2025

Journal

Papier journal

The rapid development of devices on the Internet of Things
(IoT) and the diversity of their applications have made them
ubiquitous. However, deploying these devices in large-scale
networks presents several challenges, including limited energy
capacity, security concerns, unreliable links, and transmission
delays. This paper, proposes a multi-objective optimization
approach for wireless IoT networks based on machine learning
techniques. Specifically, a clustering scheme is developd by
using an improved k-means algorithm. This is combined
with a dynamic routing strategy based on multi-objective
Q-learning using parallel Q-tables. This approach leads to
measurable gains in energy efficiency, transmission latency,
and reliability. Compared to existing approaches in similar
contexts, such as weighted sum, the proposed solution achieves
significant improvements in overall network performance.

Bibtex