CNS Core: Medium: Collaborative: Reality-Aware Networks

该项目旨在通过融合网络和传感的现实感知无线架构来提高无线传感和网络技术的鲁棒性。强大的感知能力和高带宽网络将有利于混合现实、机器人和自动驾驶汽车等多种高影响力领域的创新。例如，使用这些技术来增强驾驶员辅助系统或自动驾驶汽车有可能挽救无数生命。除了通过学术出版物传播结果外，该项目还将与无线和汽车行业合作，促进技术转让。该项目还包括一系列综合教育和扩大参与活动，通过在各参与机构开展实习项目、教育和外展活动，吸引和留住代表性不足群体的学生。随着无线传感和网络技术在当今世界取得重大进展，自动驾驶或增强现实等应用越来越多地涉及对环境的丰富感知和前所未有的网络需求。现有的严格分离网络堆栈和感知组件的方法在提供鲁棒感知和高带宽网络方面面临挑战。为了解决这个问题，该项目开发和研究了一种现实感知无线架构，该架构将网络和传感组件混合在一起，而不是将它们隔离开来。这种方法利用传感器信息和场景几何来提供改进的、更可预测的无线网络性能。它还使用通过网络接收的信息来辅助感知功能，如物体识别和点对应。该团队首先通过设计低能耗标签和视觉信号策略，探索了融合感知和通信的网络架构的设计空间。然后，该团队开发了同时定位和映射算法，将传统策略与网络信息相结合，以增强鲁棒性。设计几何匹配技术，增强图像与网络信息的对象关联。在网络和链路层，该系统将利用从摄像机视图和其他传感器获得的物理障碍物和周围几何形状的知识，提供更可预测和无缝的高带宽覆盖。来自推力的结果被集成到一个现实感知网络架构中，该架构利用通过传感器收集的环境信息。该架构在室内和室外实验中进行了实施和评估，最终在先进无线研究平台（PAWR） COSMOS测试台上进行了验证。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 project seeks to improve the robustness of wireless sensing and networking technologies through a reality-aware wireless architecture that blends networking and sensing. Robust perception and high-bandwidth networking benefit innovations across a diverse spectrum of high-impact areas including mixed-reality, robotics, and automated vehicles. For example, the use of such techniques to enhance driver assistance systems or automated vehicles has the potential to save numerous lives. In addition to disseminating results through scholarly publication, the project will engage the wireless and automotive industry to facilitate the technology transfer. The project also includes a set of integrated education and broadening participation activities to engage and retain students from underrepresented groups through internship programs, educational and outreach activities at each participating institution.As wireless sensing and networking technologies make significant strides in today's world, applications such as automated driving or augmented reality are increasingly involving rich sensing of the environment with unprecedented network requirements. Existing approaches that strictly separate the network stack and the perception component face challenges in providing robust perception and high-bandwidth networking. To address this, this project develops and studies a reality-aware wireless architecture that blends networking and sensing components, rather than isolating them. This approach exploits sensor information and scene geometry to provide improved and more predictable wireless network performance. It also uses information received over the network to aid perception functions such as object recognition and point correspondence. The team first explores the design space of network architectures for blending perception and communications by designing low-energy tags and visual signaling strategies. The team then develops simultaneous localization and mapping algorithms that blend conventional strategies with network information to enhance robustness. It also designs geometric matching techniques to enhance object association in images with network information. At the network and link layers, the system will exploit knowledge about physical obstacles and the surrounding geometry obtained from camera views and other sensors to provide more predictable and seamless high-bandwidth coverage. The outcomes from the thrusts are integrated into a reality-aware network architecture that exploits information about the environment gathered via sensors. The architecture is implemented and evaluated in indoor and outdoor experiments, culminating in a validation on the Platform for Advanced Wireless Research (PAWR) COSMOS testbed.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.