[IEICE-CS202303] 置くだけIoTの実現に向けたIoT機器グループ化における実環境向けCSIサンプリング手法の提案 / Proposal of CSI Samping for Practical Environment on Device Grouping toward Put-and-Play IoT Systems
n p FUSIC[jiokeng 20], SpotFi[kotaru 15] ➔ ※ Ishida Lab, Future University Hakodate Mar 2, 2023 9 [sen 12] You are facing the Mona Lisa: Spot localization using PHY layer information, ACM MobiSys [wang 16] CSI phase fingerprinnting for indoor localization with a deep learning approach, IEEE Internet Things J. 3(6) [wang 17] CSI-based fingerprinting for indoor localization: A deep learrning approach, IEEE Trans. Veh. Technol. 66(1) [jiokeng 20] When FTM discovered MUSIC: Accurate WiFi-based ranging in the presence of multipath, IEEE INFOCOM [kotaru 15] SpotFi: decimeter level localization using WiFi, ACM SIGCOMM CC Review
) p ARI=1.00 ➔ [joya 21] Ishida Lab, Future University Hakodate Mar 2, 2023 10 [ishida 22] Room-by-room device grouping for put-and-play IoT system, IEEE GLOBECOM [joya 21] Design of room-layout estimator using smart speaker, EAI MobiQuitous v v A B C D F E G G F E D C B A
Future University Hakodate Mar 2, 2023 11 Human location ARI 0.42 Dining room 0.27 Living room 1.00 Bedroom 0.29 Anywhere 0.95 Anywhere or no where 0.95
1 ARI=0.943 l 𝑁'$() = 2 ARI=0.991 l 𝑁'$() ≥ 4 ARI=1.00 p PCA p Random Ishida Lab, Future University Hakodate Mar 2, 2023 26 0 2 4 6 8 10 Nsamp 0.7 0.8 0.9 1.0 Mean ARI ICA PCA Random 44.4%