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RPL over Bluetooth Low Energy

DC Field Value Language
dc.contributor.authorLee, Taeseop-
dc.contributor.authorKim, Hyung-Sin-
dc.contributor.authorLee, Myung-Sup-
dc.contributor.authorBahk, Saewoong-
dc.date.accessioned2024-05-14T08:07:04Z-
dc.date.available2024-05-14T08:07:04Z-
dc.date.created2024-04-16-
dc.date.issued2015-11-
dc.identifier.citationSenSys'15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, pp.467-468-
dc.identifier.urihttps://hdl.handle.net/10371/202148-
dc.description.abstractIn this demo, we present interoperability between Bluetooth Low Energy (BLE) and IPv6 routing protocol for low power and lossy networks (RPL). To make the operation of RPL over BLE feasible, we design an adaptation layer between BLE and RPL, termed ALBER. Specifically, we develop three technical features in ALBER which enable BLE to be combined with RPL. First, it broadcasts RPL control messages through BLE using advertising channels with low energy consumption. Second, it updates RPL routing table considering connection management of BLE. Lastly, it estimates link quality based on round trip time of link layer ping packets to provide routing metric for RPL. We implement our ALBER on Linux kernel. This demo will present an operation example of RPL over BLE using ALBER in a small scale multi-hop topology, where each node comprises a Raspberry Pi platform and a BLE dongle.-
dc.language영어-
dc.publisherASSOC COMPUTING MACHINERY-
dc.titleRPL over Bluetooth Low Energy-
dc.typeArticle-
dc.identifier.doi10.1145/2809695.2817857-
dc.citation.journaltitleSenSys'15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems-
dc.identifier.wosid000380612400074-
dc.identifier.scopusid2-s2.0-84962784444-
dc.citation.endpage468-
dc.citation.startpage467-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorKim, Hyung-Sin-
dc.contributor.affiliatedAuthorBahk, Saewoong-
dc.type.docTypeProceedings Paper-
dc.description.journalClass1-
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  • Graduate School of Data Science
Research Area Distributed machine learning, Edge, Mobile AI

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