CPS: Frontier: SONYC: A Cyber-Physical System for Monitoring, Analysis and Mitigation of Urban Noise Pollution

CPS：前沿：SONYC：用于监测、分析和缓解城市噪声污染的网络物理系统

• 批准号: 1544753
• 负责人: Juan Bello
• 金额: $ 462.82万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544753&HistoricalAwards=false
• 关键词: CPS; Frontier; SONYC; Cyber; Physical

This Frontier award supports the SONYC project, a smart cities initiative focused on developing a cyber-physical system (CPS) for the monitoring, analysis and mitigation of urban noise pollution. Noise pollution is one of the topmost quality of life issues for urban residents in the U.S. with proven effects on health, education, the economy, and the environment. Yet, most cities lack the resources for continuously monitoring noise and understanding the contribution of individual sources, the tools to analyze patterns of noise pollution at city-scale, and the means to empower city agencies to take effective, data-driven action for noise mitigation. The SONYC project advances novel technological and socio-technical solutions that help address these needs.SONYC includes a distributed network of both sensors and people for large-scale noise monitoring. The sensors use low-cost, low-power technology, and cutting-edge machine listening techniques, to produce calibrated acoustic measurements and recognizing individual sound sources in real time. Citizen science methods are used to help urban residents connect to city agencies and each other, understand their noise footprint, and facilitate reporting and self-regulation. Crucially, SONYC utilizes big data solutions to analyze, retrieve and visualize information from sensors and citizens, creating a comprehensive acoustic model of the city that can be used to identify significant patterns of noise pollution. This data can in turn be used to drive the strategic application of noise code enforcement by city agencies, in a way that optimally reduces noise pollution. The entire system, integrating cyber, physical and social infrastructure, forms a closed loop of continuous sensing, analysis and actuation on the environment. SONYC is an interdisciplinary collaboration between researchers at New York University and Ohio State University. It provides multiple educational opportunities to students at all levels, including an outreach initiative for K-12 STEM education. The project uses New York City as its focal point, involving partnerships with the city's Department of Environmental Protection, Department of Health and Mental Hygiene, the business improvement district of Lower Manhattan, and ARUP, one of the world's leaders in environmental acoustics. SONYC is an innovative and high-impact application of cyber-physical systems to the realm of smart cities, and potentially a catalyst for new CPS research at the intersection of engineering, data science and the social sciences. It provides a blueprint for the mitigation of noise pollution that can be applied to cities in the US and abroad, potentially affecting the quality of life of millions of people.

该奖项支持SONYC项目，这是一项智慧城市计划，专注于开发用于监测、分析和缓解城市噪声污染的网络物理系统（CPS）。噪音污染是美国城市居民生活质量最重要的问题之一，对健康，教育，经济和环境都有影响。然而，大多数城市缺乏持续监测噪声和了解单个来源的贡献的资源，缺乏分析城市规模噪声污染模式的工具，以及使城市机构能够采取有效的数据驱动的噪声缓解行动的手段。SONYC项目推进了新的技术和社会技术解决方案，有助于满足这些需求。SONYC包括一个由传感器和人员组成的分布式网络，用于大规模噪声监测。这些传感器采用低成本、低功耗技术和尖端的机器监听技术，可产生校准的声学测量结果，并真实的实时识别单个声源。公民科学方法被用来帮助城市居民与城市机构和彼此联系，了解他们的噪音足迹，并促进报告和自我监管。至关重要的是，SONYC利用大数据解决方案来分析、检索和可视化来自传感器和市民的信息，创建一个全面的城市声学模型，可用于识别噪声污染的重要模式。这些数据可以反过来用于推动城市机构对噪声法规执法的战略应用，以最佳方式减少噪声污染。整个系统整合了网络、物理和社会基础设施，形成了一个对环境进行持续感知、分析和驱动的闭环。SONYC是纽约大学和俄亥俄州州立大学的研究人员之间的跨学科合作。它为各级学生提供多种教育机会，包括K-12 STEM教育的推广计划。 该项目以纽约市为中心，与该市环境保护部、健康和心理卫生部、下曼哈顿商业改善区以及世界环境声学领导者之一ARUP建立了伙伴关系。SONYC是智能城市领域网络物理系统的创新和高影响力应用，并可能成为工程，数据科学和社会科学交叉领域新CPS研究的催化剂。它为减轻噪音污染提供了蓝图，可应用于美国和国外的城市，可能影响数百万人的生活质量。

项目成果

Infrastructure-free, Deep Learned Urban Noise Monitoring at ~100mW

无需基础设施、深度学习的城市噪声监测，功率约为 100mW

• DOI: 10.1109/iccps54341.2022.00012
• 发表时间: 2022
• 期刊: 2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS
• 作者: Yun, Jihoon;Srivastava, Sangeeta;Roy, Dhrubojyoti;Stohs, Nathan;Mydlarz, Charlie;Salman, Mahin;Steers, Bea;Bello, Juan Pablo;Arora, Anish
• 通讯作者: Arora, Anish