The Application of an Internet of Things Cyber-Infrastructure for the Study of Ecology of Lake George in the Jefferson Project

We review the design and implementation and, describe the application of an Internet of Things (IoT) cyber-infrastructure for environmental sensing in support of the Jefferson Project at Lake George. The Jefferson Project is collaboration between Rensselaer Polytechnic Institute, IBM, and the FUND for Lake George focused on developing a detailed understanding of the overall ecology of Lake George, including the interactions of the physical, chemical, and biological environment in and around the lake. Aspects of research are driven principally by the management of numerous complex factors affecting the lake including: road salt, storm water runoff, and invasive species. The lake is located within the Adirondack State Park region of upstate New York, approximately 220 miles north of New York City. Lake George is a glacial, oligotrophic water body and is unique among fresh water lakes because of its ecology, geographic orientation, historical importance, and tourism-driven economic impact. The partners are developing an integrated observing and prediction system to enable a comprehensive understanding of the current ecological state of the lake while simultaneously supporting an ongoing research and monitoring program. This integrated IoT based environmental monitoring and prediction system is composed of three major components. The first of which is a cohesive, and adaptive, cyber infrastructure responsible for the control, coordination, communication, aggregation, and delivery of the various data streams from a real time multi-sensor observing network composed of in-situ sensors for atmospheric, stream and lake measurement. These data are used to inform and validate a coupled multi-model environmental prediction system. The cyber-infrastructure provides system level control of power management, data collection rates and autonomous adaptation of sensing strategies in real time. The application of this integrated system will enable predictive and real-time analytics for research, operations, and management as part of a monitoring and assessment program that can be applied to inland and coastal water systems.