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Copyright ©2013 by SJTU, IWCT.Dongchuan Road #800, Minhang,
Shanghai,200240All rights reserved.
Indoor Localization with a Crowdsourcing based Fingerprints Collecting
Copyright ©2013 by SJTU, IWCT.Dongchuan Road #800, Minhang,
Shanghai,200240All rights reserved.
System Architecture
Kernel Density EstimateSufficient Statistics
Extract FingerprintOptimum Reception Theory
ClusteringAffinity Propagation
Crowdsourced Process
User ADevice A
User BDevice B
User CDevice C
Crowdsourced Fingerprint Collection
Location AlgorithmK Nearest Neighbor (KNN)
User Upload Rss ValueUse Any Device
User ADevice A
Cluster MatchingAffinity Propagation
Grid Window FilterRestrict Estimate Results into
Sub Regions
AP DetectionRemove Aps below
threshold
Get Estimate Location Information
Cloud Computing Platform - CloudFoundry
Location Process Using Fingerprint Database
Cloud Computing Platform - CloudFoundry
MMC-KNN
FingerPrint Database for Diverse Devices
FingerPrint Database for Diverse Devices
Copyright ©2013 by SJTU, IWCT.Dongchuan Road #800, Minhang,
Shanghai,200240All rights reserved.
• Crowdsourcing based fingerprint extraction methods
• Localization Algorithms based on clustering theory
Key Technology
Copyright ©2013 by SJTU, IWCT.Dongchuan Road #800, Minhang,
Shanghai,200240All rights reserved.
• In crowdsourcing model, multiple users will upload fingerprints via diverse devices
• Our method extract fingerprint value based on RSS probability estimation, choose the optimum value from upload samples
• Kernel density estimation eliminates device diversity than Gaussian probability estimation
Fingerprints Extraction
Copyright ©2013 by SJTU, IWCT.Dongchuan Road #800, Minhang,
Shanghai,200240All rights reserved.
• Comparison of Gaussian and Kernel density estimation:
Fingerprints Extraction
Copyright ©2013 by SJTU, IWCT.Dongchuan Road #800, Minhang,
Shanghai,200240All rights reserved.
• Based on kernel density estimation, choose optimum value from multiple upload RSS samples by multiple users by diverse devices.
Fingerprints Extraction
Copyright ©2013 by SJTU, IWCT.Dongchuan Road #800, Minhang,
Shanghai,200240All rights reserved.
• MMC-KNN algorithm: find M most matched clusters, then apply KNN principle to choose out matched fingerprint
• Use affinity propagation to process clustering:
Localization Algorithm: MMC-KNN
Copyright ©2013 by SJTU, IWCT.Dongchuan Road #800, Minhang,
Shanghai,200240All rights reserved.
• How to find out the M most matched cluster?– Consider uploaded observation’s connections and
similarities with all exemplars– Apply affinity propagation again and get
responsibility vector:
– choose the M most matched cluster by sort this responsibility vector
Localization Algorithm: MMC-KNN
Copyright ©2013 by SJTU, IWCT.Dongchuan Road #800, Minhang,
Shanghai,200240All rights reserved.
• Assign a weight factor to each cluster’s fingerprints
• Apply a grid window filter to filter a region which has the maximum weight, with the purpose to restrict KNN applied to a bursting region
Localization Algorithm: MMC-KNN
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D f o
Copyright ©2013 by SJTU, IWCT.Dongchuan Road #800, Minhang,
Shanghai,200240All rights reserved.
• Average error distance with different matched cluster number and grid window size for Nexus-S
Real-time experimental testbed
Copyright ©2013 by SJTU, IWCT.Dongchuan Road #800, Minhang,
Shanghai,200240All rights reserved.
• 220 observation’s error distance statistic with best performance parameters for Nexus-S
Real-time experimental testbed
Copyright ©2013 by SJTU, IWCT.Dongchuan Road #800, Minhang,
Shanghai,200240All rights reserved.
• CDF of location error distance for different algorithms
Real-time experimental testbed