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，气候和社会的不确定性阻碍了对饮用水供应、农业、风暴/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理解。为了减轻这种不确定性带来的社会压力，人们提出了由互联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。这种基础设施将通过实时传感、建模、决策支持和驱动来实现，这将允许它对周围的世界进行推理，并重新配置以满足不断变化的需求。在复杂动力系统的背景下，例如构成我们水资源的那些系统，适应是一个由随时间呈现的紧急系统行为驱动的反馈过程。因此，虽然适应性管理有很大的希望，但是如果它没有得到关于系统状态的准确的、包含的和当前的信息以及对未来变化的预测，它也容易失败。

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.