INNOVATIVE WIRELESS TECHNOLOGIES, INC. — Department of Energy SBIR Phase II: 18a

Description

In order to protect environmental resources and foster sustainable growth, environmental monitoring and modeling are needed to track the subsurface fate and transport of myriad constituents and contaminants, particularly in landscapes experiencing multiple stressors (e.g., land use, weather patterns, and global change). Towards this end, DOE has developed reactive transport models to predict local and regional natural chemical fluxes in complex subsurface systems. These models require in situ measurements of subsurface properties and processes with near real-time recording for capturing hydrobiogeochemical parameters. There is a need for IEQS solutions with in situ sensors that provide micro to large scale temporal and spatial distribution of soil and water properties that automate the collection, archiving, analysis, and visualization of high fidelity sensor data. IWT, in collaboration with Virginia Tech and Pacific Northwest National Lab, has successfully completed Phase II of the Integrated Environmental Quality Sensing System (IEQS) project.The Phase II project developed and field tested a commercializable prototype IEQS system that IWT has named Envōk™.Development and field test additional system features that will expand product scope beyond identified markets and thus increase commercialization success: (1) Smart auto-sampling system using machine learning algorithms that would support fully autonomous and adaptive data collection, where real-time data analysis selects parameters and frequencies to sample; (2) Modernize the Gateway for increased reliability in remote environments, and make the device easier to maintain and more cost effective; (3) Network pre-planning utility to provide users with tools to identify node locations that will support a wireless network and sufficient communication given site conditions such as topography, vegetation cover, and other obstructions; (4) Emplacement radio to allow in-field, local configuration and provide complete site access to the user from any location within the mesh network; and (5) Server enhancements including data visualization, analysis, and modeling; and a developer API for external statistical and modeling software. The in situ sensor measurement data will allow researchers to decipher the coupled interactions between water, soil, biota, and chemicals that occur within the subsurface to reveal patterns, trends, and associations relating to man-made as well as natural interactions.This will advance understanding of complex subsurface systems and lead to new methods to support cleanup of legacy waste sites as well as understanding the effects of various subsurface activities including carbon sequestration, hydraulic fracturing, mining, water supply management, land degradation, and climate change.