Leveraging Ubiquity: A Big Data Approach to Environmental Observation

人们越来越认识到，现代社会的做法和生活方式不可能无限期地持续下去。这一点在水资源方面尤为明显，气候和社会不确定性阻碍了对饮用水供应、农业、暴雨/卫生系统、生态系统以及干旱和洪水等极端事件之间复杂相互作用的全面了解。为了减轻这种不确定性造成的社会压力，提出了由相互连接的数据和控制系统组成的“智能”基础设施，以促进更具适应性的水资源管理方法。这一基础设施将通过实时感知、建模、决策支持和驱动来实现，这将使其能够对周围的世界进行推理并进行重新配置，以满足不断变化的需求。在复杂动态系统的背景下，如构成我们水资源的系统，适应是一个反馈过程，由随时间呈现的紧急系统行为驱动。因此，尽管适应性管理有很大的希望，但如果它不能不断地从关于系统状态的准确、包容和最新的信息以及未来变化的预测中获得信息，它也容易失败。 目前还没有观测技术可以在足够的时空尺度上提供环境系统的实时测量，以驱动实现智能基础设施所需的模型。这项研究计划通过推进无处不在的环境传感的新范例，解决了以足够的分辨率提供环境测量以支持水资源适应性管理的挑战。无处不在的传感不仅依赖于部署大型专用环境传感器网络来提高观测分辨率，而且还机会主义地利用“无处不在”的传感器，例如手机的集成湿度传感器。 具体地说，拟议的研究计划将围绕以下问题进行组织：能否将目前难以管理的大量不同分辨率、用途和精度的传感器的测量结果结合在一起，以创建具有目前仅靠传统环境遥感无法达到的精度和分辨率的环境时空测量结果？这个问题的关键是传感系统将专用环境传感器的高精度、低分辨率测量与重新调整用途的普遍传感器的低精度、高分辨率测量相结合的能力。然而，由于专用环境传感器和改装的普适传感器之间的精度差距很大，以及嵌入式环境传感器预计会出现很高的传感器故障率，传统的数据集成方法无法使用。 拟议的工作将通过提供关于有效设计和使用无处不在的传感器网络的见解，通过预测控制和智能基础设施来改进对复杂的自然和已建成系统的实时自适应管理，从而为社会带来好处，随着我们努力在面对环境变化的情况下维护经济和社会安全，这项任务将变得越来越重要。它还将通过创建一个程序，使用无处不在的传感器对耦合的自然、建造和社会系统进行实时传感，从而加强研究的基础设施。最后，它将促进发现和理解，同时通过将尖端研究整合到汤普森·里弗斯大学不同学生群体的跨学科自然科学和工程教育中，促进教学。

It is increasingly recognized that modern society's practices and lifestyles cannot be sustained indefinitely. This is particularly evident in the case of water resources, where climactic and social uncertainty are impeding a holistic understanding of the complex interaction of drinking water supplies, agriculture, storm/sanitary systems, ecosystems, and extreme events such as droughts and floods. To mitigate the societal stressors imposed by this uncertainty, "intelligent" infrastructure, composed of interconnected data and control systems, has been proposed to facilitate a more adaptive water resource management approach. This infrastructure will be enabled by real-time sensing, modeling, decision support, and actuation, which will permit it to reason about the world around it and to reconfigure to meet changing needs. In the context of complex dynamical systems, such as those that comprise our water resources, adaptation is a feedback process driven by emergent system behaviors that are presented over time. Hence, while adaptive management holds great promise, it is also prone to failure if it is not continually informed by accurate, inclusive, and current information on the state of the system and by projections of future change. No observational technology yet exists that can provide real-time measurements of environmental systems at sufficient spatiotemporal scales to drive the models necessary to enable intelligent infrastructure. This research program address the challenge of providing environmental measurements at sufficient resolution to support the adaptive management of water resources by advancing a new paradigm of ubiquitous environmental sensing. Ubiquitous sensing does not rely solely on the deployment of large networks of dedicated environmental sensors to improve observational resolution, but also opportunistically leverages “pervasive” sensors, such as a cell phone’s integrated humidity sensor. Specifically the proposed research program will be organized around the following question: Can measurements from a currently unmanageably large number of sensors of varying resolution, purpose, and accuracy be combined to create spatiotemporal measurements of the environment with an accuracy and resolution currently unattainable by traditional environmental sensing alone? At the crux of this question is the ability of a sensing system to integrate high-accuracy, low-resolution measurements from dedicated environmental sensors with low-accuracy, high-resolution measurements from repurposed pervasive sensors. However, because of the great accuracy disparity between dedicated environmental sensors and repurposed pervasive sensors and the high rate of sensor malfunction expected from embedded environmental sensors, traditional data integration methods cannot be used. The proposed work will provide benefits to society by providing insights on the effective design and use of ubiquitous sensor networks to improve real-time adaptive management of complex natural and built systems through predictive control and smart infrastructure, a task that will become increasingly important as we try to maintain economic and social security in the face of environmental change. It will also enhance infrastructure for research through the creation of a program for real-time sensing of coupled natural, built, and social systems using ubiquitous sensors. Finally, it will advance discovery and understanding, while promoting teaching and learning by integrating leading-edge research into interdisciplinary natural science and engineering education for Thompson Rivers University’s diverse student population.