Wireless Sensor Networks for Online Monitoring

Transcription

1 Wireless Sensor Networks for Online Monitoring Jan Beutel, ETH Zurich

2 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

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

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5 Photo: Bruno Jelk

6 Rockfall release mechanisms and their connection to time and climate (change) are not understood Foto: Bruno Jelk

7 Our patient does not fit into a laboratory

8 So the laboratory has to go on the mountain

9 PermaSense Consortium of several projects, start in 2006 Multiple disciplines (geo-science, engineering) Fundamental as well as applied research More than 20 people, 9 PhD students Nano-tera.ch X-Sense is a PermaSense project

10 PermaSense Competence in Outdoor Sensing Wireless systems, low-latency data transmission Customized sensors Ruggedized equipment Data management Planning, installing, operating (years) large deployments

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

12 PermaSense System Architecture

13 Simple Low-Power Wireless Sensors Static, low-rate sensing (120 sec) Simple scalar values: temperature, resistivity 3 years operation (~200 μa avg. power) < 0.1 Mbyte/node/day 3+ years experience, ~ data points In relation to other WSN projects Comparable to many environmental monitoring apps GDI [Szewczyk], Glacsweb [Martinez], Volcanoes [Welsh], SensorScope [Vetterli], Redwoods [Culler] Lower data rate Harsher environment, longer lifetime Higher yield requirement Focus on data quality/integrity [Beutel, IPSN2009]

14 PermaSense Sensor Types Sensor rods (profiles of temperature and electric conductivity) Thermistor chains Crack meters Water pressure Ice stress Self potential Data: Simple sensors, constant rate sampling, scalar values

15 Established: Rock/ice Temperature Aim: Understand temperatures in heterogeneous rock and ice Measurements at several depths Two-minute interval, autonomous for several years Survive, buffer and flush periods without connectivity [Hasler 2011]

16 Established: Crack Dilatation Aim: To understand temperature/ice-conditioned rock kinematics Temperature-compensated, commercial instrument Auxiliary measurements (temperature, additional axes, ) Two-minute interval, autonomous for several years Protection against snow-load and rock fall

17 Results: Rock Kinematics Stagnation [Hasler, A., Gruber, S. & Beutel, J. (in revision) Kinematics of steep bedrock permafrost, Journal of Geophysical Research]

18 Field Site Support Base station On-site data aggregation Embedded Linux Solar power system Redundant connectivity Local data buffer Database synchronization Cameras PTZ webcam High resolution imaging (D-SLR) Weather station Remote monitoring and control

19 WLAN Long-haul Communication Data access from weather radar on Klein Matterhorn (P. Burlando, ETHZ) Leased fiber/dsl from Zermatt Bergbahnen AG Commercial components (Mikrotik) Weatherproofed

20 Data Management Online Semantic Data Global Sensor Network (GSN) Data streaming framework from EPFL (K. Aberer) Organized in virtual sensors, i.e. data types/semantics Hierarchies and concatenation of virtual sensors enable on-line processing Dual architecture translates data from machine representation to SI values, adds metadata GSN Import from field GSN Web export Private Public Metadata ============ Position Sensor type Validity period

21 Multi-Site, Multi-Station, Multi-Revision Data

22 Central Web-based Data Access

23 Visualization with Google Earth

24 In Real-Time: Combined Data Overlays

25 Central Web-based Data Access

26 Sensor Network Technology

27 Highly Resource Constrained Distributed State Wireless Networked Embedded Systems Unreliable Communication Interaction and Tight Embedding in Environment

28 PermaSense Sensor Node Hardware Shockfish TinyNode584 MSP430, 16-bit, 8MHz, 10k SRAM, 48k Flash LP Radio: 868 MHz [Beutel, IPSN2009] 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 Li-SOCl 2 battery, 13 Ah ~300 µa power budget measured avg. current consumption ~148 µa

29 TinyNode + Sensor Interface Board Extension: One Serial Bus (Power) control using GPIO Optimized for low-power duty cycling

30 External Storage Extension External SD card memory Data buffering End-to-end validation

31 Ultra Low-Power Multi-hop Networking 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 30sec beacons) [Burri, IPSN2007] contention window data transfer beacon jitter slot 1 slot 2 slot k time But in reality: Connectivity can not be guaranteed Situation dependent transient links (scans/re-connects use energy) Account for long-term loss of connectivity (snow!)

32 Sensors Contribute to Power Consumption

33 1-hop Power Trace Validation

34 Total Power Performance Analysis 1/120 sec 1/30 sec 148 ua average power

35 Timing Control = Energy Savings =

36 Power Optimization Squeeze with Implications Regulator uses 17uA quiescent current Bypass used to shutdown regulator -> ~1uA in standby No Bypass increases ADC accuracy: stddev >

37 Power Quality Increases Data Accuracy Before After

38 Reconstructing Global Time Stamps No network-wide time synchronization available Implications on data usage a 4 sec Elapsed time on arrival Sensor nodes measure/accumulate packet sojourn time Base station annotates packets with UTC timestamps Generation time is calculated as difference c 4 sec b 2 sec 1 sec 6 sec 7 sec 2011/04/14 10:03:31 7 sec = 2011/04/14 10:03:24

39 PermaSense Base Station Situation

40 CoreStation Embedded Linux & WLAN Gumstix Verdex TinyNode WLAN Router GSM EMP Protectors IP68 Enclosure and Connectors

41 Test & Validation Infrastructure Technische Technische Informatik Informatik und und Kommunikationsnetze Computer Computer Engineering and and Networks

42 Challenge: The Physical Environment Lightning, avalanches, rime, prolonged snow/ice cover, rockfall Strong daily variation of temperature 30 to +40 C ΔT 20 C/hour

43 Ruggedized for Alpine Extremes

44 Impact of Environmental Extremes Software testing in a climate chamber Clock drift compensation yields ± 5ppm Validation of correct function Tighter guard times increase energy efficiency

45 WSN Testbed DSN and FlockLab v2 Testbed Key Differentiators Distributed observers, local intelligence Mobility: Wireless, battery powered Testbed Functionality Remote reprogramming Extraction of log data Stimuli, e.g. fault injection Synchronization of traces and actions Target Sensor Network Centralized logging Detailed behavioral analysis [SenSys2004, IPSN2005, EWSN2007]

46 Physical Emulation Architecture Influence of power sources/quality Detailed physical characterization Emulation of environment and resources Temperature Cycle Testing (TCT) Controlled RF attenuation Sensor stimuli and references [EmNets2007]

47 Installation Effort: 1 man-day/sensor

48 Key PermaSense Challenges System Integration Correct Test and Validation Actual Data Interdisciplinary Team

49 ETH Zurich Lab Geodesy and Geodynamics Lab University of Zurich EPFL Department of Geography Distributed Information Systems Laboratory University of Basel Department Computer Science Interested in more?