Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks* *...

Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks*

*This work was partially supported by “Fondazione Cassa di Risparmio Padova e Rovigo” under the project ``A large scale wireless sensor network for pervasive city-wide ambient intelligence.”

RealWSN08 Workshop

ACM Eurosys 2008

April 1st 2008

Glasgow - UK

Giovanni Zanca, Francesco Zorzi, Andrea Zanella and Michele ZorziSignet research Group

Department of Information Engineering, University of Padova, Italy{zancagio,zorzifra,zanella,zorzi}@dei.unipd.it

OutlineOutline

RealWSN08 Workshop - Glasgow April 1st 2008

• Problem statement• Possible approaches• Wireless channel characterization• SOA review• Experimental setup• Results• Conclusions


Problem StatementProblem Statement

Position knowledge required by many WSN applications

Two main approaches

Nodes position hard written:

• High deployment cost/time

• Not always feasible

• Very accurate

Motes capable of self-localizing:

• Easy deployment

• Need dedicated hardware to achieve high precision


Localization ApproachesLocalization Approaches

Three main ranging approaches:

• Angle of Arrival

• Time of Arrival

• Received Signal Strength Indicator (RSSI)

Focus on RSSI:

• No specific Hardware required

• Poor outdoor ranging performance

• Very poor indoor ranging performance


Indoor Radio Channel Indoor Radio Channel CharacterizationCharacterization

Indoor Radio channel:

• Highly affected by log-normal shadowing

• Moderate path loss

Received power

Transmitted power

Path loss coefficient reference

distance

environmental constant

real transmitter-receiver distance Shadowing Shadowing

fast fading

td

dKPdBmP i

iTxi

010log10

Channel ModelChannel Model

• Path loss channel model: received power Pi @ distance di



Localization AlgorithmsLocalization Algorithms

RSSI sample

s

Anchor position

s

Localization

Algorithm

Mote position

Range free approach

• Avoid ranging by direct comparison of RSSI samples

• Independent of channel parameters

• Imperfect localization even with ideal channel

Range based approach

• Localization based on RSSI ranging

• Depend on channel parameters

•“Potential” perfect localization with ideal channel

Range based:• Min-Max

– Extremely simple– Limited performance

• Multilateration– Simple and scalable– Highly affected by noisy

samples• Maximum Likelihood

– Complex– Asymptotically optimum

Selected Localization Selected Localization AlgorithmsAlgorithms

Range free:• ROCRSSI

– Computationally demanding

April 1st 2008RealWSN08 Workshop - Glasgow


Mote Platform: EyesIFX V2Mote Platform: EyesIFX V2

• MSP430 MCU 4 MHz

• 10 KB RAM

• 48 KB ROM

• USB interface

• Infineon TDA5250 transceiver

• 900 MHz narrowband FSK

• External omni-directional antenna


Experimental TestbedsExperimental Testbeds

=1.51

=6.34 dB

=1.64

=6.82 dB

Testbed #1 Testbed #2


Results – Mean ErrorResults – Mean Error

• Localization error remains quite high

• ML benefits from increasing the number of beacons, unlikely Min-Max, ROCRSSI, Multilateration

• Better performance in testbed 2 due to smaller distance to the closest beacon


Results – Error CDFResults – Error CDF

• Min-Max performance does not improve by adding beacons

• Localization error is confined within a rather narrow range around 4 meters

• ML improves performance, though errors are distributed over a wide range


ConclusionsConclusions

• ML yields better performance than the others with more than 6-7 beacons

• Multilateration is much simpler but shows very low performance

• ROCRSSI also achieves low performance but it is independent of channel parameters

• Min-Max is extremely simple but tends to localize nodes in the center of the area

• RSSI ranging is very unreliable and does not appear suitable for indoor localization

Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks*

*This work was partially supported by “Fondazione Cassa di Risparmio Padova e Rovigo” under the project ``A large scale wireless sensor network for pervasive city-wide ambient intelligence.”

RealWSN08 Workshop

ACM Eurosys 2008

April 1st 2008

Glasgow - UK

Giovanni Zanca, Francesco Zorzi, Andrea Zanella and Michele ZorziSignet research Group

Department of Information Engineering, University of Padova, Italy{zancagio,zorzifra,zanella,zorzi}@dei.unipd.it

Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks* *...

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