Abstract: This study explores the transformative potential of machine learning (ML) algorithms in optimizing the Routing Protocol for Low-Power and Lossy Networks (RPL), addressing critical challenges in Internet of Things (IoT) networks, such as Expected Transmission Count (ETX), latency, and energy consumption. The research evaluates the performance of Random Forest, Gradient Boosting, Artificial Neural Networks (ANNs), and Q-Learning across IoT network simulations with varying scales (50, 100, and 150 nodes). Results indicate that tree-based models, particularly Random Forest and Gradient Boosting, demonstrate robust predictive capabilities for ETX and latency, achieving consistent results in smaller and medium-sized networks. Specifically, for 50-node networks, Neural Networks achieved the best performance with the lowest latency (2.43862 ms) and the best ETX (5.29557), despite slightly higher energy consumption. For 100-node networks, Q-Learning stood out with the lowest energy consumption (1.62973 J) and competitive ETX (2.70647), though at the cost of increased latency. In 150-node networks, Q-Learning again outperformed other models, achieving the lowest latency (0.68 ms) and energy consumption (2.21 J), though at the cost of higher ETX. Neural Networks excel in capturing non-linear dependencies but face limitations in energy-related metrics, while Q-Learning adapts dynamically to network changes, achieving remarkable latency reductions at the cost of transmission efficiency. The findings highlight key trade-offs between performance metrics and emphasize the need for algorithmic strategies tailored to specific IoT applications. This work not only validates the scalability and adaptability of ML approaches but also lays the foundation for intelligent and efficient IoT network optimization, laying the groundwork for future advancements in sustainable and scalable IoT networks.

Mansour Lmkaiti, Ibtissam Larhlimi, Maryem Lachgar, Houda Moudni and Hicham Mouncif, “Advanced Optimization of RPL-IoT Protocol Using ML Algorithms” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01602135

@article{Lmkaiti2025,
title = {Advanced Optimization of RPL-IoT Protocol Using ML Algorithms},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01602135},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01602135},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Mansour Lmkaiti and Ibtissam Larhlimi and Maryem Lachgar and Houda Moudni and Hicham Mouncif}
}