A machine learning approach for the classification of indoor environments using RF signatures

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

Efficient deployment of Internet of Things (IoT) sensors primarily depends on allowing the adjustment of sensor power consumption according to the radio frequency (RF) propagation channel which is dictated by the type of the surrounding indoor environment. This paper develops a machine learning approach for indoor environment classification by exploiting support vector machine (SVM) based on RF signatures computed from real-time measurements. Results obtained demonstrate that the combination of received signal strength (RSS) and channel transfer function (CTF) yields a classification accuracy of 83.0% for identifying the type of the indoor environment.

Original languageBritish English
Title of host publication2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1060-1062
Number of pages3
ISBN (Electronic)9781728112954
DOIs
StatePublished - 20 Feb 2019
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: 26 Nov 201829 Nov 2018

Publication series

Name2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
Country/TerritoryUnited States
CityAnaheim
Period26/11/1829/11/18

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