RAPID: SaTC: COVID19: Science of using wirelessly powered sensors to quickly scale up verifiable decontamination of individual N95 respirator masks

在照顾新冠肺炎患者的同时，确保一线医护人员能够获得N95口罩的供应，这对我们国家的健康极其重要。全球N95口罩短缺导致紧急建造各种净化系统，以重复使用医护人员佩戴的一次性口罩。虽然最好的选择是新口罩，但在COVID19可能卷土重来期间，当新口罩供不应求时，去污可以作为紧急备用。除确保有害水平的生物细菌灭活外，任何去污都必须防止口罩的过滤性能受到损害。湿热是一种易于部署的去污方法。设置为特定温度的烤箱可能会有热点和冷点，从而导致可以通过传感器技术检测到的净化风险。然而，每个面罩的温度和湿度传感器布线都很麻烦、耗时，而且很难扩大规模。在受控临床环境以外的高威胁环境中，传感器还必须经受住对手的干扰。敌手可以利用国际无线电干扰来愚弄传感器，让他们在模拟层看到虚假的现实。例如，如果对手欺骗温度传感器，使其看到虚假的、寒冷的温度，冷冻室可能会导致储存的标本意外解冻。因此，本研究项目进一步研究如何保护大量的温度和湿度传感器在具有信号注入能力的对手可以访问的高威胁环境中免受恶意干扰。更广泛的影响包括对N95decon.org的贡献，这是一个由来自世界各地大学和医疗机构的科学家、工程师和临床医生组成的志愿者联盟，研究N95口罩的去污染。以多种语言进行的技术报告、出版物和教育培训网络研讨会有助于在全球范围内保护医护人员，从而产生更广泛的影响。此外，可信传感器技术的科学可以扩展到保护其他新冠肺炎环境，例如运送患者的车队或需要技术来验证社交距离期间去污过程的有效性的办公空间。该研究项目解决了迫切的研究需要，以(1)确保并告知医护人员在具有不均匀加热的已知风险的烤箱中进行面罩去污过程所需的条件，(2)迅速鼓励部署高度可扩展和值得信赖的传感器网络技术，用于通过使用无线供电的计算RFID标签来监控和验证个人口罩去污，(3)通过防止恶意干扰破坏完整性来提高对基于传感器的系统安全性的知识和理解，传感器数据的可用性和保密性。该方法的关键是设计和部署基于英特尔WISP和UMassMoo的无线供电计算RFID标签，该标签带有传感器来测量湿热去污过程。通过移除电线和电池，该方法能够更快速地部署监测，而不需要对净化系统进行重大修改。这项研究对小诊所、农村设施和缺乏专用N95口罩去污系统的发展中国家特别有利。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

It's extremely important for the health of our nation to ensure that front-line healthcare workers have access to a supply of N95 masks while caring for COVID-19 patients. A global shortage of N95 respirator masks has led to the emergency construction of various decontamination systems for reuse of disposable masks worn by healthcare workers. While the best choice is a new mask, decontamination serves as an emergency backup when new masks are in short supply during likely COVID19 resurgence. Any decontamination must protect against damage to the masks' filter performance in addition to ensuring inactivation of harmful levels of bioburdens. Moist heat is one of the readily deployable decontamination methods. An oven set to a particular temperature will likely have hot spots and cold spots, leading to a decontamination risk that can be detected with sensor technology. However, wiring temperature and humidity sensors for each mask is cumbersome, time consuming, and difficult to scale up. In high threat environments beyond a controlled clinical environment, sensors must also withstand adversarial interference. An adversary could use international radio interference to fool sensors into seeing a false reality at the analog layer. For instance, cryogenic chambers may cause unintended thawing of stored specimens if an adversary tricks a temperature sensor into seeing false, cold temperatures. Thus, this research project further investigates how to protect large numbers of temperature and humidity sensors from malicious interference in high-threat environments accessible by an adversary with signal injection capabilities. The broader impact includes contributions to N95decon.org, a volunteer consortium of scientists, engineers, and clinicians from universities and healthcare facilities across the world to study N95 mask decontamination. Technical reports, publications, and educational training webinars conducted in multiple languages contribute to broader impact by helping to protect healthcare workers globally. Moreover, the science of trustworthy sensor technology can extend to protecting other COVID-19 environments such as fleets of vehicles transporting patients or office spaces needing technology to verify the effectiveness of decontamination processes during social distancing.This research project tackles the urgent research needs to (1) assure and inform healthcare workers of the conditions necessary for mask decontamination processes in ovens with known risks of non-uniform heating, (2) rapidly encourage the deployment of highly scalable and trustworthy sensor network technology for monitoring and validation of individual mask decontamination by using wirelessly powered computational RFID tags, (3) advance the knowledge and understanding of sensor-based systems security by preventing malicious interference from violating the integrity, availability, and confidentiality of sensor data. Key to the approach is the design and deployment of a wirelessly powered computational RFID tag based on the Intel WISP and UMass Moo with sensors to measure the moist heat decontamination processes. By removing wires and batteries, the approach enables more rapid deployment of monitoring without requiring significant modification to decontamination systems. The research has particular benefit to small clinics, rural facilities, and developing countries that lack convenient access to dedicated N95 mask decontamination systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.