SWIFT: LARGE: Averting Wireless Spectrum Pollution in the Era of Low-Power IoT

该项目旨在解决物联网(IoT)时代的频谱污染问题。当大规模部署的低成本、低功耗无线物联网设备无法检测和尊重共享频谱上其他设备的存在时，频谱污染是一个不可避免的挑战。核心挑战是大多数物联网设备的低功耗和简单性，由于它们是窄带设备，无法感知和避免共享频谱上的现任者。该项目研究了一种系统，该系统允许地理上分散的低功率设备团队快速高效地扫描宽带，以避免干扰。这项提案提出了Swallow，这是一种低功率设备的系统设计，可以以最低的能量和成本感知频谱，使这些设备能够充当低成本和分布式频谱观测站。通过该项目开发的试验台将作为本科生和研究生项目的载体，以及匹兹堡市K-12学生的研讨会。该团队拥有在卡内基梅隆大学、匹兹堡市、美国地质调查局(USGS)和当地行业合作伙伴部署传感器的直接经验，并将利用这些连接来大规模部署Swallow。该项目将研究一种低功耗模拟前端和相关的数字处理，使物联网设备能够使用廉价组件来总结大带宽。它研究了一种端到端系统设计，该设计可以整理来自分布式独立低功耗物联网设备的测量结果，以获得共享频谱图。它进一步开发了精确的定位框架，以对单个物联网设备进行地理定位，以精细空间分辨率采样频谱占用情况。燕子低功率前端的宽带宽自然为基于飞行时间的定位提供了准确的解决方案，其中带宽是决定系统精度的关键指标。Swallow提出了一种适用于低功率广域无线电的亚米级精确测距解决方案，该解决方案能够检测到与固定射电望远镜等已知无源射频设备的接近程度，并能够以精细的空间分辨率采样频谱占用图。该项目将在卡内基梅隆大学校区的大型可编程低功耗广域网络试验台上实施和评估，该校区服务于匹兹堡市的大部分地区。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 address the problem of spectrum pollution in the Internet-of-Things (IoT) era. Spectrum pollution is an inevitable challenge that emerges when low-cost and low-power wireless IoT devices deployed at scale cannot detect and respect the presence of other devices on shared spectrum. The core challenge is the low-power and simplicity of most IoT devices, due to which they are narrowband and unable to sense and avoid incumbents on shared spectrum. The project investigates a system that allows teams of geo-distributed low-power devices to quickly and efficiently scan wide bandwidths to avert interference. This proposal presents Swallow, a system design for low-power devices to sense spectrum at minimal energy and cost, allowing these devices to behave as low-cost and distributed spectrum observatories. The testbed developed through the project will serve as a vehicle for undergraduate and graduate-level projects as well as workshops for K-12 students in the City of Pittsburgh. The team has direct experience working with sensor deployments both at Carnegie Mellon, the City of Pittsburgh, United States Geological Survey (USGS), and local industry partners and will leverage these connections to deploy Swallow at scale.The project will study a low-power analog frontend and associated digital processing that allows IoT devices to summarize large bandwidths using inexpensive components. It investigates an end-to-end system design that collates measurements from distributed individually low-power IoT devices to obtain a shared spectrum map. It further develops an accurate localization framework to geo-locate individual IoT devices to sample spectrum occupancy at fine-spatial resolution. The wide bandwidth of Swallow’s low-power frontend naturally leads to an accurate solution for time-of flight based localization, where bandwidth is a key metric that dictates system accuracy. Swallow proposes a sub-meter accurate ranging solution for low-power wide-area radios that enables detecting proximity to known passive RF devices such as fixed radio telescopes and enables spectrum occupancy maps sampled at fine-spatial resolution. The project will be implemented and evaluated on a large programmable Low-Power Wide-Area Networking testbed in the Carnegie Mellon University campus that serves large parts of the City of Pittsburgh.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.