LOA-RPL: Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sennan, Sankar | - |
dc.contributor.author | Ramasubbareddy, Somula | - |
dc.contributor.author | Nayyar, Anand | - |
dc.contributor.author | Nam, Yunyoung | - |
dc.contributor.author | Abouhawwash, Mohamed | - |
dc.date.accessioned | 2021-09-10T06:27:13Z | - |
dc.date.available | 2021-09-10T06:27:13Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1546-2218 | - |
dc.identifier.issn | 1546-2226 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/19089 | - |
dc.description.abstract | Energy conservation is a significant task in the Internet of Things (IoT) because IoT involves highly resource-constrained devices. Clustering is an effective technique for saving energy by reducing duplicate data. In a clus-tering protocol, the selection of a cluster head (CH) plays a key role in prolong-ing the lifetime of a network. However, most cluster-based protocols, including routing protocols for low-power and lossy networks (RPLs), have used fuzzy logic and probabilistic approaches to select the CH node. Consequently, early battery depletion is produced near the sink. To overcome this issue, a lion optimization algorithm (LOA) for selecting CH in RPL is proposed in this study. LOA-RPL comprises three processes: cluster formation, CH selection, and route establishment. A cluster is formed using the Euclidean distance. CH selection is performed using LOA. Route establishment is implemented using residual energy information. An extensive simulation is conducted in the network simulator ns-3 on various parameters, such as network lifetime, power consumption, packet delivery ratio (PDR), and throughput. The performance of LOA-RPL is also compared with those of RPL, fuzzy rule-based energy -efficient clustering and immune-inspired routing (FEEC-IIR), and the routing scheme for IoT that uses shuffled frog-leaping optimization algorithm (RISA-RPL). The performance evaluation metrics used in this study are network lifetime, power consumption, PDR, and throughput. The proposed LOA-RPL increases network lifetime by 20% and PDR by 5%-10% compared with RPL, FEEC-IIR, and RISA-RPL. LOA-RPL is also highly energy-efficient compared with other similar routing protocols. | - |
dc.format.extent | 21 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Tech Science Press | - |
dc.title | LOA-RPL: Novel Energy-Efficient Routing Protocol for the Internet of Things Using Lion Optimization Algorithm to Maximize Network Lifetime | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.32604/cmc.2021.017360 | - |
dc.identifier.scopusid | 2-s2.0-85107868148 | - |
dc.identifier.wosid | 000659131200026 | - |
dc.identifier.bibliographicCitation | Computers, Materials and Continua, v.69, no.1, pp 351 - 371 | - |
dc.citation.title | Computers, Materials and Continua | - |
dc.citation.volume | 69 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 351 | - |
dc.citation.endPage | 371 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.subject.keywordAuthor | Internet of things | - |
dc.subject.keywordAuthor | cluster head | - |
dc.subject.keywordAuthor | clustering protocol | - |
dc.subject.keywordAuthor | optimization algorithm | - |
dc.subject.keywordAuthor | lion optimization algorithm | - |
dc.subject.keywordAuthor | network lifetime | - |
dc.subject.keywordAuthor | routing protocol | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(31538) 22, Soonchunhyang-ro, Asan-si, Chungcheongnam-do, Republic of Korea+82-41-530-1114
COPYRIGHT 2021 by SOONCHUNHYANG 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.