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

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

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.