“Minimal Transmission Power vs. Signal Strength as ... · 4 4 Frank Reichenbach University of Rostock „Minimal Transmission Power vs. Signal Strength as Distance Estimation for

“Minimal Transmission Power vs. Signal Strengthas Distance Estimation for Localization in

Wireless Sensor Networks”

International Workshop on Wireless Ad-hoc and Sensor Networks (IWWAN 2006), New York

Jan Blumenthal, Frank Reichenbach, Dirk Timmermann

Institute of Applied Microelectronics and Computer EngineeringUniversity of Rostock

June 30, 2006

22

Frank ReichenbachUniversity of Rostock

„Minimal Transmission Power vs. Signal Strengthas Distance Estimation for Localization in

Wireless Sensor Networks“

IWWAN 2006

Outline

Problem StatementClassificationBackgroundDistance Estimation TechniquesNew Approach: Minimal Transmitting PowerWeighted Centroid Localization AlgorithmResultsConclusion

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IWWAN 2006

Problem Statement

PreconditionsHundreds of sensor nodes are randomly deployedPosition initially unknown

Why do we need localization?Position ↔ MeasurementSelf organization, -healingGeographic Routing

ConsiderationsNodes miniaturizedNodes strongly resource limitedChanging dynamic topologyNodes are error-prone

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IWWAN 2006

Conceivable Solution

Integrating existing localization system on every nodeGPS, GSM, Galileo

But:Sensor nodes are strongly resource limited

GPS has a relatively high power consumptionSensor nodes have to be tiny

GPS modules are comparatively largeLocalization availability

GPS does not work everywhereNodes must be cheap

GPS costs additionally

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IWWAN 2006

Problem Solution

Equip only some nodes with e.g. GPSBeacons

Rest of the nodesUnknowns

Estimation of the position withDistancesAngles

αβ

γ

d1

d2d3

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IWWAN 2006

Why not just using Trilateration?

Only 3 beacons needed (2D)Simple to calculateBut:

Distance estimations are highly defective!

RSSI over distance indoor (868MHz)

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IWWAN 2006

Classification

Exact Localization(fine-grained)

Approximate Localization(coarse-grained)

• Angulation• Trilateration• Least Squares• Kalman Filter

Examples:• Dynamic Fine Grained Multilateration

(Savvides et al.)• Acoustic with Least Squares (Kwon et al.)

Examples:• Coarse Grained Localization (Bulusu)• APIT (He et al.)• Weighted Centroid Localization

(Blumenthal et al.)• Convex Position Estimation

(Doherty et al.)

• Geometric• Proximity• Scene analysis

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IWWAN 2006

Observation Techniques

NeighboringNodes Distance Estimation Angle

Estimation

TimeMeasurement

SignalMeasurement

TimeMeasurement

„Time of Arrival“(ToA)

„Time Differenceof Arrival“(TDoA)

„Received Signal Strength Indicator“

(RSSI)

„Angle of Arrival“(AoA)

„Minimal Transmission

Power“

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IWWAN 2006

Distance Estimation with RSSI in Theory

Received Signal Strength Indicator supported by hardware

Cheap and always availableCircuit measures the received energy of a signalCompared to a reference voltageReceived Power:

Logarithm:

20

4R

R SS

P G GP d

λπ

⎛ ⎞= ⎜ ⎟⎝ ⎠

20

4R

R SS

P G GP d

λπ

⎛ ⎞= ⎜ ⎟⎝ ⎠

[ ]2

0( ) [ ] 10 log 20 log( )4R S R SP d dBm P dBm G G dλπ

⎡ ⎤⎛ ⎞= + ⋅ − ⋅⎢ ⎥⎜ ⎟⎝ ⎠⎢ ⎥⎣ ⎦

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IWWAN 2006

Problems with RSSI in Practice

Transceiver sensitivitySignals in real world are strongly influencedAttenuation when passing objects

876MHz 8-20dB by a tree2.4 GHz bricks 3dB, tinted glass walls 19dB

Signal propagation characteristics can change frequentlyReceived Signal Strength depends on battery level

Blocking Reflection Scattering Diffraction

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IWWAN 2006

Result Measurements: Indoor (Chipcon)

