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\begin{document}
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\author[1]{Hossein Asadollahi}%
\author[1]{Nader Mokari}%
\author[1]{Hamid Saeedi}%
\author[1]{Halim Yanikomeroglu}%
\affil[1]{Affiliation not available}%


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  \date{April 27, 2026}


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\begin{abstract}
Indoor localization within buildings is paramount due to its diverse
applications, including the Internet of Things (IoT), healthcare, and
personnel monitoring. To improve the performance of indoor localization,
this study proposes an algorithm combining Deep Neural Networks (DNNs)
and Convolutional Neural Networks (CNNs) used within a hybrid model that
uses both cellular technology, i.e, base station (BS) and Wi-Fi access
point (AP). Within a realistic environment, we evaluate the performance
of our approach across four scenarios: (1) (2 APs + 2 BSs), (2) (1 AP +
2 BSs), (3) (2 APs), and (4) (2 BSs). Additionally, we consider both
stationary and seated user positions. Our results demonstrate
significant improvements over existing methods. Specifically, in the 2
APs + 2 BSs scenario, we achieve an average Euclidean distance error of
1.04 meters, with a maximum error of 2.51 meters, and the Root Mean
Square Error (RMSE) of 1.15 meters. We also obtain the corresponding
Cumulative Distribution Function (CDF) for the error which indicates
that 90\% of the time, the error is less than 1.69 meters. Our hybrid
model achieves over 99\% accuracy in classifying different building
floors and over 88\% accuracy in identifying user states (e.g., standing
or sitting). We validate our approach using smartphone-based indoor
localization in a two story residential building with robust
construction materials and double-layered walls.%
\end{abstract}%



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