European Wireless Sensor Networks 2009 http://www.lifeunderyourfeet.org/
Sundial: Using Light to Reconstruct Global Timestamps
Jayant Gupchup†, Răzvan Musăloiu-E.† , Alex Szalay±, Andreas Terzis†
Department of Computer Science, Johns Hopkins University†
Department of Physics and Astronomy, Johns Hopkins University±
European Wireless Sensor Networks 2009 http://www.lifeunderyourfeet.org/
Outline
Introduction Problem Description Solution Evaluation Discussion
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Introduction
Local Clock
DateTime /Universal Clock
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Postmortem Timestamp Reconstruction
Commonly used by environmental monitoring networks Time-Synchronization is expensive Increase network lifetime
Measurements are recorded in “Local timestamps”
Global Timestamps are assigned/mapped retro-actively collect pairs of <local ts, global ts>, i.e. “anchor points” Typically sampled by a gateway/basestation
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Problems
TraditionalPostmortem Reconstruction
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Basic MethodologyGTS = α . LTS + β
“α” (slope) representsClock-skew
“β” (intercept) represents
Node Deployment time
^ ^
<LTS, GTS>“Anchor Points”
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Reboots
Segment 1
Segment 2
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Reboots
Segment 1 Segment 2
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Failures
Basestation can fail Network is in “data-logging” mode
Nodes become disconnected from the network Mote is in data-logging mode
Basestation clock (global clock source) could have an offset/Drift Corrupt “anchor points” Bad estimates for α and β
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Propagation of α errors
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Example(s) in Data
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Solution
Robust Global Timestamp Reconstruction Algorithm “Sundial”
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Robust Global Timestamp Reconstruction (RGTR) Algorithm
Piece-Wise Timestamp Reconstruction
Identify Segments
Identify Anchor points associated with each segment
Obtain a fit (αi, βi) for each segmenti
Apply the fit for each segment to reconstruct global timestamps
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Robust estimates
“Anchor Points” belonging to a segment
Propertiesi.Line passing through the points has a slope ~ 1
ii.Intercept for equation of a line passing through the points must be same
Remove “outliers” for a robust Fit
Bad anchor points corrupt the fitIterative fit works as follows:
i. Obtain a fit using the “good” points
ii. Compute residuals of points from the fit
iii. Censor bad pointsiv. Repeat until “convergence”
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Motivation for “Sundial”
Global clock source might Contain an offset Drift Fail
Nodes might become disconnected from the network
“Sun” to the rescue !
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Annual Solar Patterns
<LOD, noon> = f (Latitude, Time of Year)
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On-board Light Data
Smooth
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“Sundial”
Length of day (LOD)
Noon
Local Noon Global Noon
Lts 1 Gts 1
Lts 2 Gts 2
… …
… …
Lts n Gts n
“Anchor Points”
argmax lag Xcorr (LOD lts, LOD gts, lag)
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Architecture
“Sundial”
“Anchor Points”
“Time Reconstruction Algorithm (E.g. RGTR)”
Universal Timestamps (unixts)
Light (localclock)
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Evaluation
Establish Ground Truth Results
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Ground Truth Fit
Used reconstructed Segments that passed Validation Checks
Validation of global timestamps Use Ambient Temperature data Correlate among sensors Correlate with co-located Weather Station
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Segments
“Leakin” Deployment
- MicaZ motes- 20 minute sampling- 6 boxes- Max Size : 587 days
“Jug bay” Deployment
- Telos B motes- 30 minute sampling - 13 boxes- Max Size : 167 days
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Reconstruction Results
Day Error
-Offset in days
-Proportional to Error in Intercept (β)
Minute Error
-RMSE Error in minute within the day
-Proportional to Error in slope/clock drift (α)
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Effect of Segment LengthExperimental Set up
-Select Segments of varying size-To eliminate bias,
- Segment-start chosen from a Uniform PDF-Use “Sundial” to reconstruct timestamps
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Eliminate Day ErrorPrecipitation Soil Moisture
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Eliminate Day ErrorPrecipitation Soil Moisture
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Discussion
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Discussion/Conclusion
Novel Post-mortem Timestamp Reconstruction Algorithm Not a synchronization-protocol
Works in conjunction with other timestamp reconstruction methods (RGTR, [1]),
Robust to “random mote-reboots” and “drifting global clocks”
Uses inexpensive on-board light data and annual solar patterns to reconstruct timestamps (no anchor points)
Experimental Results using light data sampled at 20 minutes Accuracy towards 10 parts per million Reconstruction within minutes (always within one sample period)
Data from nearby-weather stations can also be used Susceptible to “microclimate effects”
[1] G. Werner-Allen et. al, Yield in a Volcano Monitoring Sensor Network. In Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation (OSDI), Nov. 2006.
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Questions ?
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Extras
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Estimation of Clock Drift
Observations
-Difference in Clock drifts due to Node-Types
- Error in ppm is close to operating frequency of Quartz crystal
- Error is related to Length of Deployment (Leakin shows less error)
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Eliminate Day Error Day Error < 7 days (8 segments). Correlate data with “known” events (E.g. rain)
Correlate in local neighborhood Correlate daily Soil Moisture vectors with Rain Vectors 7 out of 8 aligned to the correct day
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Discussion Sundial uses well-established Solar patterns to reconstruct timestamps
Does not replace other Timestamp reconstruction methods (RGTR, [1]), but works in conjunction with them
Sundial can be used Motes disconnect from the network and reboot Base-station fails and motes reboot The global clock source is unreliable Independent validation using “LOD” and “noon” metrics Other ?
Data from nearby-weather stations can also be used Susceptible to “microclimate effects”
[1] G. Werner-Allen et. al, Yield in a Volcano Monitoring Sensor Network. In Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation (OSDI), Nov. 2006.
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