Classification of indoor environments for IoT applications: A machine learning approach

MI AlHajri, NT Ali, RM Shubair - IEEE Antennas and Wireless …, 2018 - ieeexplore.ieee.org
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 …

Indoor localization for IoT using adaptive feature selection: A cascaded machine learning approach

MI AlHajri, NT Ali, RM Shubair - IEEE Antennas and Wireless …, 2019 - ieeexplore.ieee.org
Evolving Internet-of-things applications often require the use of sensor-based indoor
tracking and positioning, for which the performance is significantly improved by identifying …

A novel real-time deep learning approach for indoor localization based on RF environment identification

Z Chen, MI AlHajri, M Wu, NT Ali… - IEEE Sensors …, 2020 - ieeexplore.ieee.org
Internet-of-Things (IoT) applications often require the use of sensor-based indoor tracking
and positioning, for which the performance is significantly enhanced by identifying the type …

Integrating Cost-231 Multiwall Propagation and Adaptive Data Rate Method for Access Point Placement Recommendation

FS Mukti, PDP Adi, DA Prasetya… - … Journal of Advanced …, 2021 - eprints.unmer.ac.id
A new approach has been developed to provide an overview about signal behavior in
indoor environments using Cost-231 Multiwall Model (Cost-231 MWM) and Adaptive Data …

Indoor localization under limited measurements: A cross-environment joint semi-supervised and transfer learning approach

MI AlHajri, RM Shubair, M Chafii - 2021 IEEE 22nd International …, 2021 - ieeexplore.ieee.org
The development of highly accurate deep learning methods for indoor localization is often
hindered by the unavailability of sufficient data measurements in the desired environment to …

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

MI AlHajri, NT Ali, RM Shubair - 2018 IEEE Global Conference …, 2018 - ieeexplore.ieee.org
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 …

Capacity Maximization of the 6G Networks Deploying IRS

M Mahbub, RM Shubair - arxiv preprint arxiv:2303.11807, 2023 - arxiv.org
The objective of the work is to improve the capacity of the micro cell, ie, enabling the micro
cell base station of a two-tier network to serve an increased number of devices. Therefore …

Channel prediction with liquid time-constant networks: an online and adaptive approach

H Yin, Y Zhou, L Cao, Y Xu - 2021 IEEE 94th Vehicular …, 2021 - ieeexplore.ieee.org
Accurate channel state information (CSI) prediction and estimation are critical to the
communication system to adapt to the rapid change of wireless channels. The CSI feedback …

Downlink Received Power Performance Analysis of IRS for 6G Networks

M Mahbub, RM Shubair - arxiv preprint arxiv:2305.03174, 2023 - arxiv.org
The Internet of Things (IoT) is going to be a few of the most influential and critical role
spectators in the post-5G and 6G wireless networking paradigm because IoT is being …

Intelligent Reflecting Surfaces for the Enhancement of 6G Internet of Things

M Mahbub, RM Shubair - arxiv preprint arxiv:2207.07999, 2022 - arxiv.org
With the advancement of sensing technologies over the years, it has become critical to
ensure the seamless connectivity of the Internet of Things (IoT) gadgets. With the …