1

TASK in Redwood Trees

Wei Hong (IRB)Sam Madden (IRB, MIT)

Rob Szewczyk (UCB)Jan. 14, 2003

2

Acknowledgement TASK Team

Alan Broad (Xbow) Phil. Buonadonna (IRB) Anind Dey (IRB) David Culler (UCB, IRB) David Gay (IRB) Joe Hellerstein (IRB, UCB) Alan Mainwaring (IRB) Jaidev Prabhu (Xbow) Joe Polastre (UCB)

3

Outline TASK Overview Status Report Progress in Making TASK Real TASK/Deployment Roadmap The Redwood Tree Deployment Data From the Redwood Trees Calibration Results Conclusions

4

TASK Overview TASK = Tiny Application Sensor Kit,

formerly GSK, ASK. Sensor Network in a Box: rapid sensor

network deployment for non-computer scientists

Tools suite built on top of TinyDB Sensor metadata management Query configuration Network monitoring Data visualization Integration with DBMS and data analysis

tools

5

TASK Architecture

TASK Field Tools

JDBC/ODBC

DBMS(PostgreSQL)

TASK Client Tools

JDBCInternet

TASK Server

Sensornet Appliance

External Tools

TinyDB Sensor Network

6

Status Report Released with TinyOS 1.1!

Install the task-tinydb package apps/TASKApp,

tools/java/net/tinyos/task http://berkeley.intel-research.net/task

Successful deployments in Lab and UCBG redwood trees Largest deployment: ~80 weather

station nodes Network longevity: 4-5 months

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Progress in Making TASK Real Power Management Time Synchronization Improved Query Sharing Watchdog Improved Routing Layer

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Power ManagementCoarse-grained app-controlled communication

scheduling

1

2

3

4

5

Mote ID

time

Epoch (10s -100s of seconds)

2-4s Waking Period

… zzz … … zzz …

9

Power Management (cont) Benefits

Can still use CSMA within waking period No reservation required: new nodes can join easily!

Minimal code changes from previous code base without power management

Drawbacks Longer waking time vs. TDMA?

Could stagger slots based on tree-depth No “guaranteed” slot reservation

Nothing is guaranteed anyway Improvement

Adaptively setting waking period (currently hardwired to 4s)

Network size Sensor startup + acquisition time

10

Time Synchronization All messages include a 5 byte time stamp

indicating system time in ms Synchronize (e.g. set system time to timestamp) with

Any message from parent Any new query message (even if not from parent)

Punt on multiple queries Timestamps written just after preamble is xmitted

All nodes agree that the waking period begins when (system time % epoch dur = 0)

And lasts for WAKING_PERIOD ms Adjustment of clock happens by changing

duration of sleep cycle, not wake cycle. If node hasn’t heard from it’s parent for k

epochs Switch to “always on” mode for 1 epoch

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Improved Query Sharing Viral propagation of query messages

(each query many messages) Compensate for loss of query messages New nodes joining the network

Previous implementation Each node asks for all query messages

whenever a query result with unknown query id is snooped

Jams network! Improved implementation

Query request message contains bitmap of messages already received

Shut up if a neighbor has just requested for the same query

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Stopping a query Must stop query on all nodes at the

same time, or query rekindles Solution:

Explicitly notify neighbors of “dead” queries

Don’t share “dead” queries

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Watchdog New watchdog component Timer set to multiples of epoch

duration Watchdog reset every time a data

message is heard during an epoch Watchdog triggers when no data

messages are heard in multiple epochs.

Key: motes always resetable remotely!

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TASK/Deployment Roadmap Distributions of Do-It-Yourself kits, Q1’04

Stargate-based gateway appliance VB/HTML-based improved GUI tools PDA field tool New generation packaging of motes

Support for high data rate applications, Q2’04 Take over Intel fab vibration monitoring application GGB app?

Evolve into core software infrastructure for all Intel Research pilot projects

HP data center SAP asset tracking Etc.

Port to iMote

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The Redwood Tree Deployment Collaboration with Prof.

Todd Dawson Collect dense sensor

readings to monitor climatic variations across

altitudes, angles, time, forest locations, etc.

Versus sporadic monitoring points with 30lb loggers!

Current focus: study how dense sensor data affect predictions of conventional tree-growth models

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Data from the Redwood Trees

08/04 08/05 08/06 08/07 08/08 08/09 08/1040

60

80

100R

elat

ive

hu

mid

ity

(%)

Relative humidity at different heights

1020303440

08/04 08/05 08/06 08/07 08/08 08/09 08/10-30

-20

-10

0

10

20

Hu

mid

ity

Dif

fere

nce

(%

)

Date

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Redwood trees data

08/04 08/05 08/06 08/07 08/08 08/09 08/1010

15

20

25

30T

emp

erat

ure

( C

)

Temperature at different heights

08/04 08/05 08/06 08/07 08/08 08/09 08/10-5

0

5

10

Date

Tem

per

atu

re d

iffe

ren

ce(

C)

1020303440

18

Towards Gradient analysis

00:00 06:00 12:00 18:00 00:00-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

Time of day

Tem

per

atu

re d

iffe

ren

ce(

C)

Average temperature difference between top and bottom of the tree

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Temperature

00:00 06:00 12:00 18:00 00:00-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

Time of day

Tem

per

atu

re d

iffe

ren

ce(

C)

