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.