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; 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.

在未来智慧城市的景观中，人工智能和边缘计算的整合导致了许多应用程序，从而为可持续的城市生活带来了变革性的潜力。从智能医疗保健系统到智能交通控制系统，这些应用程序与地理分布式设备生成的大量数据集有关。该项目的目的是开发一条集成且可靠的管道，该管道将有效，自动准备，清洁和分析关联的分布式数据集，同时最大程度地降低系统的总体成本，并在数据准备和数据处理任务之间动态平衡。这将使用创新技术（包括联合数据预处理，联合学习，新编码方案和压缩技术）来完成。将开发一套优化问题和相关算法解决方案。提出的方法将通过广泛的模拟进行验证和完善，并使用PI实验室内开发的测试台进行的实验。该项目有可能通过启用数据驱动的智能技术，例如尚不可行的智能医疗保健监控和智能交通管制系统，从而显着改善美国公民的生活质量。一个关键目标是为创造更高效和可持续的城市环境做出贡献。此外，该项目将包括肯塔基州的《一切都是科学节》等当地活动的综合教育，外展和心理活动，并与肯塔基州 - 西弗吉尼亚州路易斯·斯托克斯联盟合作，参加少数群体参与。一个关键目标是促进多样性和包容性，并赋予在机器学习，数据科学和边缘计算新兴领域工作的下一代专家。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和更广泛的影响审查标准通过评估来进行评估的。