Abstract

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.

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