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，前提是它们可以实时吸收大量不同种类的数据，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感知和建模。对环境无线传感的广泛应用，如霓虹灯和更清洁的环境，有着浓厚的兴趣，等待着DDDAS技术，该技术可以根据未来数据对了解部署、获取、传输和存储成本的贡献来评估其价值。这种平衡对环境数据尤其重要，因为网络通常部署在不能接入基础设施(例如电力)的偏远位置，采样间隔从几米几秒到几年不等，具体取决于流程、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可能性。网络控制必须是动态的，并由能够了解环境和网络的模型驱动。该项目的重点是动态传感器网络应用，包括了解全球变化如何影响生物多样性和碳储存。具体地说，该项目旨在了解在这些和其他可能迅速波动的变量的背景下，气候、二氧化碳和干扰的变化如何影响林木的生长、存活和繁殖。这一目标涉及树木生长和资源分配如何受到变量的影响的模型，这些变量可以通过在时间和空间的不同尺度上进行自适应采样来理解。该项目将为动态数据驱动的无线网络控制启用一个通用框架，该框架将网络内外的环境建模和传感器网络建模结合在一起。在网络之外，环境建模需要充分吸收所有信息，并利用那里可用的计算资源。网络中的环境建模基于提供实时、近似答案的简化表示法。网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，以进行诊断、监控和数据同化。根据对环境变量、参数和电池寿命的最全面了解，定期更新网络内模型。具体目标是：(I)建立无线传感和联网基础设施，支持网络内和监测联合测量、建模和预测的新范式；(Ii)制定所需的建模战略，将系统理解与有效的环境无线传感成本相结合；(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；以及(Iv)加强计算机科学、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 of learning 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 updated based 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.