ROBUST WASTE COLLECTION: EXPLOITING IoT POTENTIALITY IN SMART CITIES

2017 ◽  
Vol 11 (3) ◽  
pp. 10 ◽  
Author(s):  
GOENKA SAKSHI ◽  
MANGRULKAR R.S. ◽  
◽  
Keyword(s):  
Smart Cities ◽  
Waste Collection

scholarly journals City with Smart Waste Management: Introducing Waste Bin Technologies

2021 ◽  
Vol 34 (02) ◽  
pp. 1032-1038
Author(s):  
Arya Majidi
Keyword(s):  
Internet Of Things ◽  
Adverse Effects ◽  
Waste Management ◽  
Smart Cities ◽  
Human Life ◽  
The Other ◽  
The Internet ◽  
Waste Collection ◽  
Waste Production ◽  
The Internet Of Things

Population growth and urbanization have led to an increase in the rate of waste production, the lack of timely and proper management of which will have adverse effects on human life and the environment. Since most of the waste management costs are spent on waste collection and transportation, it is necessary to find solutions to control the huge costs of this sector. On the other hand, today, intelligent technologies are used globally as solutions to meet challenges in various fields such as agriculture to improve agro-industrial production, transportation, and waste management, which creates a concept called smart cities. One of the categories that has changed the concept of cities and made them have easier and smarter answers to various events and needs is the "Internet of Things", in which many cases and infrastructures with new hardware technologies and Software are integrated. Waste collection is no exception to this rule and efforts have been made to make it smarter. In this research, some of the latest innovations presented globally in order to make trash smarter have been examined.

scholarly journals Development of Solar-Powered of Sensors Based Smart Waste Monitoring System

2021 ◽  
Vol 12 (5) ◽  
pp. 580-586
Author(s):  
Polaiah Bojja, Pamula Raja Kumari, A.Nagavardhan N.Dinesh, M.Gopla D Anirudh
Keyword(s):  
Waste Management ◽  
Monitoring System ◽  
Smart City ◽  
Waste Treatment ◽  
Smart Cities ◽  
Municipal Waste ◽  
Waste Collection ◽  
Solar Powered ◽  
Analyze Data

Dustbins (or Garbage Bins, Trash Cans, whatever you name them) are small containers of plastic or metal used on a temporary basis to store trash (or waste). They are also used for the collection of waste in houses, workplaces, highways, parks, etc. Littering is a major crime in some countries, and public waste bins are also the only way to dispose of small waste. Usually, using different bins for handling wet or dry, recyclable or non-recyclable waste is a common practice. From an ETS perspective, smart waste collection can help municipalities and private waste management companies avoid the need for collection sites, waste disposal facilities and waste treatment plants. As communities increasingly rely on smart city technology to improve, among other things, the quality of life of their residents and the environment, city leaders recognize that smart waste management can also help them achieve sustainability goals such as zero waste and improve services to residents, while improving service to residents. As an example, Development of Some solar-powered bins and recycling bins are already equipped with sensors that analyze data on what is disposed of or recycled and notify collectors when the bin is too full and needs to be picked up. These developed Smart waste management solutions use sensors placed in waste bins to measure levels, notify municipal waste collection services, when the bins are ready to be emptied, and also notify municipal waste collection with a ton has been emptied. Therefore, the solar-powered of sensors based smart waste monitoring system is more and more useful to the current smart cities policies under the smart city project works.

scholarly journals Waste generation prediction under uncertainty in smart cities through deep neuroevolution

2019 ◽  
pp. 128-138 ◽  
Author(s):  
Andrés Camero ◽  
Jamal Toutouh ◽  
Javier Ferrer ◽  
Enrique Alba
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Smart Cities ◽  
Uncertain Data ◽  
Recurrent Network ◽  
The State ◽  
Waste Generation ◽  
Waste Collection ◽  
The Way

The unsustainable development of countries has created a problem due to the unstoppable waste generation. Moreover, waste collection is carried out following a pre-defined route that does not take into account the actual level of the containers collected. Therefore, optimizing the way the waste is collected presents an interesting opportunity. In this study, we tackle the problem of predicting the waste generation ratio in real-world conditions, i.e., under uncertainty. Particularly, we use a deep neuroevolutionary technique to automatically design a recurrent network that captures the filling level of all waste containers in a city at once, and we study the suitability of our proposal when faced to noisy and faulty data. We validate our proposal using a real-world case study, consisting of more than two hundred waste containers located in a city in Spain, and we compare our results to the state-of-the-art. The results show that our approach exceeds all its competitors and that its accuracy in a real-world scenario, i.e., under uncertain data, is good enough for optimizing the waste collection planning.

scholarly journals Increasing Efficiency of IoT-enabled Waste Collectionwith Controlled Mobility of Robotic Bins

2020 ◽  
Vol Volume 14 ◽  
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Waste Collection ◽  
Robot Assisted ◽  
Controlled Mobility ◽  
Sustainable Solutions ◽  
Saint Petersburg ◽  
Model Combining ◽  
The Impact ◽  
Line Following

Smart Cities cater for ever increasing population, which needs sustainable solutions for efficient wellbeing. Waste collection is significant for providing a green ecosystem in such cities. IoT-enabled waste collection solutions assist such a green ecosystem. Waste collection used to be performed by humans or via human intervention.However, contemporary research incorporates robots to perform waste collection. In this paper we describethe real case of a line following robot bin that assists waste collection in the Smart City of Saint Petersburg, Russia. Evaluation is performed through a model combining the distance covered by the actor, the time passed for the collection and the bins emptied. The results show the superiority of robot bins, compared to human workers, highlighting the impact of IoT-enabled robot assisted waste collection as part of a green ecosystem

scholarly journals A VISUAL ANALYTICS OF MOVEMENT DATA OF A WASTE COLLECTION SERVICE: A TOOL FOR SMART CITIES

2020 ◽  
Vol VI-4/W2-2020 ◽  
pp. 111-118
Author(s):  
A. Moreno ◽  
D. I. Hernandez ◽  
D. Moreno ◽  
M. Caglioni ◽  
J. T. Hernandez
Keyword(s):  
Waste Management ◽  
Visual Analytics ◽  
Visual Analysis ◽  
Solid Waste Management ◽  
Smart Cities ◽  
Test Case ◽  
Analysis Tool ◽  
Waste Collection ◽  
Movement Data ◽  
Management Context

Abstract. Solid waste management is an important urban issue to be addressed in every city. In the smart city context, waste collection allows massive collection of data representing movements, provided by satellite tracking technologies and sensors on waste collection equipment. For decision makers to take advantage of this opportunity, an analytical tool suitable for the waste management context, able to visualize the complexity of the data and to deal with different types of formats in which the data is stored is required.The aim of this paper is to evaluate the potential of an interactive data analysis tool, based on R and R-Shiny, to better understand the particularities of a waste collection service and how it relates to the local city context. The User-centered Analysis-Task driven model (AVIMEU) is presented. The model is organized into seven components: database load, classification panel, multivariate analysis, concurrency, origin-destination, points of interest and itinerary. The model was implemented as a test case for the waste collection service of the city of Pasto in the southwest of Colombia. It is shown that the model based on visual analysis is a promising approach that should be further enhanced. The analyses are oriented in such a way that they provide practical information to the agents or experts of the service. The model is available on the site https://github.com/MerariFonseca/AVIMEU-visual-analytics-for-movement-data-in-R .

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