Skip to Main content Skip to Navigation
Conference papers

CNLoc: Channel State Information Assisted Indoor WLAN Localization Using Nomadic Access Points

Abstract : Wireless local area network (WLAN) based indoor localization is expanding its fast-paced adoption to facilitate a variety of indoor location-based services (ILBS). Unfortunately, the performance of current WLAN localization systems relying on fixed access points (APs) deployment is constrained by the spatial localizability variance (SLV) problem that different locations may exhibit significantly distinct localization resolution. Prior approaches tackle this problem through nomadic APs with favorable mobility to dynamically adjust the network topology. However, the lack of prior knowledge of nomadic AP’s position has been a challenge for location distinction and will lead to prohibitive performance degradation. In this paper, we propose and develop CNLoc, a novel CSI-based (Channel State Information) indoor WLAN localization framework to overcome the location uncertainty of nomadic APs. Our implementation and evaluation show that CNLoc can improve the accuracy with unknown location information of nomadic APs. We also discuss some open issues and new possibilities in future nomadic AP based indoor localization.
Keywords : WLAN CSI RSS Mobility
Document type :
Conference papers
Complete list of metadata

Cited literature [24 references]  Display  Hide  Download

https://hal.inria.fr/hal-02279560
Contributor : Hal Ifip <>
Submitted on : Thursday, September 5, 2019 - 1:31:38 PM
Last modification on : Friday, July 17, 2020 - 7:12:04 PM
Long-term archiving on: : Thursday, February 6, 2020 - 7:54:54 AM

File

477597_1_En_1_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Jiang Xiao, Huichuwu Li, He Li, Hai Jin. CNLoc: Channel State Information Assisted Indoor WLAN Localization Using Nomadic Access Points. 15th IFIP International Conference on Network and Parallel Computing (NPC), Nov 2018, Muroran, Japan. pp.1-12, ⟨10.1007/978-3-030-05677-3_1⟩. ⟨hal-02279560⟩

Share

Metrics

Record views

76

Files downloads

12