A load balancing scheme based on deep-learning in IoT
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Hye-Young | - |
dc.contributor.author | Kim, Jong-Min | - |
dc.date.available | 2020-07-10T05:21:21Z | - |
dc.date.created | 2020-07-06 | - |
dc.date.issued | 2017-03 | - |
dc.identifier.issn | 1386-7857 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/6017 | - |
dc.description.abstract | Extending the current Internet with interconnected objects and devices and their virtual representation has been a growing trend in recent years. The Internet of Things (IoT) contribution is in the increased value of information created by the number of interconnections among things and the transformation of the processed information into knowledge for the benefit of society. Benefit due to the service controlled by communication between objects is now being increased by people who use these services in real life. The sensors are deployed to monitor one or more events in an unattended environment. A large number of the event data will be generated over a period of time in IoT. Hence, the load balancing protocol is critical considerations in the design of IoT. Therefore, we propose an agent Loadbot that measures network load and process structural configuration by analyzing a large amount of user data and network load, and applying Deep Learning's Deep Belief Network method in order to achieve efficient load balancing in IoT. Also, we propose an agent Balancebot that processes a neural load prediction algorithm based on Deep Learning's Q-learning method and neural prior ensemble. We address the key functions for our proposed scheme and simulate the efficiency of our proposed scheme using mathematical analysis. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.title | A load balancing scheme based on deep-learning in IoT | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Hye-Young | - |
dc.identifier.doi | 10.1007/s10586-016-0667-5 | - |
dc.identifier.scopusid | 2-s2.0-84995794342 | - |
dc.identifier.wosid | 000397969100070 | - |
dc.identifier.bibliographicCitation | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, v.20, no.1, pp.873 - 878 | - |
dc.relation.isPartOf | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | - |
dc.citation.title | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | - |
dc.citation.volume | 20 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 873 | - |
dc.citation.endPage | 878 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.subject.keywordAuthor | Load balancing | - |
dc.subject.keywordAuthor | Internet of things | - |
dc.subject.keywordAuthor | Deep belief network | - |
dc.subject.keywordAuthor | Q-learning | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
94, Wausan-ro, Mapo-gu, Seoul, 04066, Korea02-320-1314
COPYRIGHT 2020 HONGIK UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.