A Centralized Reinforcement Learning Framework for Adaptive Clustering
  with Low Control Overhead in IoT Networks
F. Fernando Jurado-Lasso J. F. Jurado Xenofon Fafoutis
Wireless Sensor Networks (WSNs) play a pivotal role in enabling Internet of
Things (IoT) devices with sensing and actuation capabilities. Operating in
remote and resource-constrained environments, these IoT devices face challenges
related to energy consumption, crucial for network longevity. Clustering
protocols have emerged as an effective solution to alleviate energy burdens on
IoT devices. This paper introduces Low-Energy Adaptive Clustering Hierarchy
with Reinforcement Learning-based Controller (LEACH-RLC), a novel clustering
protocol that employs a Mixed Integer Linear Programming (MILP) for strategic
selection of cluster heads (CHs) and node-to-cluster assignments. Additionally,
it integrates a Reinforcement Learning (RL) agent to minimize control overhead
by learning optimal timings for generating new clusters. Addressing key
research questions, LEACH-RLC seeks to balance control overhead reduction
without compromising overall network performance. Through extensive
simulations, this paper investigates the frequency and opportune moments for
generating new clustering solutions. Results demonstrate the superior
performance of LEACH-RLC over conventional LEACH and LEACH-C, showcasing
enhanced network lifetime, reduced average energy consumption, and minimized
control overhead. The proposed protocol contributes to advancing the efficiency
and adaptability of WSNs, addressing critical challenges in IoT deployments.