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.
