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基于深度强化学习的能源消耗优化,用于无线传感器网络中的点对点 (P2P) 通信

Jinyu Yuan¹, Jingyi Peng², Qing Yan³

¹School of Knowledge Based Technology and Energy, Tech University of Korea, Siheung-si 15073, Gyeonggi-do, Republic of Korea.

Sensors (Basel, Switzerland)

|March 13, 2024

概括

本研究引入了深度强化学习方法,以优化无线传感器网络 (WSN) 的能源消耗,使用点对点 (P2P) 通信. 该方法通过管理P2P传感器和授权传感器之间的干扰来有效地减少能源消耗.

科学领域:

无线传感器网络 (WSN) 是一种无线传感器网络.
通信系统通信系统
人工智能的人工智能

背景情况:

由于传感器的快速发展,无线传感器网络 (WSN) 面临着重大的能源消耗挑战.
对等通信 (P2P) 对于克服WSN能源瓶至关重要.
频谱干扰和功率限制阻碍了WSN的性能.

研究的目的:

提出基于深度强化学习的能源消耗优化策略,用于WSN的P2P通信.
为了使P2P传感器 (PU) 能够智能地管理与授权传感器 (AU) 共享频谱.
在WSN环境中减少干扰和节省能源.

主要方法:

使用P2P传感器作为能够控制功率和选择资源以减轻干扰的代理.
采用双深Q网络 (DDQN) 算法为代理学习详细的干扰特征.
与传统方法和标准深度Q网络 (DQN) 相比,模拟了拟议的算法.

主要成果:

基于DDQN的方法在与DQN和传统算法相比显示出更高的性能.
拟议的方法有效地减少了WSN中P2P通信的能源消耗.
智能资源分配由代理人最小化了PU和AU之间的干扰.

结论:

深度强化学习,特别是DDQN,为WSN P2P通信中的能源优化提供了有效的解决方案.
拟议的战略成功地解决了WSN中的干扰和功率限制问题.
这种方法提高了无线传感器网络的效率和寿命.

关键词:

深度强化学习的学习.能源消耗能源消耗能源消耗同行对同行通信.电源控制器的功率控制器无线传感器网络是无线传感器网络.

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