CCRI: Planning-C: Federated Cloud Platform for Networked Cyber Physical Systems Research

网络化信息物理系统（NCPS）通过有效地控制和管理关键服务在现代社会中发挥着至关重要的作用。这种N-CPS与传统的信息物理系统（CPS）相比具有显著的优势，包括易于使用和控制、更好的服务和系统管理、更好的资源利用率、使用商用现货（COTS）组件的能力、以及以增加的复杂性为代价的改进的可靠性和容错服务。采用N-CPS增加了网络安全、系统开发、系统管理和培训方面的挑战，需要联合云平台来支持协作的N-CPS研究。此联合云平台适用于网络化网络物理系统（FCP-NCPS）将使研究人员能够解决N-CPS的安全性，互操作性，管理和培训挑战，同时为数据分析和网络安全研究创建现实的数据集。1）在图森地区、巴尔的摩地区组织研究社区和行业研讨会，和底特律地区，讨论通过FCP-NCPS解决的需求和交流研究思路; 2）在图森、巴尔的摩、和底特律地区，讨论和了解他们的需要，传授培训CPS他们的学生。这些活动将有助于：1）创建一个社区云平台，用于无缝集成不同的CPS测试平台，这些测试平台在语法上，语义上和地理上都是分离的，用于现实实验，以解决N-CPS安全，互操作性，管理和培训挑战; 2）开发一个集成到联邦系统中的数据集收集框架，以解决网络安全和数据分析领域现实数据集的稀缺性; 3）提供一个社区基础设施，FCP-NCPS是一个可扩展的社区研究平台，也将在不同领域找到应用，包括扩大人工智能（AI）的可解释性和可信度，医疗设备实验和医疗培训实践，以及自主系统的实验和进化等等。此外，拟议的联邦云平台将帮助全球研究界（特别是发展中国家）进行现实的实验和评估他们的研究想法，以推进科学前沿。有关FCP-NCPS的信息，请访问https://fcpncps.arizona.edu。该网站将托管FCP-NCPS的所有内容，包括项目信息，API，文档和数据集，在项目期间和终止后三年内。该项目是亚利桑那大学、底特律-梅西大学、约翰霍普金斯大学和滨江研究所之间的合作项目。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Networked Cyber-Physical Systems (NCPS) play an essential role in modern society by efficiently controlling and managing critical services. Such N-CPS' have noticeable advantages over traditional Cyber-Physical Systems (CPS), including ease of use and control, better services and system management, better resource utilization, ability to use Commercial Off The Shelf (COTS) components, and improved reliability and fault-tolerant services at the cost of increased complexity. Adopting N-CPS adds challenges in cybersecurity, system development, systems management, and training, necessitating a federated cloud platform to support collaborative N-CPS research. This Federated Cloud Platform for Networked Cyber-Physical Systems (FCP-NCPS) will enable researchers to address N-CPS security, interoperability, management, and training challenges while enabling the creation of realistic datasets for research in data analytics and cybersecurity.This planning grant will enable: 1) to organize research community and industry workshops in the Tucson Area, Baltimore Area, and Detroit Area to discuss the needs and exchange research ideas addressable through the FCP-NCPS; 2) Organizing workshops with teaching and K-12 schools in Tucson, Baltimore, and Detroit areas to discuss and understand their needs to impart training in CPS to their students. These activities will allow: 1) Creation of a community cloud platform for seamless integration of diverse CPS testbeds that are syntactically, semantically, and geographically separated for realistic experimentation to address N-CPS security, interoperability, management, and training challenges; 2) to develop a dataset collection framework integrated into the federated system to address the scarcity of realistic datasets in the fields of cybersecurity, and data analytics; 3) to provide a community infrastructure for imparting realistic, practical (hands-on) training to students to address the nation's N-CPS workforce requirements.The FCP-NCPS being a scalable community research platform, will also find applications in different fields, including expanding interpretability and the trustworthiness of Artificial Intelligence (AI), experimentation of medical devices and medical training practices, and experimentation and evolution of autonomous systems, to name a few. Moreover, the proposed Federated Cloud Platform will aid the global research community (especially in the developing world) in performing realistic experimentation and evaluating their research ideas to advance the frontiers of science.Information on FCP-NCPS is available at https://fcpncps.arizona.edu. This site will host all the content for the FCP-NCPS, including the project information, APIs, documentation, and datasets, for the duration of the project and three years after its termination. This project is a collaboration between the University of Arizona, University of Detroit-Mercy, Johns Hopkins University, and Riverside Research.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.