Bhatti, Mansoor Ahmed and Riaz, Rabia and Rizvi, Sanam Shahla and Shokat, Sana and Riaz, Farina 
ORCID: https://orcid.org/0000-0001-9223-7117 and Kwon, Se Jin
(2020)
Outlier detection in indoor localization and Internet of Things (IoT) using machine learning.
Journal of Communications and Networks, 22 (3).
pp. 236-243.
ISSN 1229-2370
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Text (Published Version)
Outlier_detection_in_indoor_localization_and_Internet_of_Things_IoT_using_machine_learning.pdf Available under License Creative Commons Attribution Non-commercial 4.0. Download (860kB) | Preview |
Abstract
In Internet of things (IoT) millions of devices are intel- ligently connected for providing smart services. Especially in in- door localization environment, that is one of the most concerning topic of smart cities, internet of things and wireless sensor net- works. Many technologies are being used for localization purpose in indoor environment and Wi-Fi using received signal strengths (RSSs) is one of them. Wi-Fi RSSs are sensitive to reflection, re- fraction, interference and channel noise that cause irregularity in signal strengths. The irregular and anomalous RSS values, used in a Wi-Fi indoor localization environment, cannot define the location of any unknown node correctly. Therefore, this research has de- veloped an outlier detection technique named as iF_Ensemble for Wi-Fi indoor localization environment by analyzing RSSs us- ing the combination of supervised, unsupervised and ensemble ma- chine learning methods. In this research isolation forest (iForest) is used as an unsupervised learning method. Supervised learning method includes support vector machine (SVM), K-nearest neigh- bor (KNN) and random forest (RF) classifiers with stacking that is an ensemble learning method. For the evaluation purpose accu- racy, precision, recall, F-score and ROC-AUC curve are used. The evaluation of used machine learning method provides high accu- racy of 97.8 percent with proposed outlier detection methods and almost 2 percent improvement in the accuracy of localization pro- cess in indoor environment after eliminating outliers.
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| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Faculty/School / Institute/Centre: | No Faculty |
| Faculty/School / Institute/Centre: | No Faculty |
| Date Deposited: | 02 Nov 2021 01:37 |
| Last Modified: | 03 Nov 2021 06:37 |
| Uncontrolled Keywords: | Internet of things, localization, outliers, outliers de- tection |
| Fields of Research (2008): | 08 Information and Computing Sciences > 0805 Distributed Computing > 080503 Networking and Communications 08 Information and Computing Sciences > 0805 Distributed Computing > 080505 Web Technologies (excl. Web Search) |
| Fields of Research (2020): | 46 INFORMATION AND COMPUTING SCIENCES > 4606 Distributed computing and systems software > 460609 Networking and communications |
| Identification Number or DOI: | https://doi.org/10.1109/JCN.2020.000018 |
| URI: | http://eprints.usq.edu.au/id/eprint/44024 |
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