Real-time environmental sensor data: An application to water quality using web services

While real-time sensor feeds have the potential to transform both environmental science and decision-making, such data are rarely part of real-time workflows, analyses and modeling tool chains. Despite benefits ranging from detecting malfunctioning sensors to adaptive sampling, the limited number and complexity of existing real-time platforms across environmental domains pose a barrier to the adoption of real-time data. We present an architecture built upon 1) the increasing availability of new technologies to expose environmental sensors as web services, and 2) the merging of these services under recent innovations on the Internet of Things (IoT). By leveraging recent developments in the IoT arena, the environmental sciences stand to make significant gains in the use of real-time data. We describe a use case in the hydrologic sciences, where an adaptive sampling algorithm is successfully deployed to optimize the use of a constrained sensor network resource.