SST: Wireless Sensor Networks for Studies of Ecosystem CO2 Budgets

这项为期三年的合同将设计、开发和部署一个大型、多模态、多尺度的传感器网络，以解决山区生态系统中能量、水和二氧化碳通量的时空异质性。传感器网络和网络基础设施的最新进展为了解生态系统过程的时空变异性创造了机会，并提供了潜在的能力，揭示了将局部测量扩展到区域和大陆尺度时经常出现的混淆因素。扩展当前网络和开发能够解决这种时空变化的新网络需要网络之间的集成传输路由和时间同步，传感器的动态控制以及网络的响应性重新配置。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来克服这些挑战：1)开发具有自组织行为的固定和移动灵活定义的传感器集群，允许“智能”调整采样频率以匹配时空变化；2)开发一套用于远程重编程的新算法和软件，以解决能源效率问题，纳入可加载模块，改善代码传播，并提供容错引导程序，以将传感器重新整合到网络中；3)实地部署以评估现场问题（例如能源消耗，极端环境中的鲁棒性，森林结构中的无线传播）。该研究将成为传感器和传感器网络新研究生课程的基础，将包括本科生通过与REU资助的现场赠款的互动参与，并包括研究生的培训。传播所开发的分析工具和框架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、网络、机构、国际伙伴和K-16教师。该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.