“Fingerprints cannot lie, but liars can make fingerprints.” —Unknown.
On Clustering RSS Fingerprints for Improving Scalability of … · On Clustering RSS Fingerprints...
Transcript of On Clustering RSS Fingerprints for Improving Scalability of … · On Clustering RSS Fingerprints...
MELT 2008September 19, 2008
Nattapong Swangmuang and Prashant Krishnamurthy
Graduate Program in Telecom/NetworkingUniversity of Pittsburgh, PA USA
On Clustering RSS On Clustering RSS FingerprintsFingerprints for Improving for Improving Scalability of Performance Prediction of Indoor Scalability of Performance Prediction of Indoor
Positioning SystemsPositioning Systems
Paper’s goals
• Enhance the model� for analyzing Wi-Fi location fingerprinting-based system using a proximity graph
• Study characteristics of fingerprint clusters and applying it to the performance modeling
Research Questions
• Can we reduce computational effort and make the model more scalable ?
• How much is its impact to the prediction of precision performance ?
Clustering Methodology
• Different clustering methods:
• median and K-mean
• Model each cluster separately
• Use measurement data in an office building environment
� IEEE PerCom 2008’s paper
Analytical Model: Precision as Probability
P{correct}
P{error}
comparison variable
chance of picking k over i
weights against neighbors of i
� Multi-location system: computing exact probabilty requires complex jointed probabilities and becomes prohibitive
– Solution: approximate prob. Given a MS at the i-th grid point, the prob. of selecting each fingerprint is derived
Fingerprint Clustering Example
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IS Building with 25 Location Fingerprints (K-MEAN cluster)
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Distance (meters)
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Average Error Distance Distribution of 25 Locations
nonelim
elim-nocluster
elim-median
elim-kmean
9 fps
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No significant difference in performance
No. of Operations Comparison
Median K-MeanOffline 8,284 10,789 31,875Online 27 29 50Offline 38,147 42,230 149,940Online 44 46 84
ClusteringNo ClusteringPhase
Scenario1
Scenario2
Conclusion
� Empirical study shows the model with fingerprint clustering maintain good performance
• No difference in the CDF of error distance curve
� With Clustering, the model becomes more scalable
• Save many operations required from the model without clustering
• Reduce # operations during both the offline and online phases
MELT 2008September 19, 2008
Nattapong Swangmuang and Prashant Krishnamurthy
Thank you
On Clustering RSS On Clustering RSS FingerprintsFingerprints for Improving for Improving Scalability of Performance Prediction of Indoor Scalability of Performance Prediction of Indoor
Positioning SystemsPositioning Systems