Chipcon CC1010

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IWWAN 2006

Distance Estimation with Power Adjustment

Estimation of the minimal transmitting power of a beaconTransmitting power corresponds to the distanceTransmitting power P is a register SFR in the hardwareTransmitting power SFR adjustable in the interval SFR=0..100 (d=1-300m)

SFR=11

2m

SFR=14 SFR=16

Distance d to nodes

SFRmin

P

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IWWAN 2006

Result Measurements: Indoor (Scatterweb)M

in. S

endi

ng R

egis

ter V

alue

SFR

50 400100 150 200 250 300 350-5

0

5

10

15

20

25

30

35

Distance [cm]

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IWWAN 2006

Result Measurements: Linearized

What is d?

0

100

200

300

400

500

600

700

800

900

5 30 55 80 105 130 155 180 205 230 255 280 305 330 355 380Distance [cm]

Min

. Sen

ding

Reg

iste

r Val

ueSF

R

22 ( )( ) 1.3, 0

1.3SFR dSFR d m d n d with m n= ⋅ + → = = =

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IWWAN 2006

Weighted Centroid Localization (WCL)

Approach:Including distances in localizationDefine a function for the weight wij(d)

( )1

1

( , )''( , )

b

ij jj

i b

ijj

w B x yP x y

w

=

=

⎛ ⎞⋅⎜ ⎟

⎝ ⎠=⎛ ⎞⎜ ⎟⎝ ⎠

∑

∑

1

1'( , ) ( , )n

i jj

P x y B x yn =

= ∑

WCL

CGLCD

''( , )iP x y'( , )iP x y

1B 2B

3B4B

4id3id

2id1id

( , )iP x y

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IWWAN 2006

Determination of the Weight

Free parameter is the weightSubstituting the weight:

Optimal degree g?DensityPlacement

Simulations g = 3 is optimalReal g = 2

( )1

1

( , )''( , )

b

ij jj

i b

ijj

w B x yP x y

w

=

=

⎛ ⎞⋅⎜ ⎟

⎝ ⎠=⎛ ⎞⎜ ⎟⎝ ⎠

∑

∑

( )( )1

ij g

ij

wd SFR

=

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IWWAN 2006

Realization of a Localization

Beacons transmit position with increasing transmitting powerNode saves minimal transmitting powerBeacon reached max. transmitting power

counter for the round is incrementedRound based system via “Token Ring”-mechanism

Beacon (known position)Sensor node (unknown position)

B3

B1 B2

B4

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IWWAN 2006

Demonstrator ShowGraphical User Interface: SpyGlass (University of Luebeck)Transceiver Hardware: Scatterweb (FU Berlin)4 beacons at all corners, one unknown node was movedField size = 2x2 mYields in a running localization with 0,3m absolute error

http://rtl.e-technik.uni-rostock.de/~bj/movies/PositionEstimationUsingMinimalTransmissionPower.mpg

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IWWAN 2006

Discussion

ProsIncomplex Calculation (WCL) O(n)Fully distributed on every sensor node autonomouslyTransmission activity only on beaconsRelatively robust against defective inputs

ConsErrors arise by non-circular bordersRound-based transmission stresses the channelLatencyLimited precisionStrongly hardware dependent (Chipcon not possible)Transmission range depends on battery level

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IWWAN 2006

Conclusions and Future Work

Localization in WSN is strongly demandedMost classical observation techniques are defective (ecsp. indoor)

New Approach: Minimal transmitting power

Verification:High resolution and smaller variances than RSSICombining localization algorithm WCL with MTP showed good practical results2x2m field with 9 areas max. localization error = 0.3m

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IWWAN 2006

Thank You!

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IWWAN 2006

Substituting the Parameters

v

2

( )4E SP P S

rλπ

⎛ ⎞= ⎜ ⎟⎝ ⎠

Sr P=d

Ps2

( )4E SP P S

rλπ

⎛ ⎞= ⎜ ⎟⎝ ⎠

2

( )4E SP P S

dλπ

⎛ ⎞= ⎜ ⎟⎝ ⎠

2( )Sd P S=

? SS P=

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IWWAN 2006

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“Minimal Transmission Power vs. Signal Strength as ... · 4 4 Frank Reichenbach University of Rostock „Minimal Transmission Power vs. Signal Strength as Distance Estimation for