English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper, we study fingerprinting-based indoor localization in commodity 5-GHz WiFi networks. We first theoretically and experimentally validate three hypotheses on the channel state information (CSI) data of 5-GHz OFDM channels. We then propose a system termed BiLoc, which uses bi-modality deep learning for localization in the indoor environment using off-the-shelf WiFi devices. We develop a deep learning-based algorithm to exploit bi-modal data, i.e., estimated angle of arrivings and average amplitudes (which are calibrated CSI data using several proposed techniques), for both the off-line and online stages of indoor fingerprinting. The proposed BiLoc system is implemented using commodity WiFi devices. Its superior performance is validated with extensive experiments under three typical indoor environments and through comparison with three benchmark schemes.

BiLoc: Bi-Modal Deep Learning for Indoor Localization With Commodity 5GHz WiFi