CAREER: From the Cloud to the Crowd: An Enabling Solution for the Internet of Federated Things

职业：从云到人群：联合物联网的支持解决方案

基本信息

批准号：
2144147
负责人：
Raed Al Kontar
金额：
$ 50万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-01 至 2027-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144147&HistoricalAwards=false
关键词：
CAREER Cloud Crowd Enabling Solution

项目摘要

This Faculty Early Career Development Program (CAREER) award will help the US to maintain a leading position in manufacturing by leveraging the Internet of Things, the multitude of computing and data collection devices such as machine tool controllers, cell phones and remote sensors connected by the Internet, to achieve improved analytics and decision-making capability, referred to as the Internet of Federated Things (IoFT). The rapidly increasing computational power and data storage capabilities of these interconnected devices allow the development of decentralized, privacy-preserving analytics tools that can enhance fairness, privacy, cost-effectiveness and reduced computational burden through parallelization, standing in contrast to the prevalent paradigm of collecting and processing data in centralized data centers in the cloud. The accompanying educational plan supports community-level involvement in emerging technologies, helping to prepare future engineers to interact with and develop IoFT technologies. This is achieved through an outreach program oriented towards the American public and middle-school students in the greater-Detroit communities underrepresented in STEM fields and on college campuses.This research will build a data-driven, decentralized analytics framework for the IoFT capable of providing customized decision-making and control at the individual device level. A predictive framework allows devices to retain their local data-driven models while leveraging results and data from similar, though non-identical, devices. A key feature of the predictive framework is its ability to account for data heterogeneity, as devices are often operated under different operating and environmental conditions. Bayesian theory and distributed inference are then adopted to enable decentralized uncertainty quantification for the predictive models at the individual device level. The predictions are then used in an optimization framework to realize target output at the individual device level by optimizing controllable system input. The techniques developed will be evaluated using an IoFT prototype for distributed 3D printing, and will provide public data for other researchers to further investigate IoFT within manufacturing.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.

这项教师早期职业发展计划（职业）奖将通过利用物联网，众多计算和数据收集设备（例如机床控制器，手机和遥控器）来帮助美国在制造领域保持领先地位。为了获得改进的分析和决策能力，互联网称为联合事物的互联网（IOFT）。这些相互联系的设备的迅速增强的计算能力和数据存储能力允许开发分散的，隐私保护分析分析工具，可以增强公平性，隐私，成本效益，并通过并行化减轻计算负担，与普遍的收集范式相反并在云中的集中数据中心处理数据。随之而来的教育计划支持社区级别参与新兴技术的参与，有助于准备未来的工程师与IFF互动和开发IOFT技术。这是通过针对较大民主社区的美国公立和中学学生的宣传计划来实现的在单个设备级别上定制决策和控制。一个预测框架允许设备保留其本地数据驱动的模型，同时利用类似但非相同设备的结果和数据。预测框架的一个关键特征是它可以考虑数据异质性的能力，因为设备通常在不同的操作和环境条件下运行。然后采用贝叶斯理论和分布式推理，以在单个设备级别对预测模型进行分散的不确定性量化。然后将预测用于优化框架中，以通过优化可控系统输入来实现单个设备级别的目标输出。开发的技术将使用IOFT原型进行评估，用于分布式3D打印，并将为其他研究人员提供公共数据，以进一步调查制造业中的IOFT。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点评估的。和更广泛的影响审查标准。