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

Mishra, S, Jena, L, Tripathy, HK ORCID: https://orcid.org/0000-0002-4960-6228 and Gaber, TMA ORCID: https://orcid.org/0000-0003-4065-4191 2022, 'Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment' , PLOS ONE, 17 (8) .

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Abstract

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

Item Type: Article
Contributors: So-In, C (Editor)
Schools: Schools > School of Environment and Life Sciences
Journal or Publication Title: PLOS ONE
Publisher: Public Library of Science
ISSN: 1932-6203
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 06 Sep 2022 10:15
Last Modified: 06 Sep 2022 10:15
URI: http://usir.salford.ac.uk/id/eprint/64555

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