Classification of Indoor Environments for IoT Applications: A Machine Learning Approach

Evolving Internet-of-Things (IoT) applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly improved by classifying the type of the surrounding indoor environment. This classification is of high importance since it leads to efficient power consumption when operating the deployed IoT sensors. This letter presents a machine learning approach for indoor environment classification based on real-time measurements of the radio frequency (RF) signal in a realistic environment. Several machine learning classification methods are explored including decision trees, support vector machine, and
$k$
-nearest neighbor using different RF features. Results obtained show that a machine learning approach based on weighted
$k$
-nearest neighbor method, which utilizes a combination of channel transfer function and frequency coherence function, outperforms the other methods in classifying the type of indoor environment with an accuracy of 99.3%. The predication time was found to be below 10 
$\mu$
s, which verifies that the adopted algorithm is a successful candidates for real-time deployment scenarios.