Indoor localization based on visible light communication and machine learning algorithms
Alzahraa Ghonim, Wessam M. Salama, Ashraf A. M. Khalaf, Mohamed Shalaby
Abstract
An indoor localization system is proposed based on visible light communications, received signal strength, and machine learning algorithms. To acquire an accurate localization system, first, a dataset is collected. The dataset is then used with various machine learning algorithms for training purpose. Several evaluation metrics are used to estimate the robustness of the proposed system. Specifically, authors’ evaluation parameters are based on training time, testing time, classification accuracy, area under curve, F1-score, precision, recall, logloss, and specificity. It turned out that the proposed system is featured with high accuracy. The authors are able to achieve 99.5% for area under curve, 99.4% for classification accuracy, precision, F1, and recall. The logloss and precision are 4% and 99.7%, respectively. Moreover, root mean square error is used as an additional performance evaluation averaged to 0.136 cm.