CNS Core: Medium: Collaborative: Reality-Aware Networks

该项目旨在通过融合了网络和传感的现实感知无线架构来提高无线传感和联网技术的稳健性。强大的感知和高带宽网络有利于各种高影响领域的创新，包括混合现实、机器人和自动化车辆。例如，使用这种技术来增强驾驶员辅助系统或自动车辆可能会拯救无数人的生命。除了通过学术出版物传播成果外，该项目还将与无线和汽车行业合作，促进技术转让。该项目还包括一套综合教育和扩大参与活动，以通过实习计划、教育和推广活动吸引和留住来自代表不足群体的学生。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程序越来越多地涉及对环境的丰富感知，具有前所未有的网络要求。严格分离网络堆栈和感知组件的现有方法在提供强大的感知和高带宽网络方面面临挑战。为了解决这一问题，该项目开发和研究了一种可感知现实的无线体系结构，该体系结构融合了网络和传感组件，而不是孤立它们。该方法利用传感器信息和场景几何形状来提供改进的和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，例如对象识别和点对应。该团队首先通过设计低能耗标签和视觉信号策略来探索融合感知和通信的网络架构的设计空间。然后，该团队开发同时定位和映射算法，将传统策略与网络信息相结合，以增强健壮性。设计了几何匹配技术，增强了图像中目标与网络信息的关联性。在网络和链路层，该系统将利用从摄像头视图和其他传感器获得的有关物理障碍物和周围几何图形的知识，以提供更可预测和无缝的高带宽覆盖。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有关环境的信息。该架构在室内和室外实验中进行实施和评估，最终在高级无线研究平台(PAWR)COSMOS测试中进行验证。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.