# LSML-SF: a lightweight stacked ML approach for spreading factor allocation in mobile IoT LoRaWAN networks

## TL;DR

This paper introduces a lightweight machine learning model to improve communication efficiency in mobile IoT LoRaWAN networks.

## Contribution

A novel stacked-ML approach for spreading factor allocation in LoRaWAN with low computational requirements and high accuracy.

## Key findings

- The LSML-SF model achieves 85% out-of-fold cross-validation accuracy.
- The DNN component uses only 12,602 parameters and 12.3k MAC operations per inference.
- LSML-SF outperforms traditional ADR and ML methods in packet success and energy efficiency.

## Abstract

The expansion of the Internet of Things (IoT) into consumer applications demands robust and energy-efficient communication protocols. Long-range wide area network (LoRaWAN) is a key enabler, but its performance depends on optimal spreading factor (SF) allocation, where traditional adaptive data rate (ADR) mechanisms are inadequate in dynamic environments. This study presents a novel lightweight stacked-ML approach for spreading factor (LSML-SF) allocation in mobile IoT LoRaWAN network. We propose a stacked ensemble model that jointly combines a linear stochastic gradient descent classifier (log-loss), a gradient boosting model, and a deep neural network (DNN) through a logistic regression meta-learner. The LSML-SF is trained on a vast dataset of 225,109 samples generated from ns-3 simulations, and our model achieves an out-of-fold cross-validation accuracy of 85%. Importantly, we demonstrate the practical feasibility of our approach through a rigorous computational analysis, showing the DNN component requires only 12,602 parameters and 12.3k MAC operations per inference. When integrated into ns-3 simulations, our LSML-SF framework significantly outperforms traditional ADR mechanisms and existing ML approaches, improving the packet success ratio and reducing energy consumption, thereby extending the operational lifespan of consumer IoT devices.