Fernando Jurado-Lasso
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F. Fernando Jurado-Lasso
Engineer specializing in IoT, Networked Embedded Systems, and Machine Learning for communications and networking
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  • IEEE Internet of Things Journal
  • March 2025
LEACH-RLC: Enhancing IoT Data Transmission with Optimized Clustering and Reinforcement Learning
Authors
Affiliations

F. F. Jurado-Lasso

Technical University of Denmark

J. F. Jurado

Universidad Nacional de Colombia

X. Fafoutis

Technical University of Denmark

Published

March 2025

Doi

10.1109/JIOT.2025.3552126

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DOI

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Abstract
Wireless Sensor Networks (WSNs) play a pivotal role in enabling Internet of Things (IoT) devices with sensing and actuation capabilities. Operating in remote and resourceconstrained environments, these IoT devices face challenges related to energy consumption, crucial for network longevity. Existing clustering protocols often suffer from high control overhead, inefficient cluster formation, and poor adaptability to dynamic network conditions, leading to suboptimal data transmission and reduced network lifetime. This paper introduces Low-Energy Adaptive Clustering Hierarchy with Reinforcement Learning-based Controller (LEACH-RLC), a novel clustering protocol designed to address these limitations by employing a Mixed Integer Linear Programming (MILP) approach for strategic selection of Cluster Heads (CHs) and node-to-cluster assignments. Additionally, it integrates a Reinforcement Learning (RL) agent to …
BibTeX citation
                        @article{JuradoFafoutis2025,
author = {Jurado Lasso, Fabian Fernando and Jurado, Jesus Fabian and
Fafoutis, Xenofon},
title = {LEACH-RLC: {Enhancing} {IoT} {Data} {Transmission} with
{Optimized} {Clustering} and {Reinforcement} {Learning}},
journal = {IEEE Internet of Things Journal},
date = {2025-03},
doi = {10.1109/JIOT.2025.3552126}
}
Copyright

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2018-2025 F. Fernando Jurado-Lasso. Except where otherwise noted, all text and images licensed under Creative Commons CC BY 4.0

ORCID 0000-0002-5005-781X PGP public key   Fingerprint:
FC00 72B7 B1ED B725 95A5  35E2 C7FF 3CFD 3347 1693

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