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DTSTAMP:20250626T233528Z
LOCATION:B302-B305
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UID:submissions.supercomputing.org_SC24_sess533_post229@linklings.com
SUMMARY:Edge-Enabled Real-Time Data Processing in Power-Efficient Weather 
 Stations Using IBIS
DESCRIPTION:William Fowler (Tufts University); Alicia Esquivel Morel (Univ
 ersity of Missouri, Columbia); and Kate Keahey (University of Chicago, Arg
 onne National Laboratory (ANL))\n\nThere is a growing need to acquire a la
 rger quantity of meteorological data to address climate change. In this pa
 per, we design an improved Automatic Weather Station (AWS) based on a prot
 otype from the National Center for Atmospheric Research (NCAR). We integra
 te 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 hoste
 d on the Chameleon testbed, is presented. We show how the expanded periphe
 rals allow for the implementation of novel weather forecasting techniques 
 and demonstrate the power efficiency of our solution by comparing the powe
 r consumption of our choice of microcontroller to the Raspberry Pi. Lastly
 , we examine how our implementation can address challenges in big-data wea
 ther forecasting.\n\nRegistration Category: Tech Program Reg Pass, Exhibit
 s Reg Pass\n\nSession Chairs: Ayesha Afzal (Friedrich-Alexander University
 , Erlangen-Nuremberg; Erlangen National High Performance Computing Center)
 ; Sally Ellingson (University of Kentucky); and Alan Sussman (University o
 f Maryland)\n\n
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