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Edge-Enabled Real-Time Data Processing in Power-Efficient Weather Stations Using IBIS
DescriptionThere is a growing need to acquire a larger quantity of meteorological data to address climate change. In this paper, we design an improved Automatic Weather Station (AWS) based on a prototype from the National Center for Atmospheric Research (NCAR). We integrate this weather station with IBIS, a platform for adaptable, multi-sensor data collection on edge devices. Our solution utilizes a Raspberry Pi 4 to aggregate sensor data from AWSs over LOng-RAnge (LoRa) radio frequency. A real-time data visualization platform, using Grafana, InfluxDB, and hosted on the Chameleon testbed, is presented. We show how the expanded peripherals allow for the implementation of novel weather forecasting techniques and demonstrate the power efficiency of our solution by comparing the power consumption of our choice of microcontroller to the Raspberry Pi. Lastly, we examine how our implementation can address challenges in big-data weather forecasting.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
TimeTuesday, 19 November 202412pm - 5pm EST
LocationB302-B305
Registration Categories
TP
XO/EX