  The surge in Internet of Things (IoT) devices and data generation highlights
the limitations of traditional cloud computing in meeting demands for
immediacy, Quality of Service, and location-aware services. Fog computing
emerges as a solution, bringing computation, storage, and networking closer to
data sources. This study explores the role of Deep Reinforcement Learning in
enhancing fog computing's task offloading, aiming for operational efficiency
and robust security. By reviewing current strategies and proposing future
research directions, the paper shows the potential of Deep Reinforcement
Learning in optimizing resource use, speeding up responses, and securing
against vulnerabilities. It suggests advancing Deep Reinforcement Learning for
fog computing, exploring blockchain for better security, and seeking
energy-efficient models to improve the Internet of Things ecosystem.
Incorporating artificial intelligence, our results indicate potential
improvements in key metrics, such as task completion time, energy consumption,
and security incident reduction. These findings provide a concrete foundation
for future research and practical applications in optimizing fog computing
architectures.
