Quasi-Mapping and Satisfying IoT Availability with a Penalty-Based Algorithm
DC Field | Value | Language |
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dc.contributor.author | Rahmani, A.M. | - |
dc.contributor.author | Naqvi, R.A. | - |
dc.contributor.author | Ali, S. | - |
dc.contributor.author | Mirmahaleh, S.Y.H. | - |
dc.contributor.author | Hosseinzadeh, M. | - |
dc.date.accessioned | 2022-01-07T01:41:05Z | - |
dc.date.available | 2022-01-07T01:41:05Z | - |
dc.date.created | 2021-12-24 | - |
dc.date.issued | 2021-12 | - |
dc.identifier.issn | 2227-7390 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/83156 | - |
dc.description.abstract | The Internet of things and medical things (IoT) and (IoMT) technologies have been de-ployed to simplify humanity’s life, which the complexity of communications between their layers was increased by rising joining the applications to IoT and IoMT-based infrastructures. The issue is challenging for decision-making and the quality of service where some researchers addressed the reward-based methods to tackle the problems by employing reinforcement learning (RL) algorithms and deep neural networks (DNNs). Nevertheless, satisfying its availability remains a challenge for the quality of service due to the lack of imposing a penalty to the defective devices after detecting faults. This paper proposes a quasi-mapping method to transfer the roles of sensors and services onto a neural network’s nodes to satisfy IoT-based applications’ availability using a penalty-back-warding approach into the NN’s weights and prunes weak neurons and synaptic weights (SWs). We reward the sensors and fog services, and the connection weights between them when are cov-ered the defective nodes’ output. Additionally, this work provides a decision-making approach to dedicate the suitable service to the requester using employing a threshold value in the NN’s output layer according to the application. By providing an intelligent algorithm, the study decides to provide a service based on its availability and updating initial information, including faulty devices and new joined components. The observations and results prove decision-making accuracy for dif-ferent IoT-based applications by approximately 95.8–97% without imposing the cost. The study re-duces energy consumption and delay by approximately 64.71% and 47.4% compared without using neural networks besides creating service availability. This idea affects deploying IoT infrastructures to decision-making about providing appropriate services in critical situations because of removing defective devices and joining new components by imposing penalties and rewards by the designer, respectively. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | Mathematics | - |
dc.title | Quasi-Mapping and Satisfying IoT Availability with a Penalty-Based Algorithm | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000735612200001 | - |
dc.identifier.doi | 10.3390/math9243286 | - |
dc.identifier.bibliographicCitation | Mathematics, v.9, no.24 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85121286610 | - |
dc.citation.title | Mathematics | - |
dc.citation.volume | 9 | - |
dc.citation.number | 24 | - |
dc.contributor.affiliatedAuthor | Hosseinzadeh, M. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Availability | - |
dc.subject.keywordAuthor | De-cision-making | - |
dc.subject.keywordAuthor | Internet of things (IoT) | - |
dc.subject.keywordAuthor | Neural network (NN) | - |
dc.subject.keywordAuthor | Penalty | - |
dc.subject.keywordAuthor | Pruning | - |
dc.subject.keywordAuthor | Quasi-mapping | - |
dc.subject.keywordPlus | UAV | - |
dc.subject.keywordPlus | SCHEME | - |
dc.subject.keywordPlus | TRUST | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Mathematics | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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