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ESS-IoT: The Smart Waste Management System for General Household

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dc.citedby3
dc.contributor.authorWong S.Y.en_US
dc.contributor.authorHan H.en_US
dc.contributor.authorCheng K.M.en_US
dc.contributor.authorKoo A.C.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorid55812054100en_US
dc.contributor.authorid57216851801en_US
dc.contributor.authorid58653665800en_US
dc.contributor.authorid35201064500en_US
dc.contributor.authorid16023225600en_US
dc.date.accessioned2024-10-14T03:21:35Z
dc.date.available2024-10-14T03:21:35Z
dc.date.issued2023
dc.description.abstractWith the urban population�s growth, unethical and unmanaged waste disposal may negatively impact the environment. In many cities, a massive flow of people in municipal buildings or offices has generated vast amounts of waste daily, which correlates to the enormous expenses of waste management. The critical issue for better waste management is waste collection and sorting. In this study, the Electronic Smart Sorting-Internet of Things (ESS-IoT) is proposed to assist people in better waste management. The ESS-IoT system uses Raspberry Pi 4b as the microcontroller with three modules, and it is designed with two main functions: waste collection and waste classification. The two main functions have been deployed separately in the literature, while this study has combined both functions to achieve a more comprehensive smart bin waste disposal solution. Waste collection is triggered by the overflow alarm mechanism that employs ultrasonic and tracker sensors. On the other hand, the waste classification is implemented using two classification algorithms: Random Forest (RF) prediction model and Convolutional Neural Network (CNN) prediction model. An experiment is conducted to evaluate the accuracy of the two classification algorithms in classifying various types of waste. The waste materials under investigation can be classified into four categories: kitchen waste, recyclables, hazardous waste, and other waste. The results show that CNN is the better classification algorithm between the two. Future work proposes the research extension by introducing an incentive mechanism to motivate the household communities using a cloud-based competition platform incorporated with the ESS-IoT system. � Universiti Putra Malaysia Press.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.47836/pjst.31.1.19
dc.identifier.epage325
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85146749116
dc.identifier.spage311
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85146749116&doi=10.47836%2fpjst.31.1.19&partnerID=40&md5=f262835911afcd4a739cfb3b2461d49b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34668
dc.identifier.volume31
dc.pagecount14
dc.publisherUniversiti Putra Malaysia Pressen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofHybrid Gold Open Access
dc.sourceScopus
dc.sourcetitlePertanika Journal of Science and Technology
dc.subjectIoT
dc.subjectmachine learning
dc.subjectoverflow mechanism
dc.subjectwaste classification
dc.subjectwaste collection
dc.subjectwaste management
dc.titleESS-IoT: The Smart Waste Management System for General Householden_US
dc.typeArticleen_US
dspace.entity.typePublication
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