SST: Wireless Sensor Networks for Studies of Ecosystem CO2 Budgets

这个为期三年的奖项将设计，开发和部署一个大型，多模式，多尺度的传感器网络，以解决山区生态系统中能量，水和CO2通量的时空异质性。 传感器网络和网络基础设施的最新进展为获得生态系统过程的空间和时间变异性的新知识创造了机会，并提供了潜在的能力，以解开在将当地测量扩展到区域和大陆尺度时经常混淆的因素。 扩展当前的网络和开发新的网络，能够解决这种空间和时间的变化，需要集成的传输路由和网络之间的时间同步，传感器的动态控制，并响应网络的重新配置。在发展这种能力方面必须克服若干重大挑战。 该奖项有三个主要的技术目标，以克服这些挑战：1）开发固定和移动的灵活定义的传感器集群与自组织行为，允许“智能”调整采样频率，以匹配时间和空间的变化; 2）开发一套用于远程重新编程的新算法和软件，这些算法和软件将解决能源效率问题，纳入可重构模块，改善代码传播，并提供容错引导程序以将传感器重新并入网络;以及3）现场部署以评估现场问题（例如，能量消耗、极端环境中的鲁棒性、森林结构中的无线传播）。 这项研究将形成一个新的传感器和传感器网络研究生课程的基础，将包括通过与资助REU网站赠款互动的本科生参与，并包括研究生的培训。 分析工具和框架的传播将有助于现有的研究项目，不断发展的环境观测系统，如国家生态观测网络，网络，机构，国际合作伙伴和K-16教师。 微型有轨电车网络将在该奖项完成后作为研究社区资源提供。

This three-year award will design, develop, and deploy a large, multi-modal, multi-scale sensor network to resolve the temporal and spatial heterogeneity in the flux of energy, water, and CO2 in a mountainous ecosystem. Recent advances sensors networks and cyberinfrastructure have created opportunities for new knowledge of the spatial and temporal variability of ecological system processes and provides the potential capability to unravel the often confounding factors in scaling local measurements to regional and continental scales. Expanding current networks and developing new networks capable of addressing this spatial and temporal variability requires integrated transmission routing and time synchronization among networks, dynamic control of sensors, and responsive reconfiguration of the networks. Several significant challenges must be overcome in developing such capability. This award has three major technical goals to overcome these challenges: 1) develop fixed and mobile flexibly defined sensor clusters with self-organizing behavior that permit "intelligent" adjustment of sampling frequency to match temporal and spatial variability; 2) develop a suite of new algorithms and software for remote reprogramming that will address energy efficiency, incorporate loadable modules, improve code propagation, and provide fault-tolerant bootstrapping procedures to reincorporate sensors in to the network; and 3) field deployment to evaluate in-situ issues (e.g. energy consumption, robustness in extreme environments, wireless propagation in forest structure). The research will form the basis of a new graduate course in sensor and sensor networks, will include participation by undergraduates through interaction with a funded REU site grant, and include training of graduate students. The dissemination of analytical tools and framework developed will be useful to existing research projects, evolving environmental observing systems such as National Ecological Observatory Network, networks, agencies, international partners, and K-16 teachers. The micro-scale tram network will be available as a research community resource after this award is complete.