CAREER: Integrated and end-to-end machine learning pipeline for edge-enabled IoT systems: a resource-aware and QoS-aware perspective

University Of Kentucky Research Foundation, Lexington KY

Investigators

Abstract

In the landscape of future smart cities, the integration of artificial intelligence and edge computing has led to a multitude of applications that create transformative potential for sustainable urban living. From smart healthcare systems to intelligent traffic control systems, these applications are linked to processing of substantial datasets generated by geographically distributed devices. The objective of this project is to develop an integrated and reliable pipeline that will effectively and automatically prepare, clean, and analyze the associated distributed datasets while minimizing overall costs of the system and dynamically balancing between data preparation and data processing tasks. This will be accomplished using innovative technologies, including federated data pre-processing, federated learning, new coding schemes, and compression techniques. A suite of optimization problems and associated algorithmic solutions will be developed. The proposed methodologies will be validated and refined through extensive simulation and experiments performed using a testbed developed within the PI’s lab. This project has the potential to significantly improve the quality of life for US citizens by enabling data-driven, smart technologies, such as smart healthcare monitoring and smart traffic control systems that are not yet feasible. A key goal is to contribute to creating more efficient and sustainable urban environments. Further, the project will include integrated education, outreach, and mentoring activities through local events like the Everything is Science Festival in Kentucky, and collaborating with the Kentucky-West Virginia Louis Stokes Alliance for Minority Participation. A key goal is to foster diversity and inclusion and empower the next generation of experts working in the emerging fields of machine learning, data science, and edge computing. 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.