NeTS: Small: Networking and Sensing Using Visible Light Communications

移动计算正在加速超越智能手机时代。如今，人们戴着智能眼镜、智能手表和健身设备，携带智能手机、平板电脑和笔记本电脑。十年后，同样的人可能会佩戴或携带数十个无线设备，并以明显不同的方式与互联网和计算基础设施互动。后智能手机时代出现了两个根本性的挑战：在即将到来的频谱危机演变为全面危机之前应对它，以及在人们被日益增长的个人设备需求压垮之前推进人机交互（HCI）。该项目旨在通过提出首个基于可见光通信（VLC）的集成网络和传感环境来解决这两个挑战。本文的研究为VLC的研究开辟了两个新的方向：VLC网络化和VLC传感。该项目设计了用于智能空间的集成VLC网络和传感（iVLC）以及一套鲁棒的VLC网络和传感算法。VLC网络算法解决了以下挑战：1)由用户移动性和单个VLC链路的有限覆盖引起的频繁连接中断，2)漫射光的链路干扰，以及3)确定LED灯的最佳位置和密度。解决这些挑战需要相邻LED之间的有效切换方案，VLC网络的干扰处理方案，以及确定智能空间中LED灯部署的优化算法。VLC传感需要使用渲染在地板上的阴影来准确推断用户的手势。来自天花板上多个光源的光线组合模糊和扭曲了这些阴影。为了应对这一挑战，iVLC系统通过将标识符嵌入led的光信号中来分离阴影组件，从而允许地板上的光传感器测量单个光源的照度。最后，将设计、建造和部署一个新的概念验证iVLC测试平台，其中包括led和光传感器。光中比特项目的成果将有助于加速VLC作为一种新的移动网络和传感技术的部署。

Mobile computing is accelerating beyond the smartphone era. Today, people wear smart glasses, smart watches, and fitness devices and carry smartphones, tablets and laptops. In a decade, the very same people are likely to wear or carry tens of wireless devices and interact with the Internet and computing infrastructure in markedly different ways. Two fundamental challenges emerge in a post-smartphone era: dealing with the impending spectrum crunch before it becomes a full-fledged crisis, and, advancing human-computer interaction (HCI) before people buckle under the demands of dealing with an ever increasing number of personal devices. This project seeks to address these two challenges by proposing a first of its kind integrated networking and sensing environment based on visible light communication (VLC). The proposed research advances VLC research in two new directions: VLC networking and VLC sensing.The project designs integrated VLC networking and sensing for smart spaces (iVLC) and a set of robust VLC networking and sensing algorithms. The VLC networking algorithms address the following challenges: 1) frequent connection disruptions caused by user mobility and the limited coverage of individual VLC links, 2) link interference from diffusive lights, and 3) determining the optimal placement and density of LED lights. Addressing these challenges requires efficient handoff schemes between adjacent LEDs, interference handling schemes for VLC networks, and optimization algorithms to determine the LED light deployment in smart spaces. The VLC sensing requires accurate inference of user gestures using shadows rendered on the floor. The combination of light from multiple light sources on the ceiling obfuscates and distorts these shadows. To tackle this challenge, the iVLC system separates shadow components by embedding identifiers into light signals from LEDs allowing light sensors on the floor to measure the light illuminance of individual light sources. Finally, a novel proof-of-concept iVLC testbed will be designed, built, and deployed, which comprises LEDs and light sensors. Results from the Bits in the Light Project will help accelerate the deployment of VLC as a new mobile networking and sensing technology.