REU Site: Research Experiences for Undergraduates in Edge Computing

这个更新的本科生研究经验（REU）站点项目专注于德克萨斯州立大学的边缘计算。这个REU网站为10名本科生提供了机会，让他们获得为期10周的暑期研究经验，并开发创新的边缘计算技术。参与的学生将接触到与边缘计算相关的各个方面的原理、技术和挑战。该网站强调多元化学生群体的参与，特别是女性，少数民族，第一代和非传统学生。该项目包括研讨会、学生演讲和海报、实地考察当地领先的科技公司和研究实验室，以及其他专业发展机会。目标是增加计算机科学（CS）学生的保留率，向他们灌输创新精神，改善他们的职业前景，让他们参与计算的基础和应用研究，并激励他们进入研究生CS课程。在这个数据驱动和无处不在的计算的新时代，边缘计算正在兴起并蓬勃发展，通过在数据产生的地方或附近处理数据来补充云计算。边缘计算是高效和经济地实现不断增长的物联网（IoT）和网络物理系统（CPS）的关键，在这些系统中，大量数据由众多传感器和终端设备在网络边缘生成。该项目将从多个角度研究和推进边缘计算的最新技术，包括实时边缘计算、可穿戴边缘设备编程、高性能边缘计算、边缘网络资源分配和边缘时间序列数据标记。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This renewal Research Experiences for Undergraduates (REU) site project focused on edge computing at Texas State University. This REU site provides opportunities for a cohort of 10 undergraduate students to gain summer research experiences for 10 weeks and to develop innovative edge computing technologies. Participating students are exposed to the principles, techniques, and challenges in a variety of aspects associated with edge computing. The site emphasizes the participation of a diverse group of students, in particular, women, minorities, first generation, and non-traditional students. The project includes workshops, student presentations and posters, field trips to local leading tech companies as well as research labs, and other professional development opportunities. The goal is to increase the retention of Computer Science (CS) students, instill the spirit of innovation in them, improve their career perspectives, engage them to participate in both fundamental and applied research in computing, and motivate them to enter graduate CS programs.In this new era of data-driven and ubiquitous computing, edge computing is emerging and thriving to complement cloud computing by processing the data at or close to where it is generated. Edge computing is a key to efficiently and affordably realizing the growing internet of things (IoT) and cyber-physical systems (CPS) where tremendous data is generated at the edge of the networks by numerous sensors and end devices. This project will investigate and advance the state-of-the-art in edge computing from several perspectives, including real-time edge computing, wearable edge devices programming, high-performance edge computing, networks resource allocation at the edge, and edge time-series data labeling.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.