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The PermaSense Project Wireless Sensor Technology for Extreme Environments

The PermaSense Project Wireless Sensor Technology for Extreme Environments . Jan Beutel, Mustafa Yuecel, Roman Lim, Tonio Gsell, ETH Zurich. PermaSense – Alpine Permafrost Monitoring. Cooperation with Uni Basel and Uni Zurich. PermaSense – Aims and Vision.

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The PermaSense Project Wireless Sensor Technology for Extreme Environments

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  1. The PermaSense ProjectWireless Sensor Technology for Extreme Environments Jan Beutel, Mustafa Yuecel, Roman Lim, Tonio Gsell, ETH Zurich

  2. PermaSense – Alpine Permafrost Monitoring • Cooperation with Uni Basel and Uni Zurich

  3. PermaSense – Aims and Vision Geo-science and engineering collaboration aiming to: • provide long-term high-quality sensing in harsh environments • facilitate near-complete data recovery and near real-time delivery • obtain better quality data, more effectively • obtain measurements that have previously been impossible • provide relevant information for research or decision making, natural hazard early-warning systems

  4. Understanding Root Causes of Catastrophes Eiger east-face rockfall, July 2006, images courtesy of Arte Television

  5. PermaSense Deployment Sites 3500 m a.s.l. A scientific instrument for precision sensing and data recovery in environmental extremes

  6. PermaSense – Key Architectural Requirements • Support for ~25 nodes • Different sensors • Temperatures, conductivity, crack motion, ice stress, water pressure • 1-60 min sensor duty-cycle • Environmental extremes • −40 to +65° C, ΔT ≦5° C/min • Rockfall, snow, ice, rime, avalanches, lightning • Near real-time data delivery • Long-term reliability • ≧99% data yield • 3 years unattended lifetime • Relation to other WSN projects • Comparable to other environmental monitoring projects • GDI [Szewczyk], Glacsweb [Martinez], Volcanoes [Welsh], SensorScope [Vetterli], Redwoods [Culler] • Lower data rate • Harsher, higher yield & lifetime • Data quality/integrity

  7. What we have today: PermaSense Starting Points

  8. Low-power Wireless Sensors • Static, low-rate sensing (2 min) • Temperature profiles, crack meters, resistivity • 3 years operation • < 0.1 Mbyte/node/day

  9. Base Station for Data Collection • Embedded Linux • Redundant long-haul communication • Solar powered

  10. Long-haul Communication • 7.5 km WLAN link from Klein Matterhorn (ski resort) • Leased fiber/DSL from Zermatt Bergbahnen AG • Collaboration with APUNCH/CCES

  11. Data Backend Integration, Metadata and Tools • Based on GSN (EPFL research project) • Collaboration with SwissEx/EPFL • Dual GSN server setup with metadata integration • Many auxiliary tools for 24/7 operation and debugging

  12. Something a little different: Soil Moisture Sensing on the Thur

  13. Decagon 5TE Soil Moisture Probes

  14. BaseStation Installation on Existing RECORD Tower • Redundant connectivity (GPRS/WLAN) • Webcam • Local WLAN access (essid RECORD-THUR)

  15. PermaSense – Sensor Node Hardware • Shockfish TinyNode584 • MSP430, 16-bit, 8MHz, 10k SRAM, 48k Flash • LP radio: XE1205 @ 868 MHz • Waterproof housing and connectors • Protective shoe, easy install • Sensor interface board • Interfaces, power control • Temp/humidity monitor • 1 GB memory • 3-year life-time • Single battery, 13 Ah • ~300 A power budget

  16. Ruggedized for Extreme Environments

  17. Dozer Low-Power System Integration contention window • Dozer ultra low-power data gathering system • Beacon based, 1-hop synchronized TDMA • Optimized for ultra-low duty cycles • 0.167% duty-cycle, 0.032mA • System-level, round-robin scheduling • “Application processing window” between data transfers and beacons • Custom DAQ/storage routine data transfer beacon jitter time slot 1 slot 2 slot k [Burri – IPSN2007] Application processing window time slot 1 slot 2 slot k

  18. Data Management Tools and Dataflow

  19. Data Management – Online Semantic Data • Global Sensor Network (GSN) • Data streaming framework from EPFL • Organized in “virtual sensors”, i.e. data types/semantics • Hierarchies and concatenation of virtual sensors enable on-line processing • Translates data from machine representation to SI values • Adds metadata Web export Import from field GSN GSN Private Public Metadata ============== Position Sensor type …

  20. Multi-site, Multi-station Data Integration

  21. TinyOS Multiplexing Data Flow

  22. Example: Sensor Network and Backlog/CoreStation

  23. Example: Private GSN Data Intake

  24. Example: Public GSN Data Mapping and Conversion

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