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\begin{document}

\title{Security-Aware Data Offloading and Resource Allocation For MEC Systems:
A Deep Reinforcement Learning}



\author[1]{Ibrahim Elgendy }%
\author[2]{Ammar Muthanna }%
\author[1]{Mohammad Hammoudeh }%
\author[1]{Hadil Ahmed Shaiba }%
\author[1]{Devrim Unal }%
\author[1]{Mashael Khayyat }%
\affil[1]{Affiliation not available}%
\affil[2]{SPbSUT}%


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  \date{May 26, 2024}


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\begin{abstract}
The Internet of Things (IoT) is permeating our daily lives where it can
provide data collection tools and important measurement to inform our
decisions. In addition, they are continually generating massive amounts
of data and exchanging essential messages over networks for further
analysis. The promise of low communication latency, security enhancement
and the efficient utilization of bandwidth leads to the new shift change
from Mobile Cloud Computing (MCC) towards Mobile Edge Computing (MEC).
In this study, we propose an advanced deep reinforcement resource
allocation and securityaware data offloading model that considers the
computation and radio resources of industrial IoT devices to guarantee
that shared resources between multiple users are utilized in an
efficient way. This model is formulated as an optimization problem with
the goal of decreasing the consumption of energy and computation delay.
This type of problem is NP-hard, due to the curseof-dimensionality
challenge, thus, a deep learning optimization approach is presented to
find an optimal solution. Additionally, an AES-based cryptographic
approach is implemented as a security layer to satisfy data security
requirements. Experimental evaluation results show that the proposed
model can reduce offloading overhead by up to 13.2\% and 64.7\% in
comparison with full offloading and local execution while scaling well
for large-scale devices.%
\end{abstract}%



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