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

在未来智慧城市的前景中，人工智能和边缘计算的融合催生了众多应用，为可持续城市生活创造了变革潜力。从智能医疗保健系统到智能交通控制系统，这些应用程序都与地理上分布的设备生成的大量数据集的处理有关。该项目的目标是开发一个集成和可靠的管道，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，并在数据准备和数据处理任务之间动态平衡。这将通过创新技术来实现，包括联合数据预处理、联合学习、新的编码方案和压缩技术。将开发一套优化问题和相关的算法解决方案。所提出的方法将通过广泛的模拟和实验进行验证和完善，使用PI的实验室内开发的测试平台。 该项目有可能通过启用数据驱动的智能技术，如智能医疗监控和智能交通控制系统，显着提高美国公民的生活质量。一个关键目标是帮助创造更有效和可持续的城市环境。此外，该项目将包括综合教育，推广和指导活动，通过当地的活动，如一切都是科学节在肯塔基州，并与肯塔基州西弗吉尼亚州路易斯斯托克斯少数民族参与联盟合作。该奖项的主要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等新兴领域工作的下一代专家权力。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响力审查标准进行评估，被认为值得支持。

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.