对物联网环境的基于强化学习的路由和资源管理:理论观点和挑战
Arslan Musaddiq1, Tobias Olsson1, Fredrik Ahlgren1
1Department of Computer Science and Media Technology, Linnaeus University, 39182 Kalmar, Sweden.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
强化学习 (RL) 为优化物联网 (IoT) 网络中的决策提供了一个有希望的解决方案. 这项研究探讨了RL.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络工程 网络工程
背景情况:
- 物联网 (IoT) 设备被广泛使用,但在网格拓中管理众多资源受限的传感器节点是复杂而昂贵的.
- 传统的网络层路由协议不适合物联网,因为高计算和开销要求.
- 现有的物联网特定的路由协议虽然更简单,但随着网络的扩展,会产生大量的开销.
研究的目的:
- 探索强化学习 (RL) 的潜力,以加强物联网 (IoT) 设备中与通信相关的决策.
- 提出一个理论框架,用于在物联网系统的网络层应用RL.
- 刺激对RL应用的进一步研究,以提高物联网网络性能和效率.
主要方法:
- 文献综述和理论分析的强化学习 (RL) 原则.
- 对低功耗和丢失网络 (LLN) 的现有路由协议的检查.
- 开发一个概念框架,将RL整合到物联网网络层操作中.
主要成果:
- 强化学习 (RL) 显示出在资源有限的物联网环境中优化沟通决策的巨大潜力.
- 提出了一个理论框架,以指导RL在物联网设备的网络层中的应用.
- 该研究确定了用于物联网网络的RL未来研究的关键挑战和开放问题.
结论:
- 强化学习 (RL) 提供了一种可行的和强大的方法来解决物联网 (IoT) 通信中的决策复杂性.
- 需要进一步的研究来开发和验证可扩展和高效的物联网网络的实际基于RL的解决方案.
- 拟议的框架为研究RL在克服物联网网络管理当前局限性的作用提供了基础.
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