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Environmental Monitoring Using Sensor Networks (Texas Environmental Observatory)

Environmental Monitoring Using Sensor Networks (Texas Environmental Observatory) . Jue Yang, Chengyang Zhang, Xinrong Li, Yan Huang, Shengli Fu, and Miguel Acevedo. NSF CI-TEAM Grant 0636421 NSF CRI Grant CNS0709285 EEC-0431818 Texas ARP 003594-0010-2006.

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Environmental Monitoring Using Sensor Networks (Texas Environmental Observatory)

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  1. Environmental Monitoring Using Sensor Networks(Texas Environmental Observatory) Jue Yang, Chengyang Zhang, Xinrong Li, Yan Huang, Shengli Fu, and Miguel Acevedo NSF CI-TEAM Grant 0636421 NSF CRI Grant CNS0709285 EEC-0431818 Texas ARP 003594-0010-2006 December 17th, 2008 AGU Fall Meeting IN-34A

  2. Background • Motivation • Improve our scientific understanding • Inform the public of their environmental conditions • Forecast and predict • Engaging local governments and teachers • Interdisciplinary research and education • Goal • Providing near real-time information on environmental conditions • Data readily available to the public and amenable to modeling, analysis and synthesis

  3. Collaborations • UNT • College of Engineering • EE • Wireless sensor networks • CSCE • Ecoinformatics • BEE • Ecosystems • College of Arts and Sciences • Environmental Science & Geography • UV, Ozone, Water quality, Ecoplex • Weather station , biocomplexity • Elm Fork Ed Center and LLELA • Lessons, education, outreach • Computer Support • Web server, data collection server • Computing and IT Center • Research support • Clusters • Infrastructure for Database • PARTNERS • City of Denton • Weather • Flood gates • Water quality • Texas Parks and Wildlife • Greenbelt Corridor • Other cities • National Park Service (Big Thicket)

  4. TEO Paradigm • Environment Public Monitoring Inform Modeling Forecast and predict

  5. Environmental Observatory • EESAT • Greenbelt Park • Lewisville Lake • Discovery Park

  6. Design Requirements Soil moisture monitoring with flexible spatial coverage and resolution Seamlessly integrated wired and wireless sensors Long-term autonomous remote near-real-time monitoring Publicly available web services for sensor data visualization and dissemination Remote system monitoring and maintenance

  7. System Archtecture

  8. BS Wireless Sensor Nodes System Components GBC Wireless Modem PPP Link TEO Online (Web Server) RFG Server GPRS Datalogger DB Feeder RS-232 Postegre SQL DB Server CDC Server

  9. BS Wireless Sensor Nodes System Components Nano Station Ethernet WiFi TEO Online (Web Server) RFG Server Datalogger DB Feeder RS-232 Ethernet Postegre SQL DB Server CDC Server

  10. Wireless Sensor Network • The WSN hardware platform employed in our current design is the MICAz mote from Crossbow • 8MHz micro-controller • 4KB RAM, 512KB Flash • MDA300 Data Acquisition Board • 12-bit AD • EC-5 Soil Moisture Sensors • IEEE 802.15.4 compliant CC2420 Transceiver 2.4 GHz,, 250 kbps • Adjustable transmission power from -25dBm to 0dBm • LOS outdoor transmission range 100 meters

  11. Wireless Sensor Network MICAz motes are powered by solar cells and rechargeable batteries. To survive extreme weather conditions, motes are packaged in weatherproof boxes and the boxes are installed 4 feet above the ground on top of metal poles to avoid flooding water and prevent fallen leaves from covering solar cells.

  12. WSN Topology

  13. TEO Online(http://www.teo.unt.edu)

  14. TEO Online(http://www.teo.unt.edu)

  15. Sensor Data Visualization and Dissemination

  16. Conclusion and Future Work A remote near-real-time environmental monitoring system that is successfully developed and deployed to support long-term environmental studies. Seamless Integration of the emerging WSN-based system and the legacy wired sensor system. Scaling-up the soil moisture monitoring WSN at the GBC site to around 100 motes to have a much larger geographic coverage than the current eight-mote deployment. More observatory sites will be upgraded with the proposed architecture.

  17. Acknowledgements This research is supported in part by NSF under Grants OCI-0636421, CNS-0709285, and EEC-0431818, and by Texas Advanced Research Program under Grant 003594-0010-2006. The authors would like to thank David Hunter (City of Denton), Sonny Solis (Texas Parks and Wildlife), Bob Carle (National Weather Service), and members of the TEO Research Group at UNT, especially Rudi Thompson (Biology), Duane Gustavus (CITC), Adam Skelton (EE MS student), SanjayaGurung (EE MS student), and Liping Chen (Applied Geography MS student) for their contributions to the development of the TEO system.

  18. Questions? December 17th, 2008 AGU Fall Meeting IN-34A

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