CAREER: Taming Wireless Devices Cross-Layer Errors with Assistive Networked Edges

智能手机、计算机和传感器等无线设备在我们的日常生活和现代社会中无处不在，在工作效率、医疗保健、自动化控制等方面创造了前所未有的好处。由于不完善的电子设备和嘈杂的环境，无线设备固有的能力，这可能会导致多方面的数据错误，在计算，缓存和通信（C3）。这些误差被广泛认为是有害的，因此现有的误差控制主要针对绝对误差的消除。然而，数据错误可以是良性的甚至是有益的（例如，将误差引入梯度可以帮助机器学习模型摆脱局部最优），而现有的反应性和非功能性研究未能将数据错误转化为好的。因此，该项目的研究目标是积极主动地收获，渲染和控制跨C3的无线设备的数据错误，在能源效率，吞吐量，数据隐私等显着的性能增益此外，研究工作将通过新的实验室，讲座内容，推广演示和一个新的本科生/研究生共同学习教学法的发展与教育创新相结合。该项目的成功完成将增强全国无线工作人员的多样性，促进社区推广，并创造对未来无线应用具有变革性的无线技术的根本创新（例如，建议的研究活动包括跨应用层的硬件和软件设计的协同作用，嵌入式存储器和无线设备的前端无线电以及无线网络边缘的网络协议。预期的结果包括：（1）收集并将错误呈现给特定的数据位（即，应用程序感知）以实现系统性能增益。(2)将错误与上下文感知（例如，信道条件）以使用隐式控制信令进行优化适配。(3)开发一套边缘网络协议，以便于控制错误数据。与此同时，拟议的教育活动包括创建硬件和应用感知的无线项目、实验室、讲座内容和推广演示。这些材料将按照设计的本科生/研究生共同学习教学法和计划的本科生研究途径进行整体交付。还将设计和开展各种外联活动，以针对低收入和农村K-12学生和第一代大学生-PI所在州独特的代表性不足的群体。预期成果包括：（1）通过提供更清晰的学科重点，为K-12学生未来的大学学习做好准备。(2)增加我国无线技术劳动力的多样性和人口。该项目由电气、通信和网络系统部、工程局、激励竞争研究计划（EPSCoR）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审查进行评估，被认为值得支持的搜索.

Wireless devices such as smart phones, computers and sensors are ubiquitous in our daily life and modern society, creating unprecedented benefits in work efficiency, healthcare, automation control and many more. Due to the imperfect electronics and noisy environments, wireless devices are inherently faculty which can result in multifaceted data errors in computing, caching, and communications (C3). These errors have been widely deemed harmful, so the state-of-the-art on error control mainly target for absolute error removal. Yet, data errors can be benign or even beneficial (e.g., introducing errors to gradients may help the machine learning models to escape local optima), while existing research that are reactive and non-functional fail to turn data errors into good. Hence, the research objective of this project is to proactively harvest, render, and control data errors across C3 of wireless devices for significant performance gains in energy efficiency, throughput, data privacy, etc. Moreover, the research efforts will be coupled with educational innovations through the development of new laboratories, lecture contents, outreach demos and a novel undergraduate/graduate co-learning pedagogy. The successful completion of this project will enhance diversity in the wireless workforce in the nation, promote community outreach and create fundamental innovations of wireless technologies that are transformative to future wireless applications (e.g., AI and smart health).The proposed research activities include a synergy of hardware and software designs across layers of application, embedded memory and front-end radio in wireless devices as well as network protocols on wireless network edges. Expected outcomes include: (1) Harvest and render errors to specific data bits (i.e., application-aware) at runtime to realize system performance gains. (2) Integrate and control errors with contextual awareness (e.g., channel condition) for optimized adaptation using implicit control signaling. (3) Develop a suite of edge networking protocols to facilitate control of erroneous data. In parallel efforts, the proposed education activities include the creation of hardware- and application-aware wireless projects, laboratories, lecture contents and outreach demos. These materials will be delivered holistically by following a designed undergraduate/graduate co-learning pedagogy and a planned undergraduate research pathway. Various outreach activities will be also designed and performed to target low-income and rural K-12 students and first-generation college students – unique under-represented groups at PI’s home state. Expected outcomes include: (1) Prepare K-12 students for future collegiate study by offering a clearer disciplinary focus. (2) Increase the diversity and the population of our nation’s workforce in wireless technologies.This project is jointly funded by the Division of Electrical, Communications and Cyber Systems, Directorate of Engineering, and the Established Program to Stimulate Competitive Research (EPSCoR).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.