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Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment

Collaborative modelling of the Internet of Things (IoT) with Artificial Intelligence (AI) has merged into the Intelligence of Things concept. This recent trend enables sensors to track required parameters and store accumulated data in cloud storage, which can be further utilized by AI based predicti...

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Autores principales: Mishra, Sushruta, Jena, Lambodar, Tripathy, Hrudaya Kumar, Gaber, Tarek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371262/
https://www.ncbi.nlm.nih.gov/pubmed/35951589
http://dx.doi.org/10.1371/journal.pone.0272383
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author Mishra, Sushruta
Jena, Lambodar
Tripathy, Hrudaya Kumar
Gaber, Tarek
author_facet Mishra, Sushruta
Jena, Lambodar
Tripathy, Hrudaya Kumar
Gaber, Tarek
author_sort Mishra, Sushruta
collection PubMed
description Collaborative modelling of the Internet of Things (IoT) with Artificial Intelligence (AI) has merged into the Intelligence of Things concept. This recent trend enables sensors to track required parameters and store accumulated data in cloud storage, which can be further utilized by AI based predictive models for automatic decision making. In a smart and sustainable environment, effective waste management is a concern. Poor regulation of waste in surrounding areas leads to rapid spread of contagious disease risks. Traditional waste object management requires more working staff, increases effort, consumes time and is relatively ineffective. In this research, an Intelligence of Things Enabled Smart Waste Management (IoT-SWM) model with predictive capabilities is developed. Here, local sinks (LS) are deployed in specified locations. At every instant, the current status of smart bins in each LS is notified to users to determine the priority level of LS to be emptied. Based on aggregated sensor values for the three smart bins, LS weight and poison gas value, the priority order of emptying LS is computed, and decision is made whether to notify the users with an alert message or not. It also helps in predicting the LS, which is likely to be filled up at a faster rate based on assigned timestamp. This model is implemented in real time with many LS and it was observed that bins, which were close to more crowded sites filled up faster compared to sparse populated areas. Random forest algorithm was used to predict whether an alert notification is to be sent or not. An average mean of 95.8% accuracy was noted while using 60 decision trees in random forest algorithm. The average mean execution latency recorded for training and testing sets is 13.06 sec and 14.39 sec respectively. Observed accuracy rate, precision, recall and f1-score parameters were 95.8%, 96.5%, 98.5% and 97.2% respectively. Model buildup and the validation time computed were 3.26 sec and 4.25 sec respectively. It is also noted that at a threshold value of 0.93 in LS level, the maximum accuracy rate reached was 95.8%. Thus, based on the prediction of random forest approach, a decision to notify the users is taken. Obtained outcome indicates that the waste level can be efficiently determined, and the overflow of dustbins can be easily checked in time
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spelling pubmed-93712622022-08-12 Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment Mishra, Sushruta Jena, Lambodar Tripathy, Hrudaya Kumar Gaber, Tarek PLoS One Research Article Collaborative modelling of the Internet of Things (IoT) with Artificial Intelligence (AI) has merged into the Intelligence of Things concept. This recent trend enables sensors to track required parameters and store accumulated data in cloud storage, which can be further utilized by AI based predictive models for automatic decision making. In a smart and sustainable environment, effective waste management is a concern. Poor regulation of waste in surrounding areas leads to rapid spread of contagious disease risks. Traditional waste object management requires more working staff, increases effort, consumes time and is relatively ineffective. In this research, an Intelligence of Things Enabled Smart Waste Management (IoT-SWM) model with predictive capabilities is developed. Here, local sinks (LS) are deployed in specified locations. At every instant, the current status of smart bins in each LS is notified to users to determine the priority level of LS to be emptied. Based on aggregated sensor values for the three smart bins, LS weight and poison gas value, the priority order of emptying LS is computed, and decision is made whether to notify the users with an alert message or not. It also helps in predicting the LS, which is likely to be filled up at a faster rate based on assigned timestamp. This model is implemented in real time with many LS and it was observed that bins, which were close to more crowded sites filled up faster compared to sparse populated areas. Random forest algorithm was used to predict whether an alert notification is to be sent or not. An average mean of 95.8% accuracy was noted while using 60 decision trees in random forest algorithm. The average mean execution latency recorded for training and testing sets is 13.06 sec and 14.39 sec respectively. Observed accuracy rate, precision, recall and f1-score parameters were 95.8%, 96.5%, 98.5% and 97.2% respectively. Model buildup and the validation time computed were 3.26 sec and 4.25 sec respectively. It is also noted that at a threshold value of 0.93 in LS level, the maximum accuracy rate reached was 95.8%. Thus, based on the prediction of random forest approach, a decision to notify the users is taken. Obtained outcome indicates that the waste level can be efficiently determined, and the overflow of dustbins can be easily checked in time Public Library of Science 2022-08-11 /pmc/articles/PMC9371262/ /pubmed/35951589 http://dx.doi.org/10.1371/journal.pone.0272383 Text en © 2022 Mishra et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mishra, Sushruta
Jena, Lambodar
Tripathy, Hrudaya Kumar
Gaber, Tarek
Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment
title Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment
title_full Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment
title_fullStr Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment
title_full_unstemmed Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment
title_short Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment
title_sort prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371262/
https://www.ncbi.nlm.nih.gov/pubmed/35951589
http://dx.doi.org/10.1371/journal.pone.0272383
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