copyright ©2013 by sjtu, iwct. dongchuan road #800, minhang, shanghai,200240 all rights reserved....

Copyright ©2013 by SJTU, IWCT.Dongchuan Road #800, Minhang,

Shanghai,200240All rights reserved.

Indoor Localization with a Crowdsourcing based Fingerprints Collecting



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



• Crowdsourcing based fingerprint extraction methods

• Localization Algorithms based on clustering theory

Key Technology



• 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



• Comparison of Gaussian and Kernel density estimation:




• Based on kernel density estimation, choose optimum value from multiple upload RSS samples by multiple users by diverse devices.




• 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



• 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




• 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


( )( , )

cew f

D f o



• Average error distance with different matched cluster number and grid window size for Nexus-S

Real-time experimental testbed



• 220 observation’s error distance statistic with best performance parameters for Nexus-S




• CDF of location error distance for different algorithms




• Comparison of different types devices’ location performance under diverse fingerprint databases


copyright ©2013 by sjtu, iwct. dongchuan road #800, minhang, shanghai,200240 all rights reserved....

Documents

dongchuan road

mmcknn slide

system architecture

mmcknn algorithm

clusters fingerprints

knn principle

matched clusters

rss probability estimation