@article{ART003332357},
author={JaeHyuk, Lee},
title={Design of a Device-Robust Bluetooth RSS-Based Localization Framework with Multi-Channel Representation Learning in IoT Environments},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2026},
volume={12},
number={2},
pages={39-47}
TY - JOUR
AU - JaeHyuk, Lee
TI - Design of a Device-Robust Bluetooth RSS-Based Localization Framework with Multi-Channel Representation Learning in IoT Environments
JO - Journal of Internet of Things and Convergence
PY - 2026
VL - 12
IS - 2
PB - The Korea Internet of Things Society
SP - 39
EP - 47
SN - 2466-0078
AB - This study proposes a Bluetooth RSS-based localization framework with multi-channel representation learning to address device heterogeneity in IoT environments. Unlike conventional fingerprint-based approaches, the proposed method directly processes variable-length MAC sets and models RSS using multi-channel features to capture structural signal characteristics. Experiments on the publicly available OutFin dataset show that the proposed model achieves 100% Top-1 and Top-5 accuracy in the in-device setting, outperforming k-NN, SVM, and MLP baselines. Under cross-device evaluation, traditional methods exhibit performance drops of up to 79%, whereas the proposed model maintains 93.74% Top-1 accuracy with only a 6.26% reduction. These results demonstrate that the proposed framework effectively mitigates cross-device degradation and provides robust, generalizable localization in practical IoT environments.
KW - Bluetooth RSS Localization;Deep Learning-Based Localization;Device Robustness;IoT Positioning System;Representation Learning
DO -
UR -
ER -
JaeHyuk, Lee. (2026). Design of a Device-Robust Bluetooth RSS-Based Localization Framework with Multi-Channel Representation Learning in IoT Environments. Journal of Internet of Things and Convergence, 12(2), 39-47.
JaeHyuk, Lee. 2026, "Design of a Device-Robust Bluetooth RSS-Based Localization Framework with Multi-Channel Representation Learning in IoT Environments", Journal of Internet of Things and Convergence, vol.12, no.2 pp.39-47.
JaeHyuk, Lee "Design of a Device-Robust Bluetooth RSS-Based Localization Framework with Multi-Channel Representation Learning in IoT Environments" Journal of Internet of Things and Convergence 12.2 pp.39-47 (2026) : 39.
JaeHyuk, Lee. Design of a Device-Robust Bluetooth RSS-Based Localization Framework with Multi-Channel Representation Learning in IoT Environments. 2026; 12(2), 39-47.
JaeHyuk, Lee. "Design of a Device-Robust Bluetooth RSS-Based Localization Framework with Multi-Channel Representation Learning in IoT Environments" Journal of Internet of Things and Convergence 12, no.2 (2026) : 39-47.
JaeHyuk, Lee. Design of a Device-Robust Bluetooth RSS-Based Localization Framework with Multi-Channel Representation Learning in IoT Environments. Journal of Internet of Things and Convergence, 12(2), 39-47.
JaeHyuk, Lee. Design of a Device-Robust Bluetooth RSS-Based Localization Framework with Multi-Channel Representation Learning in IoT Environments. Journal of Internet of Things and Convergence. 2026; 12(2) 39-47.
JaeHyuk, Lee. Design of a Device-Robust Bluetooth RSS-Based Localization Framework with Multi-Channel Representation Learning in IoT Environments. 2026; 12(2), 39-47.
JaeHyuk, Lee. "Design of a Device-Robust Bluetooth RSS-Based Localization Framework with Multi-Channel Representation Learning in IoT Environments" Journal of Internet of Things and Convergence 12, no.2 (2026) : 39-47.
