Autonomous Wireless Sensor Networks: from development to long term implementation
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The document discusses the development and implementation of autonomous wireless sensor networks (WSNs) for environmental monitoring, highlighting various applications and the advantages of real-time data management and reduced human effort. It emphasizes the Santa Rosa Environmental Monitoring Super Site in Costa Rica as a key example, collecting extensive data on CO2/H2O fluxes and hyperspectral observations. The document also addresses challenges such as hardware durability, power management, and the evolving paradigms of data analysis in environmental monitoring.
Autonomous Wireless Sensor Networks: from development to long term implementation
- 1. + Autonomous Wireless Sensor Networks: from development to long term implementation Prof. Dr. Arturo Sanchez-Azofeifa1 (arturo.sanchez@ualberta.ca) Saulo Castro1, Mauricio Vega-Araya2 1. Alberta Centre for Earth Observation Sciences (CEOS) University of Alberta 2. Univesidad Nacional de Costa Rica
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- 4. + Applications of some WSN in the literature Application Location #,sensor,nodes Institution Year Duration Variables Links,between,weather,&, hydrology:,catchment? scale,monitoring Hawaii not,given U,Hawaii 2008 7,months water:,pH,,temp,, conductivity,,pO2,, turbidity,,water,level Soil,water,content Almkerk,, Netherlands 18 Twente,&, Wageningen 2009 6,months soil,moisture, (Decagon) Climate,,broadly Amazon UNAMA 2006 Petrel,nesting Maine 32 Intel,&,UC, Berkeley 2002 7,months light,,temp,,IR,,RH,, barometric,pressure Sediment Kansas 2 Kansas,State 2008 8,months opacity Center?pivot,irrigation Texas 17 USDA 2008 1,month IR,thermometer,for, canopy,temp,,air, temp,,RH,,solar, radiation,,windspeed,, rainfall Traveling,irrigation Montana 5 USDA 2008 4,months temp,,RH,,wind, speed,,wind,direction After MacGregor et al. 2013
- 5. + • Measurements at many spatial and temporal scales • Changing data needs (usually sparse) • Increased spatial coverage in heterogeneous environments. • Synchronized sampling across sensors. • Real-time data retrieval capabilities. • Landscape scale remote sensing validation of biophysical products. • Reduced human effort with increased information output. • Non-intrusive! Advantages ofWSN in monitoring
- 6. + Network Topologies and spatial design
- 7. + Sensor Network Cyberinfrastructure n Near real-time data management for Wireless Sensor Networks n Simplified data/trend visualization n Data mining: web data/metadata for cross- discipline social network research cooperation and analysis
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- 9. + Santa Rosa Environmental Monitoring Super Site, Costa Rica
- 10. Santa Rosa National Park, Environmental Monitoring Super Site, Guanacaste, Costa Rica: • 10 billion data points/year • CO2/H20 fluxes (vegetation and soil) • Hyperspectral canopy observations • Wireless Sensor Networks • On-line/Real time communication via satellite technology • Drone research • Micro-Satellite testing site (AlbertaSat) • Atmospheric Sounding calibration site • NASA Calibration/Validation site • Airborne and ground-based LiDAR
- 11. +Santa Rosa Environmental Monitoring Super Site: NEE and APAR from WSNs. 0 0.2 0.4 0.6 0.8 1 1.2 2013-161 2013-185 2013-209 2013-233 2013-257 2013-281 2013-305 2013-329 2013-353 2014-09 2014-33 2014-57 2014-81 2014-105 2014-129 2014-153 2014-177 2014-201 2014-225 2014-249 2014-273 2014-297 2014-321 2014-345 2015-01 2015-25 2015-49 2015-73 2015-97 2015-121 2015-145 2015-169 2015-193 2015-217 2015-241 2015-265 2015-289 2015-313 2015-337 2015-361 2016-017 2016-041 2016-065 2016-089 FPAR Terra MODIS v Environet FPAR Raw MODIS FPAR Raw Environet FPAR Score
- 12. + 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0 0.2 0.4 0.6 0.8 1 EnviroNetFPARWSNEstimate MODIS FPAR Estimate Santa Rosa Environmental Monitoring Super Site: FPAR MODIS Comparison 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.2 0.4 0.6 0.8 1 EnvironetFPARWSNEstimate MODIS FPAR Estimate MODIS TERRA MODIS AQUA
- 13. +Santa Rosa Environmental Monitoring Super Site: NEE and APAR from WSNs.
- 14. + Long term deployment of a WSN: Brazil, 10 –years (2006-2016). Temperature/RH Photosynthetic Active Radiation (PAR)
- 15. + Final remarks: Challenges with Environmental WSNs n Standardization n Durability of Hardware n Power Management n Data Management!!!
- 16. +Final Remarks: Changes on the environmental monitoring paradigm
- 17. + Current fact finding Analyze data in motion – before it is stored Low latency paradigm, push model Data driven – bring the data to the query Historical fact finding Find and analyze information stored on disk Batch paradigm, pull model Query-driven: submits queries to static data Traditional Computing Stream Computing Query Data Results Data Query Results Stream computing – Analyzing data in motion
- 18. + Thank You!