A waste city management system for smart cities applications

Vu, Dung Do; Kaddoum, Georges

doi:10.1109/rtuwo.2017.8228538

Cited by 43 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dung D. Vu and Georges Kaddoum(2017), 16 In their research on waste management, they proposed a dynamic and efficient approach to waste collection. This approach involves predicting the location of waste, identifying the placement of trash bins, calculating the quantity of waste, and determining the shortest path for delivery.…”

Section: Literature Reviewmentioning

confidence: 99%

Natural language based smart garbage management system using Artificial Intelligence

Agrawal,

Gehani,

Dubey

et al. 2024

JIST

View full text Add to dashboard Cite

The tremendous growth in population around the globe has leads to the society in an unclean and unhygienic environment. This problem then converts to various diseases and other symptoms that affects the human kind in all phases of work. The traditional and conventional approaches used for waste management are not quite enough to manage the generated waste. The traditional system also lags the people's feedback that provide valuable suggestions in terms of complaints etc. that are necessary parameters for the system upgradation. The use of opinion analysis for upgradation of the existing system has been found very effective in the service oriented sector as it deals with the real time problems of the user. This paper introduces a novel management methodology that combines the Internet of Things (IOT) domain along with the opinion analysis module for predicting and summarizing the user opinions for effective decision making. The evaluation of the system depicted an overall improvement in the waste management.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Natural language based smart garbage management system using Artificial Intelligence

Agrawal,

Gehani,

Dubey

et al. 2024

JIST

View full text Add to dashboard Cite

show abstract

“…Computer vision is not the only way in which AI may be applied within smart waste management systems: Convolutional Neural Networks (CNNs) [ 42 , 43 , 44 ], decision forest regression models [ 45 ] or random forest classifiers [ 46 ] are some other examples. Filling level of public waste bins may be also estimated by making use of logistic regression models set up over machine learning techniques [ 47 ] or combined with graph theory [ 48 ]: these are other methods so as to optimize routes during depletion procedures. In addition, such an optimization problem may be also worked out by adopting deep neuroevolutionary techniques so as to build up recurrent neural networks predicting the waste generation in a robust fashion (i.e., by taking into account uncertainty) [ 49 ] or even by employing data analytics platforms [ 50 ].…”

Section: Related Workmentioning

confidence: 99%

A Multi-Layer LoRaWAN Infrastructure for Smart Waste Management

Baldo

Mecocci

Parrino

et al. 2021

Sensors

View full text Add to dashboard Cite

Long Range Wide Area Network (LoRaWAN) has rapidly become one of the key enabling technologies for the development of Internet of Things (IoT) architectures. A wide range of different solutions relying on this communication technology can be found in the literature: nevertheless, the most part of these architectures focus on single task systems. Conversely, the aim of this paper is to present the architecture of a LoRaWAN infrastructure gathering under the same network different typologies of services within one of the most significant sub-systems of the Smart City ecosystem (i.e., the Smart Waste Management). The proposed architecture exploits the whole range of different LoRaWAN classes, integrating nodes of growing complexity according to the different functions. The lowest level of this architecture is occupied by smart bins that simply collect data about their status. Moving on to upper levels, smart drop-off containers allow the interaction with users as well as the implementation of asynchronous downlink queries. At the top level, Video Surveillance Units (VSUs) are provided with machine learning capabilities for the detection of the presence of fire nearby bins or drop-off containers, thus fully implementing the Edge Computing paradigm. The proposed network infrastructure and its subsystems have been tested in a laboratory and in the field. This study has enhanced the readiness level of the proposed technology to Technology Readiness Level (TRL) 3.

show abstract

“…Moreover, they did not provide any technologies for the transmission of data from the trash bin to the other devices in the system. In particular, optimization algorithms have been clearly defined for IoT-based waste management, such as the nearest neighbour search, colony optimization, genetic algorithm, and particle swarm optimization methods [17,18]. In [19], the authors proposed a solution to manage a garbage system integrated with IoT technology, which was an autonomous line-following vehicle with a robotic hand for garbage collection, in which they did not apply any algorithms to optimize the waste collection.…”

Section: Related Workmentioning

confidence: 99%

“…First, waste collection is a daily task in urban areas entailing the planning of waste truck routes, in which environmental, economic, and social factors must be considered. Second, the path length should be shortened, in order to avoid high fuel costs and reduce the work amount, by applying graph theory [14][15][16][17]. Some solutions have introduced the use of IoT devices to estimate the fill level of inboxes and send this data over the Internet to a server for decision-making [18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Waste Management System Using IoT-Based Machine Learning in University

Khoa

Phuc

Lam

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Along with the development of the Internet of Things (IoT), waste management has appeared as a serious issue. Waste management is a daily task in urban areas, which requires a large amount of labour resources and affects natural, budgetary, efficiency, and social aspects. Many approaches have been proposed to optimize waste management, such as using the nearest neighbour search, colony optimization, genetic algorithm, and particle swarm optimization methods. However, the results are still too vague and cannot be applied in real systems, such as in universities or cities. Recently, there has been a trend of combining optimal waste management strategies with low-cost IoT architectures. In this paper, we propose a novel method that vigorously and efficiently achieves waste management by predicting the probability of the waste level in trash bins. By using machine learning and graph theory, the system can optimize the collection of waste with the shortest path. This article presents an investigation case implemented at the real campus of Ton Duc Thang University (Vietnam) to evaluate the performance and practicability of the system’s implementation. We examine data transfer on the LoRa module and demonstrate the advantages of the proposed system, which is implemented through a simple circuit designed with low cost, ease of use, and replace ability. Our system saves time by finding the best route in the management of waste collection.

show abstract

A waste city management system for smart cities applications

Cited by 43 publications

References 15 publications

Natural language based smart garbage management system using Artificial Intelligence

Natural language based smart garbage management system using Artificial Intelligence

A Multi-Layer LoRaWAN Infrastructure for Smart Waste Management

Waste Management System Using IoT-Based Machine Learning in University

Contact Info

Product

Resources

About