Collaborative research launched by IBM and Dublin City University (DCU) Water Institute seeks to advance the application of Internet of Things (IoT) technologies to water resources monitoring and management. The effort involves deployment of sensors developed at DCU with IBM’s machine learning and cognitive IoT technologies.
Deploying a vertical profiler on Lake George, NY. (Source: Rensselaer Polytechnic Institute)
IoT solutions are under development at IBM to support sensor networks designed to better manage a multitude of ecosystem challenges, such as water quality changes due to natural, artificial, or climate-related effects. Sensors can measure physical, chemical, and biological parameters to help better understand environmental changes. Applications may include improved management of pollution from agricultural or storm water runoff that can affect lakes, rivers, estuaries, and marine ecosystems.
Cognitive IoT technologies provide deep learning capabilities for sensor platforms, which ensure quality and reliable data capture under a range of environmental conditions. Advanced analytics embedded in IoT-based sensor platforms, or the sensors themselves, can help detect subtle trends or early detection of environmental changes that may be crucial to public health and safety or remediation efforts.
The collaboration will focus on newly developed DCU sensor technologies with potential for monitoring several key aspects of water quality at costs significantly lower than current commercial technologies. This new generation of sensors, when combined with IBM’s environmental IoT platform, may deliver significant benefits for water management on a global scale.
The technologies will be piloted in Ireland and the U.S. The first sensors are being deployed on Lake George, NY, in conjunction with the ongoing Jefferson Project at Lake George. The latter, a partnership between Rensselaer Polytechnic Institute, IBM Research, and The Fund for Lake George, combines IoT technology with powerful analytics to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement.
