SST: Wireless Sensor Networks for Studies of Ecosystem CO2 Budgets

这项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、水和二氧化碳流动的时空异质性。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空间和时间变异性创造了新的机会，并提供了潜在的能力来解开在将地方测量尺度扩大到区域和大陆尺度方面往往令人困惑的因素。扩展现有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集成的传输路由和时间同步、传感器的动态控制和网络的响应性重新配置。在发展这种能力方面必须克服几个重大挑战。该奖项有三个主要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间和空间变化；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，整合可加载模块，改进代码传播，并提供容错引导程序，以将传感器重新整合到网络中；以及3)现场部署，以评估现场问题(例如，能源消耗、极端环境中的健壮性、森林结构中的无线传播)。这项研究将构成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资助的现场赠款互动参与，并包括对研究生的培训。所开发的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，如国家生态观测站网络、网络、机构、国际合作伙伴和16岁以下教师。在该奖项完成后，微型有轨电车网络将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.