the population was black, had lower educational levels and lower income had significantly poor paving, street lighting, afforestation, storm drain, sidewalk and wheelchair ramp. This study analyzed national data from 2010 and provides a baseline for future studies and government planning. The relevant social inequalities reported in this study need to be addressed by effective public policies.

scholarly journals Evolución del diseño de un sistema de recolección de basura utilizando manufactura aditiva en un robot limpiador de playa

2019 ◽  
pp. 23-28
Author(s):  
Martha Isabel Aguilera-Hernández ◽  
Diana Nishiyama-Gómez ◽  
Alejandro Santillán-Martínez ◽  
Gustavo Emilio Rojo-Velazquez
Keyword(s):  
Additive Manufacturing ◽  
Garbage Collection ◽  
Collection System ◽  
Cleaning Robots ◽  
Design Options

Beach cleaning robots have been proposed as an option in reducing the pollution of beaches in Mexico, through garbage collection. To collect waste the robot must have a mechanism that can take the waste and deposit it in a container where they can remain without generating pollution. In our institute various mechanisms have been implemented that allow waste to be collected and this article shows the description of some of them showing the application of additive manufacturing in their design. The objective is to show the design, implementation and application of each collection system designed, showing the improvement by applying additive manufacturing. The basic contribution of this work is to show different design options and the parameters to consider in the design of each of them.

scholarly journals A Real-Time Smart Dumpsters Monitoring and Garbage Collection System using Iot

2019 ◽  
Vol 8 (2S5) ◽  
pp. 85-89
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Monitoring System ◽  
Garbage Collection ◽  
Smart Cities ◽  
Environmental Costs ◽  
Collection System ◽  
Alert System ◽  
Vehicle Monitoring ◽  
Rf Transmitter

In most of the cities the overflowed garbage dumpsters are creating an obnoxious smell and making an unhygienic environment. The Collection of garbage is a very much needed municipal service that requires huge expenditures and execution of this operation is high-priced. The high pricing is due to the various factors such as man power, navigation of vehicles, fuel, maintenances and environmental costs. The above factor necessitates the design, implementation and execution of the new Smart Intelligent Garbage Alert System (SIGAS) for the smart cities. This paper focuses on the implementation of an IoT based embedded system which integrates various Sensors & controllers with RF transmitter and receiver for dumpster and vehicle monitoring system with their performance measured in real time environment.

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