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Indoor Localisation using Aroma Fingerprints: A First Sniff

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Indoor Localisation using Aroma Fingerprints: A First Sniff. / Müller, Philipp; Lekkala, Jukka; Ali-Löytty, Simo; Piche, Robert.

2017 14th Workshop on Positioning, Navigation and Communications (WPNC). Bremen, Germany : IEEE, 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Müller, P, Lekkala, J, Ali-Löytty, S & Piche, R 2017, Indoor Localisation using Aroma Fingerprints: A First Sniff. in 2017 14th Workshop on Positioning, Navigation and Communications (WPNC). IEEE, Bremen, Germany, WORKSHOP ON POSITIONING, NAVIGATION AND COMMUNICATION, 1/01/00. https://doi.org/10.1109/WPNC.2017.8250046

APA

Müller, P., Lekkala, J., Ali-Löytty, S., & Piche, R. (2017). Indoor Localisation using Aroma Fingerprints: A First Sniff. In 2017 14th Workshop on Positioning, Navigation and Communications (WPNC) Bremen, Germany: IEEE. https://doi.org/10.1109/WPNC.2017.8250046

Vancouver

Müller P, Lekkala J, Ali-Löytty S, Piche R. Indoor Localisation using Aroma Fingerprints: A First Sniff. In 2017 14th Workshop on Positioning, Navigation and Communications (WPNC). Bremen, Germany: IEEE. 2017 https://doi.org/10.1109/WPNC.2017.8250046

Author

Müller, Philipp ; Lekkala, Jukka ; Ali-Löytty, Simo ; Piche, Robert. / Indoor Localisation using Aroma Fingerprints: A First Sniff. 2017 14th Workshop on Positioning, Navigation and Communications (WPNC). Bremen, Germany : IEEE, 2017.

Bibtex - Download

@inproceedings{586eb9d6394742f393469fefef7e7bb6,
title = "Indoor Localisation using Aroma Fingerprints: A First Sniff",
abstract = "Electronic noses (eNoses) can detect and classify a large variety of smells. They are, in general, much more sensitive than the human nose. Could they identify different indoor locations based on the locations' characteristic combinations of airborne chemicals? We study in this paper how well location can be determined in an indoor environment using only measurements from an ion mobility spectrometry eNose and a K nearest neighbour (KNN) classifier. Based on the results of test with real-world data eNose-based localisation seems to have potential but there are several questions and issues that still have to be addressed. This paper provides therefore a discussion of questions and issues that have to be studied in the future, and proposes potential solutions.",
author = "Philipp M{\"u}ller and Jukka Lekkala and Simo Ali-L{\"o}ytty and Robert Piche",
year = "2017",
month = "10",
doi = "10.1109/WPNC.2017.8250046",
language = "English",
publisher = "IEEE",
booktitle = "2017 14th Workshop on Positioning, Navigation and Communications (WPNC)",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Indoor Localisation using Aroma Fingerprints: A First Sniff

AU - Müller, Philipp

AU - Lekkala, Jukka

AU - Ali-Löytty, Simo

AU - Piche, Robert

PY - 2017/10

Y1 - 2017/10

N2 - Electronic noses (eNoses) can detect and classify a large variety of smells. They are, in general, much more sensitive than the human nose. Could they identify different indoor locations based on the locations' characteristic combinations of airborne chemicals? We study in this paper how well location can be determined in an indoor environment using only measurements from an ion mobility spectrometry eNose and a K nearest neighbour (KNN) classifier. Based on the results of test with real-world data eNose-based localisation seems to have potential but there are several questions and issues that still have to be addressed. This paper provides therefore a discussion of questions and issues that have to be studied in the future, and proposes potential solutions.

AB - Electronic noses (eNoses) can detect and classify a large variety of smells. They are, in general, much more sensitive than the human nose. Could they identify different indoor locations based on the locations' characteristic combinations of airborne chemicals? We study in this paper how well location can be determined in an indoor environment using only measurements from an ion mobility spectrometry eNose and a K nearest neighbour (KNN) classifier. Based on the results of test with real-world data eNose-based localisation seems to have potential but there are several questions and issues that still have to be addressed. This paper provides therefore a discussion of questions and issues that have to be studied in the future, and proposes potential solutions.

U2 - 10.1109/WPNC.2017.8250046

DO - 10.1109/WPNC.2017.8250046

M3 - Conference contribution

BT - 2017 14th Workshop on Positioning, Navigation and Communications (WPNC)

PB - IEEE

CY - Bremen, Germany

ER -