Designing Reliable Networked Embedded Systems Jan Beutel, ETH Zurich National Competence Center in...
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Transcript of Designing Reliable Networked Embedded Systems Jan Beutel, ETH Zurich National Competence Center in...
Designing Reliable Networked Embedded
Systems Jan Beutel, ETH Zurich
National Competence Center in Research – Mobile Information and Communication Systems
Trends in Information and Communication
New Applications andSystem Paradigms
Large-scaleDistributed Systems
CentralizedSystems
NetworkedSystems
Internet
The State of Wireless Sensor Network Design
• More an “art” than a coordinated effort yielding predictable results
• First generation research provided the proof-of-concept– Performance is poor– Causes are not fully understood– We are often lacking the
necessary (scientific) rigor
• Contributions in this talk– System architecture– Development tools and
design methodology– Application case study
[Phil
Levis
, Sta
nfo
rd]
Upcoming Keynote at EWSN 2009
Wireless Sensor Networks: Time for Real-Time? John A. Stankovic
THE BTNODE PLATFORMSystem Architecture for Sensor Networks
BTnodes – Research Impact & Technology Transfer
A system solution for fast-prototyping sensor
network applications BTnut System Software Webpage & mailing list Installer CDROM Developer kit & tutorial
2004
2001
2000
BTnode rev1
BTnode rev2
BTnode rev3
Mote-class devices Dual-radio (Bluetooth
and ISM band low-power)
TinyOS compatible Commercialized with
industrial partner
[SENSYS2003/2004, EWSN2004]
100+ scientific publications based on or related to BTnodes
DISTRIBUTED TEST AND VALIDATION
Development Tools and Methodology
Methodology and Development Tools
Continuous
Integration
Testbeds
Physical
Emulation
Advanced Software Engineering• Best practices in enterprise-level SW
development• Regression (unit) testing
Extending the Logical View• Detailed physical
characterization• Control of the environment• Physical stimulation• Control of resources
Execution on Real Platforms• Distributed, native execution• Influence of the environment• Remote reprogramming• Stimuli and log file analysis
Testbed – The Deployment-Support Network
Target Sensor Network
DSN Testbed Key Differentiators
• Distributed observers• Mobility: Wireless, battery powered
DSN Testbed Functionality• Remote reprogramming• Extraction of log data• Stimuli, e.g. fault injection• Time synchronization
[SenSys2004, IPSN2005, EWSN2007]
• Centralized logging• Detailed behavioral
analysis
DSN Impact – Automated Test Case Generation
• Detailed control, analysis and replay of simulation and testbed
• Developed and in-use at Siemens Building Technologies, Zug, CH– Protocols for high reliability wireless applications (fire alarm)
[DCOSS2007,INSS2007/2008]
Regression Testing Using Continuous Integration
On code change applications are built from scratch and analyzed– Standard practice in enterprise level software development– Deeper understanding of long term development trends– Service to the TinyOS community, increasing software quality
+4500 TinyOS-2.x regression builds
over the last 2 years at ETHZ
[http://tik42x.ee.ethz.ch:8080]
WSN Design and Development Tools
Virtualization &
Emulation EmStar arrays [Ganesan2004,Cerpa03/04]
BEE [Chang2003,Kuusilinna2003]
Sca
le
Reality Figure abridged from D. Estrin/J. Elson
Simulation TOSSIM [Levis2003]
PowerTOSSIM [Shnayder2004]
Avrora [Titzer2005]
Test Grids moteLab [Werner-
Allen2005]
Twist [Handziski2006]
Kansei [Dutta2005]
Can we Emulate Reality in the
Lab?
DSN Wireless
Testbed
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
Integration and automation
with DSN Testbed
[EmNets2007]
Visualizing Long Term Development Trends – Power
• Assertions based on reference traces/specification• Integrated with each build (regression testing)
Detailed Tracing – Validation using Formal Bounds
[WEWSN2008,SUTC2008]
Test and Validation – Research Outlook
• Past accomplishments– Developed a baseline infrastructure– Involved in numerous interesting case studies– Gained valuable experience and lots of data
• Large quantity of data requires automation and tools
• Fundamental differences in networked embedded systems require novel approaches– Unreliable wireless medium– Distribution nature– Tight embedding in the environment
• Recent focus on formalization of our methods– E.g. by using Uppaal for trace analysis
THE PERMASENSE PROJECT
A Compelling Application Driving Technology Research
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
PermaSense Deployment Sites 3500 m a.s.l.
A scientific instrument for precision sensing and data recovery in environmental extremes
PermaSense – Matterhorn Site Details
• Site of recent rockfall due to extreme warming (07/2003)
• ~25 nodes
• Different sensors– Temperatures, electrodes, crack
motion, ice stress, water pressure
• Environmental extremes– −40 to +65° C, ΔT ≦5° C/min– Rockfall, snow and ice,
avalanches
• Long-term reliability– 1-60 min. DAQ duty-cycle– ≧99% data yield– 3 years unattended lifetime
PermaDAQ: Precision Sensing and Data Recovery
• Sensor node architecture– Shockfish TinyNode584– Customized sensor interface board– Modular sensor concept– 1 GB storage (redundancy and
validation)– Single battery power supply
(~300 uA power budget)
• TinyOS based on Dozer system[submitted to IPSN2009]
Dozer Low-Power System Integration
• Dozer ultra low-power data gathering system– Beacon based, 1-hop synchronized TDMA– Optimized for ultra-low duty cycles
• System-level, round-robin scheduling– “Application processing window” between data transfers and beacons– Custom DAQ/storage routine
time
jitter
slot 1 slot 2 slot k
data transfer
contention window beaco
n
courtesy of R. Wattenhofer [IPSN2007]
Physical Reality Impacts Sampling Performance
• Storage duration
• Temperature
• ADC duration
Watchdog resets
Sensor Station Mounted on the Mountain
• Powerful embedded Linux
• 4 GB storage, all data duplicated
• Solar power (2x 90W, 100 Ah, ~3 weeks)
• GPRS connectivity, 2nd backup modem
PermaSense – Base Station Installation
Site Visit & Maintenance in November 2008
Base Station and Solar Panels On Matterhorn
Real Challenges of Sensor Networks Revisited
System Integration Correct Test and Validation
Actual Data Interdisciplinary Team
PermaSense Achievements – Current Status
• Dozer integration successful– Best-in-class low power– DAQ vs. COM power consumption– Extreme installation effort (time)– Relative relaxation of multihop requirement
• Continuous data since mid July
• Media attention
• First joint geo-science publications
• Started data-integration with the Swiss-Experiment
[NICOP2008]
148 uA average power
Acknowledgements
• BTnode core team– Matthias Dyer, Oliver Kasten, Kay Roemer, Matthias Ringwald
• PhD students– Matthias Woehrle, Andreas Meier, Matthias Keller
• PermaSense/Swiss-Experiment collaboration– ETHZ, EPFL, Uni Basel, Uni Zurich, University Paderborn, SLF, Art of
Technology
• Funding– SNSF (NCCR MICS), FOEN, CCES/Microsoft Research (Swiss-Experiment)
• Further information and publications– http://www.tik.ee.ethz.ch/~beutel– http://www.permasense.ch