CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles

Joint Authors

Akbar, Ali Hammad
Akram, Beenish Ayesha
Kim, Ki-Hyung

Source

Mobile Information Systems

Issue

Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2018-10-01

Country of Publication

Egypt

No. of Pages

11

Main Subjects

Telecommunications Engineering

Abstract EN

Indoor localization has continued to garner interest over the last decade or so, due to the fact that its realization remains a challenge.

Fingerprinting-based systems are exciting because these embody signal propagation-related information intrinsically as compared to radio propagation models.

Wi-Fi (an RF technology) is best suited for indoor localization because it is so widely deployed that literally, no additional infrastructure is required.

Since location-based services depend on the fingerprints acquired through the underlying technology, smart mechanisms such as machine learning are increasingly being incorporated to extract intelligible information.

We propose CEnsLoc, a new easy to train-and-deploy Wi-Fi localization methodology established on GMM clustering and Random Forest Ensembles (RFEs).

Principal component analysis was applied for dimension reduction of raw data.

Conducted experimentation demonstrates that it provides 97% accuracy for room prediction.

However, artificial neural networks, k-nearest neighbors, K∗, FURIA, and DeepLearning4J-based localization solutions provided mean 85%, 91%, 90%, 92%, and 73% accuracy on our collected real-world dataset, respectively.

It delivers high room-level accuracy with negligible response time, making it viable and befitted for real-time applications.

American Psychological Association (APA)

Akram, Beenish Ayesha& Akbar, Ali Hammad& Kim, Ki-Hyung. 2018. CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles. Mobile Information Systems،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1204745

Modern Language Association (MLA)

Akbar, Ali Hammad…[et al.]. CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles. Mobile Information Systems No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1204745

American Medical Association (AMA)

Akram, Beenish Ayesha& Akbar, Ali Hammad& Kim, Ki-Hyung. CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles. Mobile Information Systems. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1204745

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1204745