Locating in fingerprint space: wireless indoor localization with little human intervention. Proceedings of the 18th annual international conference on Mobile computing and networking. ACM, RSSI Fingerprint Automatic Radio Map Generation Presenter: Jongtack Jung

 Localization technique where each location is associated with the RSSI Fingerprint of the location  Arbitrary fingerprint from an unknown location is matched with the radio map, and best fitting option is selected 2 RSSI Fingerprint Method?

 Site survey process  Training phase a.k.a. calibration  Fingerprint  A set of RSS values obtained at a location  Radio map  The map of RSS fingerprints associated with the location  MDS (Multi-Dimensional Scaling)  A method to map points into given dimensional space where only the dissimilarities among the points are known  Stress (MDS term)  How well the mapping expresses the dissimilarity matrix 3 Terminology

PROS  All APs can be exploited  Including password protected APs  Fast execution  Best accuracy of all 4 Pros and Cons of RSSI Fingerprint CONS  Necessary training period  Necessary maintenance  EXPENSIVE  Training and maintenance require human labor

 The cost of RSSI Fingerprint method can be reduced using automated status update mechanism  The concept of automation is adopted  Many methods have been attempted to automate the process of site surveying 5 RSSI Fingerprint

 Main Idea  Since the geographic distance does not really represent the actual walking distance of two positions, use walking distance to create a map  Concept  Two position close together in walking distance means similar fingerprint  The number of footsteps obtained from accelerometer provides the distance between locations  Hybrid of fingerprint and dead reckoning 6 Locating in Fingerprint Space – Innovation!

7 Overview

 Stress  The accuracy of MDS  If a distance map can be perfectly resolved in given dimensions, the stress is 0  Given dataset, higher dimension means less stress  Draw 3D floor plan  Disparity between two locations is given with the number of footsteps  The distance between two nodes in the graph is the actual walking distance  Footstep recognition  The number of footsteps is obtained from accelerometer – only the #steps, not the distance 8 Stress-free Floor Plan

 The distance between fingerprints can also be expressed with disparity map  MDS algorithm is tolerant to measurement errors on its own  If no user actually passes through a particular pair of fingerprints, the value is calculated with shortest path 9 Fingerprint Space High dimension floor plan (top) and fingerprint space map(bottom)

 With above equation as dissimilarity, two points having the value less than threshold are considered as the same point and merged together. 10 Pre Processing

 Fingerprint space needs to be mapped on stress-free floor plan  Floor-level transformation  Use simplest linear transformation and shift between the two graphs  Room-level transformation  Detect rooms with K-cluster method and apply MDS to each room, and then match them 11 Space Transformations MST of fingerprint space map

 The virtual high dimensional data needs to be mapped on actual floor plan  Corridor recognition  MST betweenness  Room Recognition  Clustering of nodes  Reference Point Mapping  Point where values change largely are considered as doors 12 Mapping

 Betweenness Centrality Distribution of all points  K-Means Clustering of all points 13 Evaluation Results

14 Evaluation Results

15 Fingerprints clusters vs. Floor plan rooms

 The result is not so much impressive, but the values indicate the Fingerprint generation without site survey is possible  Fingerprint generation needs to be conveyed with human hands, but the required labor for the system is reduced a lot 16 Notes on High Dimension Fingerprint

 RSSI Fingerprint method’s credibility has been widely accepted to be the best method  It shows slightly less accuracy than traditional fingerprint method, but the cost is reduced by much 17 Conclusion