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.

翻译：无线传感器网络（WSNs）在赋予物联网（IoT）设备感知与执行能力方面发挥着关键作用。工作于偏远且资源受限的环境下，这些物联网设备面临着与能耗相关的挑战，而能耗对网络寿命至关重要。聚类协议已成为减轻物联网设备能量负担的有效解决方案。本文提出了一种基于强化学习控制器的低能耗自适应聚类层次协议（LEACH-RLC），这是一种新颖的聚类协议，它采用混合整数线性规划（MILP）进行簇头（CH）的选址策略及节点到簇的分配。此外，该协议集成了一个强化学习（RL）智能体，通过学习生成新簇的最佳时机，从而最小化控制开销。针对关键研究问题，LEACH-RLC旨在平衡控制开销的降低与整体网络性能的维持。通过大量仿真实验，本文研究了新聚类解决方案生成的频率与最佳时机。结果表明，LEACH-RLC相比传统的LEACH和LEACH-C协议展现出更优越的性能，包括更长的网络寿命、更低的平均能耗以及更小的控制开销。所提出的协议有助于提升无线传感器网络的效率与适应性，应对物联网部署中的关键挑战。