SIParCS 2020 - Geeta Nain
Investigating Internet-of-Things (IoT) Platforms for 3D Printed Weather Stations
Weather forecasting and warning decisions can be improved with real time monitoring of weather. Expanding and extending existing ground observation mesonets, especially by addressing data sparse regions within these networks, can now be accomplished in a variety of low cost ways, including 3D-printed technologies, inexpensive environmental sensors, wireless communications networks and the Internet of Things (IoT). The 3D-PAWS (3D-Printed Automatic Weather Station) and other low-cost platforms, set the stage for developing flexible, inexpensive ground observation technologies based on commodity computing, communications and power hardware. Such platforms are now motivating newer, modular, easy-to-deploy, rapid-configuration IoT-based architectures. While the diffusion of such architectures might pave a way forward for mesonet expansion, managing, storing, visualizing and analyzing the corresponding increased data volumes becomes a critical concern. In this research, we examine various IOT management platforms to study, compare and identify key attributes such as ease of set up, portal configuration, platform integration, network connectivity, interoperability and scalability,data analytics, storage and visualization. Initial testing of candidate IoT platforms is conducted and a comparative landscape mapping is prepared to identify and select the most suitable platforms for data explorers. Real-time data is now emerging from a simple and cost effective, experimental mesonet expansion based on a new 3D-printed IoTwx station architecture deployment in Puerto Rico. Using this data a single candidate IoT platform, ThingsBoard, is used to explore the requirement of managing telemetry data produced by the IoTwx stations.
Mentors: Agbeli Ameko, Elliot Foust and Keith Maull