Enabling Accurate Indoor Localization Using a Machine Learning Algorithm
Autor: | Kayhan Zrar Ghafoor, Haidar Abdulrahman Abbas |
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Rok vydání: | 2020 |
Předmět: |
Normalization (statistics)
Computer science RSS Decision tree naïve bayes Machine learning computer.software_genre Bayes' theorem Naive Bayes classifier decision tree Classifier (linguistics) support vector machine lcsh:Science Boolean function wireless access points business.industry computer.file_format Support vector machine indoor localization received signal strength lcsh:Q Artificial intelligence wireless fidelity fingerprinting business computer Algorithm |
Zdroj: | UHD Journal of Science and Technology, Vol 4, Iss 1, Pp 96-102 (2020) |
ISSN: | 2521-4217 2521-4209 |
DOI: | 10.21928/uhdjst.v4n1y2020.pp96-102 |
Popis: | In this paper, fingerprint referencing methods based on wireless fidelity Wi-Fi received signal strength (RSS) have used for indoor positioning. More precisely, Naïve Bayes, decision tree (DT), and support vector machine (SVM) one-to-one multi-classes and error-correcting-output-codes classifier are to enable accurate indoor positioning. Then, normalization is used to reduce positioning error by reducing the fluctuation and diverse distribution of the RSS values. Different devices are used in this experiment; the training dataset is not included in the main dataset. Nonetheless, the learned model by the SVM algorithm cannot be affected by the elimination of train datasets of the test device. The efficiency of DT is lower than the other machine learning algorithms, because it performs by Boolean function, and it provides the low accuracy of prediction for dataset than the algorithms. Naïve Bayes technique based on Bayes Theorem is better than DT and close to SVM for positioning approves that 1–1.5 m positioning accuracy for indoor environments can be achieved by the proposed approach which is an excellent result than traditional protocol. |
Databáze: | OpenAIRE |
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