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

职业：边缘物联网系统的集成端到端机器学习管道：资源感知和 QoS 感知的视角

• 批准号: 2340075
• 负责人: Hana Khamfroush
• 金额: $ 62.47万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2029-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340075&HistoricalAwards=false
• 关键词: CAREER; Integrated; end; machine; learning

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.

在未来智慧城市的格局中，人工智能和边缘计算的集成催生了多种应用，为可持续城市生活创造了变革潜力，从智能医疗系统到智能交通控制系统，这些应用都与大量数据处理相关。该项目的目标是开发一个集成且可靠的管道，该管道将有效地自动准备、清理和分析相关的分布式数据集，同时最大限度地降低系统的总体成本并动态平衡数据准备和数据之间的关系。这将通过创新技术来完成，包括将开发联合数据预处理、联合学习、新的编码方案和压缩技术，并通过使用内部开发的测试平台进行广泛的模拟和实验来验证和完善所提出的方法。 PI 实验室的一个关键目标是通过启用数据驱动的智能技术（例如尚不可行的智能医疗监控和智能交通控制系统）来显着改善美国公民的生活质量。创造更高效、更可持续的城市此外，该项目将包括通过肯塔基州一切都是科学节等当地活动进行综合教育、外展和指导活动，并与肯塔基州-西弗吉尼亚州路易斯斯托克斯少数族裔参与联盟合作，一个关键目标是促进多样性和包容性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。