Dimmer：基于强化学习的自适应网络泛洪
Dimmer: Self-Adaptive Network-Wide Flooding with Reinforcement Learning
摘要：
在过去的十年中，出现了同步传输（ST）作为低功率无线网络中的有效通信范例。许多ST协议在正常无线条件下提供高可靠性和能量效率，以满足各种各样的业务需求。最近，随着EWSN可靠性竞赛，社区将ST推向更苛刻和高干扰的环境，通过使用自定义规则，手工定制参数和额外的重传来改进经典ST协议。结果是复杂的协议，需要事先的专业知识和广泛的测试，通常针对特定的部署和设想的场景进行调整。在本文中，我们探讨了ST协议如何从自适应中受益;自适应ST协议选择自己的最佳参数，以（1）应对外部环境动态和（2）随着时间的推移适应其拓扑结构。我们引入调光器作为自适应ST协议。Dimmer基于LWB构建，并使用强化学习来调整其参数并匹配无线介质的当前属性。通过学习如何从未标记的数据集中表现出来，Dimmer可以适应不同的干扰类型和模式，并能够解决以前看不见的干扰。通过Dimmer，我们探索如何为受限设备高效地设计基于人工智能的系统，并概述基于人工智能的低功耗网络的优点和缺点。我们评估我们的协议上的两个部署的资源受限的节点实现95.8%的可靠性对强，未知的WiFi干扰。我们的结果优于非自适应ST协议（27%）和PID控制器等基线，并显示出接近手工制作和更复杂解决方案的性能，如Crystal（99%）。
英文摘要：
The last decade saw an emergence of Synchronous Transmissions (ST) as an effective communication paradigm in low-power wireless networks. Numerous ST protocols provide high reliability and energy efficiency in normal wireless conditions, for a large variety of traffic requirements. Recently, with the EWSN dependability competitions, the community pushed ST to harsher and highly-interfered environments, improving upon classical ST protocols through the use of custom rules, hand-tailored parameters, and additional retransmissions. The results are sophisticated protocols, that require prior expert knowledge and extensive testing, often tuned for a specific deployment and envisioned scenario. In this paper, we explore how ST protocols can benefit from self-adaptivity; a self-adaptive ST protocol selects itself its best parameters to (1) tackle external environment dynamics and (2) adapt to its topology over time. We introduce Dimmer as a self-adaptive ST protocol. Dimmer builds on LWB and uses Reinforcement Learning to tune its parameters and match the current properties of the wireless medium. By learning how to behave from an unlabeled dataset, Dimmer adapts to different interference types and patterns, and is able to tackle previously unseen interference. With Dimmer, we explore how to efficiently design AI-based systems for constrained devices, and outline the benefits and downfalls of AI-based low-power networking. We evaluate our protocol on two deployments of resource-constrained nodes achieving 95.8% reliability against strong, unknown WiFi interference. Our results outperform baselines such as non-adaptive ST protocols (27%) and PID controllers, and show a performance close to hand-crafted and more sophisticated solutions, such as Crystal (99%).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
