标题： 基于博弈论和强化学习的节能无线传感器网络簇头选择 显示英文标题

标题： Game-Theoretic and Reinforcement Learning-Based Cluster Head Selection for Energy-Efficient Wireless Sensor Network

摘要： 无线传感器网络（WSNs）中的能量对于网络寿命和数据传输至关重要。 然而，这些传感器节点的耐用性和可靠性的主要障碍是它们的短电池寿命。 目前，节能算法如聚类和路由算法已提高了标准协议的能量效率。 本文提出了一种基于聚类的路由方法，以创建自适应、节能的机制。 我们的系统采用多步骤聚类策略来选择具有最佳能量分布的动态簇头 （CH）。 我们使用博弈论（GT）和 强化学习（RL）来优化资源利用。 将网络建模为一个多智能体RL问题，利用GT原则，可以在优化传感器寿命和能量平衡的同时实现自我聚类。 所提出的AI驱动的CH查找算法通过防止特定节点过早耗尽能量，同时确保网络中均匀的能量使用，从而提高网络效率。 我们的解决方案实现了受控的能耗，从而得到确定性的网络寿命。 这种可预测性通过减少节点更换的需求来降低维护成本。 此外，我们提出的方法通过指定电量最高的传感器作为中间节点并使用单跳路由，防止传感器节点从网络中断开。 这种方法提高了无线传感器网络（WSN）部署的能量效率和稳定性。 显示英文摘要

摘要： Energy in Wireless Sensor Networks (WSNs) is critical to network lifetime and data delivery. However, the primary impediment to the durability and dependability of these sensor nodes is their short battery life. Currently, power-saving algorithms such as clustering and routing algorithms have improved energy efficiency in standard protocols. This paper proposes a clustering-based routing approach for creating an adaptive, energy-efficient mechanism. Our system employs a multi-step clustering strategy to select dynamic cluster heads (CH) with optimal energy distribution. We use Game Theory (GT) and Reinforcement Learning (RL) to optimize resource utilization. Modeling the network as a multi-agent RL problem using GT principles allows for self-clustering while optimizing sensor lifetime and energy balance. The proposed AI-powered CH-Finding algorithm improves network efficiency by preventing premature energy depletion in specific nodes while also ensuring uniform energy usage across the network. Our solution enables controlled power consumption, resulting in a deterministic network lifetime. This predictability lowers maintenance costs by reducing the need for node replacement. Furthermore, our proposed method prevents sensor nodes from disconnecting from the network by designating the sensor with the highest charge as an intermediary and using single-hop routing. This approach improves the energy efficiency and stability of Wireless Sensor Network (WSN) deployments.