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Communication Dans Un Congrès Année : 2021

WKNN indoor Wi-Fi localization method using k-means clustering based radio mapping

Siyang Liu
  • Fonction : Auteur
  • PersonId : 183103
  • IdHAL : siyang-liu
Raul de Lacerda
Jocelyn Fiorina

Résumé

Wifi fingerprinting using received signal strength has been widely studied for indoor localization. Classic similaritybased methods like weighted K-nearest neighbor (WKNN) localize targets by searching the best matching fingerprint in the dataset. Performance of these methods suffers from RSS variance and they are slow under a large size of fingerprint dataset. In this paper, we propose a WKNN localization strategy using k-means clustering radio mapping to improve localization precision while mitigating computational complexity.
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Dates et versions

hal-03697714 , version 1 (17-06-2022)

Identifiants

Citer

Siyang Liu, Raul de Lacerda, Jocelyn Fiorina. WKNN indoor Wi-Fi localization method using k-means clustering based radio mapping. 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Apr 2021, Helsinki, Finland. ⟨10.1109/VTC2021-Spring51267.2021.9448961⟩. ⟨hal-03697714⟩
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