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

Mohamed I. AlHajri, Nazar T. Ali, Raed M. Shubair

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations

Abstract

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.

Original languageBritish English
Title of host publicationAI for Emerging Verticals
Subtitle of host publicationHuman-robot computing, sensing and networking
PublisherInstitution of Engineering and Technology
Pages205-224
Number of pages20
ISBN (Electronic)9781785619823
DOIs
StatePublished - 1 Jan 2021

Keywords

  • Adaptive feature selection
  • Cascaded machine learning approach
  • Classification accuracy
  • CTF
  • FCF
  • Feature selection
  • Indoor environment
  • Indoor environment classification
  • Indoor localization
  • Internet of things
  • IoT applications
  • k-NN method
  • Learning (artificial intelligence)
  • Pattern classification
  • Real-time deployment scenarios
  • Support vector machines
  • SVM

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