@inproceedings{8f2390a2b5c645e1afcc315f6481328a,
title = "A machine learning approach for the classification of indoor environments using RF signatures",
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.",
author = "Alhajri, \{Mohamed I.\} and Ali, \{Nazar T.\} and Shubair, \{Raed M.\}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 ; Conference date: 26-11-2018 Through 29-11-2018",
year = "2019",
month = feb,
day = "20",
doi = "10.1109/GlobalSIP.2018.8646600",
language = "British English",
series = "2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1060--1062",
booktitle = "2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings",
address = "United States",
}