Garbage Management System

Geethamani, R.; P, Rakshana; Raveena, P; Ragavi, R

doi:10.1109/icaccs51430.2021.9441922

Cited by 9 publications

(2 citation statements)

References 3 publications

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“…Thole et al (2019) developed an IoT-based trash bin that sends a message to the authority when garbage reaches the maximum level, and this system automatically calculates the exact location of the bin from where the garbage has to be lifted. Geethamani et al (2021) designed a web-based and an IoT-based application for managing garbage. This application displays all the garbage-related data.…”

Section: Related Workmentioning

confidence: 99%

Quantifying Reliability Indices of Garbage Data Collection IOT-based Sensor Systems using Markov Birth-death Process

Kumar,

Kumar

2023

Int. j. math. eng. manag. sci.

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The aim of this paper is to analyze the performance of a Garbage data collecting sensor network system (GDCSNS) through mathematical modelling and a reliability approach. The reliability assessment of such a system is essential to ensuring that it can collect data related to garbage at different locations consistently and accurately. After the determination of the reliability measures of the system, the next aim is to identify the weakest sensors of the system so that a timely maintenance strategy for the weakest sensor can be planned to avoid disruption in the collection of data from the sensor systems. In the considered system, three sensors have been installed at various location in the city that send the information to the center office (hub point) and then from the center office to the person who is responsible for collecting the garbage from the location and dumping it at some predefined places. These sensors collect data related to garbage level, weight, and other information and send it to computers at the city's central office. Markov modelling has been used to model the system. Based on the mathematical model, a state transition diagram and a set of Kolmogorov time-dependent differential equations have been obtained. The various state probabilities (explicit expressions) related to the performance of the system, namely, Reliability, Mean time to failure, have been obtained to understand the different maintenance policies that can be used. A sensitivity analysis has also been performed to determine the weakest sensor among the sensors.

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Section: Related Workmentioning

confidence: 99%

Quantifying Reliability Indices of Garbage Data Collection IOT-based Sensor Systems using Markov Birth-death Process

Kumar,

Kumar

2023

Int. j. math. eng. manag. sci.

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“…[ 3 ] Every year, India produces 277.1 million tons of solid waste, which is expected to rise to 387.8 million tons by 2030 and will increase to 543.3 million tons by 2050. [ 4 ] Thus, the waste management has become one of the serious and widespread issues in many industrialized and developing countries due to lack of understanding of waste management and associated factors. [ 5 ] In India, the COVID‐19 pandemic has put the biomedical waste management (BMWM) industry in trouble.…”

Section: Introductionmentioning

confidence: 99%

Management of Bio‐Medical Wastes during COVID‐19 Pandemic: A Case Study of Haryana State

Sukhram,

Dwivedi,

Sharma

et al. 2023

Macromolecular Symposia

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Management of biomedical waste (BMW) is important for a healthy and safe environment. In order to guarantee cleanliness and hygiene, BMW must be collected, disposed of, and handled appropriately. The world is being affected by the COVID‐19 pandemic, which has spread to various countries. The COVID‐19 pandemic has increased the volume of BMW production, created challenges for both the central and state pollution control boards (SPCB). During this pandemic, the Ministry of Environment, Forestry, and Climate Change (CPCB) devised and implemented various guidelines and standards for proper BMW management. The number of common biomedical waste treatment facilities (CBMWTFs) has risen, and several states and private institutions have built their own BMW treatment facilities. Despite this, some countries do not have enough infrastructures for biomedical waste management (BMWM). Haryana is responsible for a significant percentage of the COVID‐related BMW generation. Despite the fact that the state possesses 11 CBMWTFs, it is unable to handle this BMW properly. Thus, the finding of this study showed that in some Indian states including Haryana, there are insufficient institutional systems for managing biomedical and hazardous wastes during pandemic situations. The study is based on the examination of data on BMW generation and management. The data have been collected from legitimate sources including scientific literatures and annual reported from government websites. The findings are expected to provide the reader with relevant information on the suitability and efficiency of the whole BMW management framework in India.

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EnviroWatch: A Comprehensive Environmental Monitoring Web Frame and Cleanup Coordination System Using CNN

Ahire,

Gage,

Mhatre

et al. 2024

Lecture Notes in Networks and Systems

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Garbage Management System

Cited by 9 publications

References 3 publications

Quantifying Reliability Indices of Garbage Data Collection IOT-based Sensor Systems using Markov Birth-death Process

Quantifying Reliability Indices of Garbage Data Collection IOT-based Sensor Systems using Markov Birth-death Process

Management of Bio‐Medical Wastes during COVID‐19 Pandemic: A Case Study of Haryana State

EnviroWatch: A Comprehensive Environmental Monitoring Web Frame and Cleanup Coordination System Using CNN

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