无需基础设施、深度强化学习的约100mW城市噪声监测
Infrastructure-free, Deep Learned Urban Noise Monitoring at $\sim$100mW
AI总结：
本研究将SONYC无线传感器网络升级为三层架构，新增低功耗MKII节点的LoRa多跳网络，该节点搭载高效CNN嵌入模型，支持无基础设施部署与频率捷变组网，可低成本实现城市噪声监测。
摘要：
过去五年间，纽约市之声（SONYC）无线传感器网络（WSN）已在曼哈顿和布鲁克林部署，它是一个更大的人在环信息物理控制系统的组成部分，用于监测、分析和缓解城市噪声污染。本文介绍了两层式SONYC WSN从声学数据采集架构演进为三层式原位噪声投诉监测WSN的过程，以及当前的评估情况。新增的层级由我们设计并制造的新型低功耗声学节点MKII（“Mach 2”）组成的长距离（LoRa）多跳网络构成。MKII节点在三个方面表现突出：第一，它们引入了基于实时卷积神经网络（CNN）的嵌入模型，在该应用领域提升了节点级的机器学习能力，该模型性能与其他方案相当，同时所需训练数据减少10倍，运行时资源减少约2个数量级。第二，它们可以方便地部署在离上层基站节点较远的位置，无需在操作相关地点（如施工区域）配备电力或网络基础设施支持，是一种成本相对较低的解决方案。第三，与传统LoRa网络不同，它们的网络具备频率捷变特性：通过结合被动和主动测量的高效新方法动态选择优质频率，能够以分布式、自稳定的方式抵御杂乱的902-928MHz ISM频段城市环境中多变的外部干扰和链路衰落。
英文摘要：
The Sounds of New York City (SONYC) wireless sensor network (WSN) has been fielded in Manhattan and Brooklyn over the past five years, as part of a larger human-in-the-loop cyber-physical control system for monitoring, analyzing, and mitigating urban noise pollution. We describe the evolution of the 2-tier SONYC WSN from an acoustic data collection fabric into a 3-tier in situ noise complaint monitoring WSN, and its current evaluation. The added tier consists of long-range (LoRa), multi-hop networks of a new low-power acoustic mote, MKII ("Mach 2"), that we have designed and fabricated. MKII motes are notable in three ways: First, they advance machine learning capability at mote-scale in this application domain by introducing a real-time Convolutional Neural Network (CNN) based embedding model that is competitive with alternatives while also requiring 10$\times$ lesser training data and $\sim$2 orders of magnitude fewer runtime resources. Second, they are conveniently deployed relatively far from higher-tier base station nodes without assuming power or network infrastructure support at operationally relevant sites (such as construction zones), yielding a relatively low-cost solution. And third, their networking is frequency agile, unlike conventional LoRa networks: it tolerates in a distributed, self-stabilizing way the variable external interference and link fading in the cluttered 902-928MHz ISM band urban environment by dynamically choosing good frequencies using an efficient new method that combines passive and active measurements.
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。
