A cascaded machine learningapproach for indoor classification and localization using adaptive feature selection

This chapter developed a machine learning approach for indoor environment classification based on real-time measurements of the RF signal. Several machine learning classification methods were contemplated, including DTs, SVM, and k-NN, using different RF features. Results obtained show that a machine learning approach using k-NN method, utilizing CTF and FCF, outperforms the other methods in identifying the type of the indoor environment with a classification accuracy of 99.3%. The predication time was obtained to be less than 10 u, s, which verifies that the embraced algorithm is successful for real-time deployment scenarios. The results of this chapter facilitate an efficient deployment of IoT applications in dynamic channels Chapter Contents: • 10.1 Introduction • 10.2 Indoor radio propagation channel • 10.2.1 Characteristics of RF indoor channel • 10.2.2 Design considerations for the RF indoor channel • 10.3 Data collection phase: practical measurements campaign • 10.4 Signatures of indoor environment • 10.4.1 Primary RF features • 10.4.2 Hybrid RF features • 10.5 Spatial correlation coefficient • 10.6 Machine learning algorithms • 10.6.1 Decision trees • 10.6.2 Support vector machine • 10.6.3 k-Nearest neighbor • 10.7 Cascaded Machine Learning Approach • 10.7.1 Machine learning for indoor environment classification • 10.7.2 Machine learning for localization position estimation • 10.8 Conclusion • References.