Adaptive sink mobility management scheme for wireless sensor networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hwang, Kwang-il | - |
dc.contributor.author | Eom, Doo-seop | - |
dc.date.accessioned | 2021-09-09T06:42:57Z | - |
dc.date.available | 2021-09-09T06:42:57Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2006 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/123187 | - |
dc.description.abstract | In wireless sensor networks, it is important to efficiently disseminate information from each source to a sink node. In particular, in mobile sink applications, due to the sink mobility, a stationary dissemination path may no longer be effective. The path will have to be continuously reconfigured according to the current location of the sink. In this paper, an Adaptive Reversal Tree (ART) protocol, based on the Adaptive Reversal algorithm and dynamic Root change mechanism, is proposed. Data dissemination from each source to a mobile sink can be easily achieved along the ART without additional control overhead, because the ART proactively performs adaptive sink mobility management. In addition, the ART can maintain a robust tree structure by quickly recovering the partitioned tree with minimum packet transmission. Finally, the simulation results demonstrate that the ART is a considerably energy-efficient and robust protocol. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER-VERLAG BERLIN | - |
dc.title | Adaptive sink mobility management scheme for wireless sensor networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Eom, Doo-seop | - |
dc.identifier.wosid | 000240542600049 | - |
dc.identifier.bibliographicCitation | UBIQUITOUS INTELLIGENCE AND COMPUTING, PROCEEDINGS, v.4159, pp.478 - 487 | - |
dc.relation.isPartOf | UBIQUITOUS INTELLIGENCE AND COMPUTING, PROCEEDINGS | - |
dc.citation.title | UBIQUITOUS INTELLIGENCE AND COMPUTING, PROCEEDINGS | - |
dc.citation.volume | 4159 | - |
dc.citation.startPage | 478 | - |
dc.citation.endPage | 487 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
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