Average temperature difference between 30 feet and bottom of the tree

20

Relative humidity

00:00 06:00 12:00 18:00 00:00-15

-10

-5

0

5

10

Time of day

Rel

ativ

e h

um

idit

y d

iffe

ren

ce(%

)

Average RH difference between top and bottom of the tree

21

Relative humidity

00:00 06:00 12:00 18:00 00:00-15

-10

-5

0

5

10

Time of day

Rel

ativ

e h

um

idit

y d

iffe

ren

ce(%

)

Average RH difference between 30 feet and bottom of the tree

22

Data from the Redwood Trees

08/04 08/05 08/06 08/07 08/08 08/09 08/1040

60

80

100R

elat

ive

hu

mid

ity

(%)

Relative humidity at different heights

1020303440

08/04 08/05 08/06 08/07 08/08 08/09 08/10-30

-20

-10

0

10

20

Hu

mid

ity

Dif

fere

nce

(%

)

Date

23

The Calibration Process Growth chamber

calibration of temperature, humidity, light against trusted sensor

VLSB rooftop calibration of PAR sensor against trusted sensor

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Redwood trees data

08/04 08/05 08/06 08/07 08/08 08/09 08/1010

15

20

25

30T

emp

erat

ure

( C

)

Temperature at different heights

08/04 08/05 08/06 08/07 08/08 08/09 08/10-5

0

5

10

Date

Tem

per

atu

re d

iffe

ren

ce(

C)

1020303440

25

Towards Gradient analysis

00:00 06:00 12:00 18:00 00:00-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

Time of day

Tem

per

atu

re d

iffe

ren

ce(

C)

Average temperature difference between top and bottom of the tree

26

Temperature

00:00 06:00 12:00 18:00 00:00-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

Time of day

Tem

per

atu

re d

iffe

ren

ce(

C)

Average temperature difference between 30 feet and bottom of the tree

27

Relative humidity

00:00 06:00 12:00 18:00 00:00-15

-10

-5

0

5

10

Time of day

Rel

ativ

e h

um

idit

y d

iffe

ren

ce(%

)

Average RH difference between top and bottom of the tree

28

Relative humidity

00:00 06:00 12:00 18:00 00:00-15

-10

-5

0

5

10

Time of day

Rel

ativ

e h

um

idit

y d

iffe

ren

ce(%

)

Average RH difference between 30 feet and bottom of the tree

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03:00 06:00 09:00 12:00 15:00 18:000

100

200

300P

AR

( m

ol m

-2s-1

Ground truth data, growth chamber, Dec. 1, 2003

03:00 06:00 09:00 12:00 15:00 18:000

20

40

Tem

per

atu

re (

C)

03:00 06:00 09:00 12:00 15:00 18:000

50

100

150

Time

RH

(%

)

Chamber data -- reference

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Chamber data – measured

03:00 06:00 09:00 12:00 15:00 18:000

20

40P

AR

( m

ol m

-2s-1

Mote 69 data, growth chamber, Dec. 1, 2003

03:00 06:00 09:00 12:00 15:00 18:000

20

40

Tem

per

atu

re (

C)

03:00 06:00 09:00 12:00 15:00 18:000

50

100

Time

RH

(%

)

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Temperature measurements

5 10 15 20 25 30 355

10

15

20

25

30

35

Reference Temperature ( C)

Mea

sure

d T

emp

erat

ure

( C

)

Measured Temperature v. reference temperature, chamber Dec. 2003

Mote 69Mote 65Ideal fit

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Temperature error distribution

-3 -2 -1 0 1 20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Temperature difference ( C)

Fra

ctio

n o

f sa

mp

les

CDF of temperature errors

December 2003 chamber run

Bias of 1 C

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Relative humidity measurements

0 20 40 60 80 1000

10

20

30

40

50

60

70

80

90

100

Reference relative humidity (%)

Mea

sure

d r

elat

ive

hu

mid

ity

(%)

Measured humidity vs. reference sensor

Mote 69Mote 65

34

Relative humidity measurements

-20 -10 0 10 20 300

0.050.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.90.95

1

Relative humidity difference (%)

Fra

ctio

n o

f sa

mp

les

CDF of relative humidity errors

December 2003 chamber run

Errors computed assuming a constant sensor offset of 13.25%

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PAR Measurements – regression

18:00 00:00 06:00 12:00 18:00 00:00 06:00 12:00 18:00-200

0

200

400

600

800

1000

1200

1400

1600

Time

PA

R ( m

ol m

-2s-1

)Calibrated PAR and best fit measurements

fit=f(par, v, 1/v, par/v)

Calibrated DataFit DataDifference

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PAR Measurements

0 500 1000 15000

500

1000

1500Measured vs. calibrated PAR

Mea

sure

d P

AR

( m

ol m

-2s-1

)

Calibrated PAR (mol m-2s-1)

fit=f(par, v, 1/v, par/v)

Calibrated vs. Our DataIdeal fit

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PAR measurements

-200 -150 -100 -50 0 50 100 150 2000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Error magnitude (mol m-2s-1)

Fra

ctio

n o

f re

adin

gs

CDF of PAR errors

fit=f(par, v, 1/v, par/v)

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Still a ways to go…

08/04 08/05 08/06 08/07 08/08 08/09

0

500

1000

1500

2000

Date

Co

nve

rted

PA

R (

mo

l m-2

s-1)Converted PAR data

10203034

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Conclusions TASK readily available in

TinyOS1.1 Proven to work well in low-data-

rate, environmental monitoring type of applications

Love to get more “customers” Need more developers Have lots of data, love to share