Collaborative Research: DDDAS-TMRP: Dynamic Sensor Networks - Enabling the Measurement, Modeling, and Prediction of Biophysical Change in a Landscape

下一代无线传感器网络将是动态系统，有可能彻底改变对环境变化的理解，前提是它们能够实时吸收大量异构数据，快速评估（优化）新数据收集的相对价值和成本，并相应地安排后续测量。因此，它们是动态数据驱动的应用系统，在自适应框架中集成了传感和建模。人们对无线环境传感的广泛应用（如NEON和CLEANER）有着浓厚的兴趣，期待DDDAS技术能够根据其对部署、获取、传输和存储成本的贡献来估计未来数据的价值。这种平衡对于环境数据尤其重要，因为网络通常部署在没有基础设施（例如电力）的偏远地区，采样间隔从米和秒到景观和年不等，这取决于过程、系统的当前状态、状态的不确定性以及感知到的快速变化的潜力。网络控制必须是动态的，并且由能够学习环境和网络的模型驱动。本项目的重点是动态传感器网络的应用，包括了解生物多样性和碳储量如何受到全球变化的影响。具体而言，该项目旨在了解在气候、二氧化碳和干扰变化以及其他可能迅速波动的变量的背景下，森林树木的生长、生存和繁殖如何受到这些变化的影响。这一目标涉及树木生长和资源分配如何受到变量影响的模型，这些变量可以通过在时间和空间上跨越不同尺度的自适应采样来理解。该项目将为动态数据驱动的无线网络控制提供一个通用框架，该框架结合了网络内外的环境建模和传感器网络建模。在网络之外，环境建模需要充分吸收所有信息，并利用可用的计算资源。网络中的环境建模基于提供实时、近似答案的简化表示。网络内控制模型为新的测量提供快速调度，并将网络信息传递给服务器，用于诊断、监督控制和数据同化。基于对环境变量、参数和电池寿命的最全面了解，网络内模型会定期更新。具体目标有：(i)构建无线传感和网络基础设施，支持网络内和监督联合测量、建模和预测的新范式；（ii）开发将系统理解与有效无线环境传感成本相结合所需的建模策略；（iii）在理解生物多样性的维持和测量生态系统属性方面取得重大进展。（iv）改善计算机科学、工程学、统计学家和环境科学家之间的合作。

The next generation of wireless sensor networks will be dynamic systems with the potential torevolutionize understanding of environmental change, provided they can assimilate large amounts of heterogeneous data in real time, rapidly assess (optimize) the relative value and costs of new data collection, and schedule subsequent measurements accordingly. Thus, they are Dynamic Data Driven Application Systems that integrate sensing with modeling in an adaptive framework. Keen interest in broad application of wireless sensing of the environment, as in NEON and CLEANER, awaits DDDAS technology that can estimate the value of future data in terms of its contribution to understanding against the costs of deployment, acquisition, transmission, and storage. This balance is especially important for environmental data, because networks will typically be deployed in remote locations without access to infrastructure (e.g., power), and sampling intervals will range from meters and seconds to landscapes and years, depending on the process, the current state of the system, the uncertainty about that state, and the perceived potential for rapid change. Network control must be dynamic and driven by models capable oflearning about both the environment and the network. The focus of this project is the dynamic sensor network application involving understanding how biodiversity and carbon storage are influenced by global change. Specifically, this project is designed to learn how the growth, survival, and reproduction of forest trees are influenced by changes in climate, CO2 and disturbance, in the context of these and other variables that can fluctuate rapidly. This goal involves models of how tree growth and resource allocation are influenced by variables that can be understood through adaptive sampling across diverse scales in both time and space. The project will enable a general framework for dynamic data-driven wireless network control that combines environmental modeling and sensor network modeling both in and out of the network. Out of the network, environmental modeling entails full assimilation of all information, with exploitation of computing resources available there. Environmental modeling in the network is based on simplified representations that provide real-time, approximate answers. The in-network control model provides rapid scheduling for new measurements, and it communicates network information to the server, for diagnostics, supervisory control, and data assimilation. Periodically, the in-network model is updatedbased on this most complete understanding of the environmental variables, parameters, and battery life. Specific goals are (i) to construct a wireless sensing and networking infrastructure that supports a new paradigm of joint in-network and supervisory measurement, modeling, and prediction, (ii) to develop the modeling strategy needed to combine system understanding with costs for efficient wireless sensing of the environment, (iii) to make significant progress in understanding the maintenance of biodiversity and in measuring ecosystem properties, and (iv) to improve collaboration between computer sciences, engineering, statisticians and environmental scientists.