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 1 st 2008 Glasgow - UK Giovanni Zanca, Francesco Zorzi, Andrea Zanella and Michele Zorzi Signet research Group Department of Information Engineering, University of Padova, Italy {zancagio,zorzifra,zanella,zorzi}@dei.unipd.it
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Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks* * This work was partially supported by Fondazione Cassa.
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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
RealWSN08 Workshop - Glasgow April 1st 2008
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
RealWSN08 Workshop - Glasgow April 1st 2008
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
RealWSN08 Workshop - Glasgow April 1st 2008
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
RealWSN08 Workshop - Glasgow April 1st 2008
RealWSN08 Workshop - Glasgow April 1st 2008
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
• 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
RealWSN08 Workshop - Glasgow April 1st 2008
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
RealWSN08 Workshop - Glasgow April 1st 2008
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