NeTS: Small: Collaborative Research: Towards Adaptive and Efficient Wireless Computing Networks

今天的移动设备不仅智能，而且随着Facebook Caffe2和b谷歌Tensor-Flow Lite等人工智能应用程序被推向移动设备，以及移动设备被集成到云雾移动架构中，它们也变得越来越智能。这需要无线网络的高效和自适应计算/通信协同设计，以优化应用程序级延迟（包括通信延迟和计算时间）并实现能源效率（考虑通信和计算消耗的能源）。该项目开发了低延迟、高能效和以计算为中心的无线网络的基础理论和新架构，以支持新兴的移动智能应用。pi开发的理论和算法不断融入两所大学的本科和研究生课程。该项目还为本科生和高中生提供了最先进的无线技术的实践经验。计算/通信协同设计对于无线网络来说是一个新事物，但它是数据中心网络中的一个核心主题。然而，所提出的解决方案虽然鼓舞人心，但由于无线网络的无线干扰、信道衰落和能量有限等独特特性，不能直接适用于无线计算网络。该项目侧重于可证明的最佳机制，动态和自适应调度计算任务和数据传输，以满足应用级性能要求，并由三个相互依存的重点组成：(i)最佳计算/通信协同设计。这一重点发展了无线计算网络的数学模型和理论限制；（二）强健的计算/通信协同设计。这个推力集中在健壮的计算和通信协同设计，在不完善的状态信息和不可避免的短期系统过载下实现期望的性能和（iii）学习辅助自适应计算/通信协同设计。这种推力通过利用历史数据和可预测的用户行为进一步提高了无线计算网络的性能。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。

Today's mobile devices are not merely smart, they are becoming intelligent as artificial intelligence applications such as Facebook Caffe2 and Google Tensor-Flow Lite are being pushed into mobile devices and as mobiles devices are being integrated into the cloud-fog-mobile architecture. This calls for efficient and adaptive computing/communication co-design of wireless networks to optimize application-level latency (including both communication latency and computing times) and to achieve energy efficiency (considering energy consumed by both communications and computing). This project develops fundamental theories and novel architectures of low-latency, energy-efficient, and computing-centric wireless networks to support emerging mobile intelligence applications. Theories and algorithms developed by the PIs are constantly integrated into the undergraduate and graduate courses taught at the two universities. This project also provides hand-on experiences to undergraduate and high school students with state-of-the-art wireless technologies. Computing/communication co-design, while new for wireless networks, is a central topic in data center networks. However, the proposed solutions, while inspiring, are not directly applicable to wireless computing networks because of the unique features of wireless networks such as wireless interference, channel fading and limited energy. This project focuses on provably optimal mechanisms that dynamically and adaptively schedule computing tasks and data transmissions to meet application-level performance requirements, and consists of three interdependent thrusts: (i) Optimal computing/communication co-design. This thrust develops mathematical models and theoretical limits of wireless computing networks, (ii) Robust computing/communication co-design. This thrust focuses on robust computing and communication co-design that achieves desired performance with imperfect state information and under unavoidable short-term system overload and (iii) Learning-aided adaptive computing/communication co-design. This thrust further improves the performance of wireless computing networks by leveraging both historical data and predictable user behaviors.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.