Conformal Prediction for Indoor Localisation with Fingerprinting Method. / Nguyen, Khuong; Luo, Zhiyuan.

Artificial Intelligence Applications and Innovations: First Conformal Prediction and Its Applications Workshop (COPA 2012). Halkidiki, Greece : Springer, 2012. p. 214-223.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published

Standard

Conformal Prediction for Indoor Localisation with Fingerprinting Method. / Nguyen, Khuong; Luo, Zhiyuan.

Artificial Intelligence Applications and Innovations: First Conformal Prediction and Its Applications Workshop (COPA 2012). Halkidiki, Greece : Springer, 2012. p. 214-223.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Nguyen, K & Luo, Z 2012, Conformal Prediction for Indoor Localisation with Fingerprinting Method. in Artificial Intelligence Applications and Innovations: First Conformal Prediction and Its Applications Workshop (COPA 2012). Springer, Halkidiki, Greece, pp. 214-223. https://doi.org/10.1007/978-3-642-33412-2_22

APA

Nguyen, K., & Luo, Z. (2012). Conformal Prediction for Indoor Localisation with Fingerprinting Method. In Artificial Intelligence Applications and Innovations: First Conformal Prediction and Its Applications Workshop (COPA 2012) (pp. 214-223). Springer. https://doi.org/10.1007/978-3-642-33412-2_22

Vancouver

Nguyen K, Luo Z. Conformal Prediction for Indoor Localisation with Fingerprinting Method. In Artificial Intelligence Applications and Innovations: First Conformal Prediction and Its Applications Workshop (COPA 2012). Halkidiki, Greece: Springer. 2012. p. 214-223 https://doi.org/10.1007/978-3-642-33412-2_22

Author

Nguyen, Khuong ; Luo, Zhiyuan. / Conformal Prediction for Indoor Localisation with Fingerprinting Method. Artificial Intelligence Applications and Innovations: First Conformal Prediction and Its Applications Workshop (COPA 2012). Halkidiki, Greece : Springer, 2012. pp. 214-223

BibTeX

@inproceedings{390bf35142c948d286614269f3e9818f,
title = "Conformal Prediction for Indoor Localisation with Fingerprinting Method",
abstract = "Indoor localisation is the state-of-the-art to identify and observe a moving human or object inside a building. Location Fingerprinting is a cost-effective software-based solution utilising the built-in wireless signal of the building to estimate the most probable position of a real-time signal data. In this paper, we apply the Conformal Prediction (CP) algorithm to further enhance the Fingerprinting method. We design a new nonconformity measure with the Weighted K-nearest neighbours (W-KNN) as the underlying algorithm. Empirical results show good performance of the CP algorithm. ",
keywords = "indoor localisation, fingerprinting, Conformal Prediction",
author = "Khuong Nguyen and Zhiyuan Luo",
year = "2012",
month = sep,
doi = "10.1007/978-3-642-33412-2_22",
language = "English",
isbn = "978-3-642-33411-5",
pages = "214--223",
booktitle = "Artificial Intelligence Applications and Innovations",
publisher = "Springer",

}

RIS

TY - GEN

T1 - Conformal Prediction for Indoor Localisation with Fingerprinting Method

AU - Nguyen, Khuong

AU - Luo, Zhiyuan

PY - 2012/9

Y1 - 2012/9

N2 - Indoor localisation is the state-of-the-art to identify and observe a moving human or object inside a building. Location Fingerprinting is a cost-effective software-based solution utilising the built-in wireless signal of the building to estimate the most probable position of a real-time signal data. In this paper, we apply the Conformal Prediction (CP) algorithm to further enhance the Fingerprinting method. We design a new nonconformity measure with the Weighted K-nearest neighbours (W-KNN) as the underlying algorithm. Empirical results show good performance of the CP algorithm.

AB - Indoor localisation is the state-of-the-art to identify and observe a moving human or object inside a building. Location Fingerprinting is a cost-effective software-based solution utilising the built-in wireless signal of the building to estimate the most probable position of a real-time signal data. In this paper, we apply the Conformal Prediction (CP) algorithm to further enhance the Fingerprinting method. We design a new nonconformity measure with the Weighted K-nearest neighbours (W-KNN) as the underlying algorithm. Empirical results show good performance of the CP algorithm.

KW - indoor localisation

KW - fingerprinting

KW - Conformal Prediction

U2 - 10.1007/978-3-642-33412-2_22

DO - 10.1007/978-3-642-33412-2_22

M3 - Conference contribution

SN - 978-3-642-33411-5

SP - 214

EP - 223

BT - Artificial Intelligence Applications and Innovations

PB - Springer

CY - Halkidiki, Greece

ER -