p Distributed wireless sensor node localization based on penguin search optimization
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Al Shayokh, Md | - |
dc.contributor.author | Shin, Soo Young | - |
dc.date.accessioned | 2022-02-04T01:40:10Z | - |
dc.date.available | 2022-02-04T01:40:10Z | - |
dc.date.created | 2022-02-04 | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 1300-0632 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/20332 | - |
dc.description.abstract | Wireless sensor networks (WSNs) have become popular for sensing areas-of-interest and performing assigned tasks based on information on the location of sensor devices. Localization in WSNs is aimed at designating distinct geographical information to the inordinate nodes within a search area. Biologically inspired algorithms are being applied extensively in WSN localization to determine inordinate nodes more precisely while consuming minimal computation time. An optimization algorithm belonging to the metaheuristic class and named penguin search optimization (PeSOA) is presented in this paper. It utilizes the hunting approaches in a collaborative manner to determine the inordinate nodes within an area of interest. Subsequently, the proposed algorithm is compared with four popular algorithms, namely particle swarm optimization (PSO), binary particle swarm optimization (BPSO), bat algorithm (BA), and cuckoo search algorithm (CS). The comparison is based on two performance metrics: localization accuracy and computation time to determine inordinate nodes. The results obtained from the simulation illustrate that PeSOA outperforms the other algorithms, achieving an accuracy higher than 30%. In terms of computation time to determine inordinate nodes, the proposed algorithm requires 28% less time (on average) than the other algorithms do. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY | - |
dc.title | p Distributed wireless sensor node localization based on penguin search optimization | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Al Shayokh, Md | - |
dc.contributor.affiliatedAuthor | Shin, Soo Young | - |
dc.identifier.doi | 10.3906/elk-2104-128 | - |
dc.identifier.wosid | 000745996200001 | - |
dc.identifier.bibliographicCitation | TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, v.30, no.1, pp.50 - 62 | - |
dc.relation.isPartOf | TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES | - |
dc.citation.title | TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES | - |
dc.citation.volume | 30 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 50 | - |
dc.citation.endPage | 62 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | RANGE-FREE LOCALIZATION | - |
dc.subject.keywordAuthor | Wireless sensor networks | - |
dc.subject.keywordAuthor | localization | - |
dc.subject.keywordAuthor | Penguin search algorithm | - |
dc.subject.keywordAuthor | optimization | - |
dc.subject.keywordAuthor | computation time | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
350-27, Gumi-daero, Gumi-si, Gyeongsangbuk-do, Republic of Korea (39253)054-478-7170
COPYRIGHT 2020 Kumoh 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.