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Locating in fingerprint space: wireless indoor localization with little human intervention.Proceedings of the 18th annual international conference on Mobile computing and networking. ACM, 2012.

RSSI FingerprintAutomatic Radio Map Generation

Presenter: Jongtack Jung

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Localization technique where each location is associated with the RSSI Fingerprint of the location

Arbitrary fingerprint from an un-known location is matched with the radio map, and best fittingoption is selected

RSSI Fingerprint Method?

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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 dis-

similarities among the points are known Stress (MDS term)

How well the mapping expresses the dissimilarity matrix

Terminology

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PROS All APs can be exploited

Including password pro-tected APs

Fast execution

Best accuracy of all

Pros and Cons of RSSI Fingerprint

CONS Necessary training period

Necessary maintenance

EXPENSIVE Training and maintenance

require human labor

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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 auto-

mate the process of site surveying

RSSI Fingerprint

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Main Idea Since the geographic distance does not really represent the actual walk-

ing 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 dis-

tance between locations Hybrid of fingerprint and dead reckoning

Locating in Fingerprint Space – Innovation!

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Overview

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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

Stress-free Floor Plan

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The distance between finger-prints 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

Fingerprint Space

High dimension floor plan (top) and fingerprint space map(bottom)

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

Pre Processing

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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

Space Transformations

MST of fingerprint space map

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The virtual high dimensional data needs to be mapped on ac-tual floor plan

Corridor recognition MST betweenness

Room Recognition Clustering of nodes

Reference Point Mapping Point where values change largely

are considered as doors

Mapping

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Betweenness Centrality Dis-tribution of all points

K-Means Clustering of all points

Evaluation Results

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Evaluation Results

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Fingerprints clusters vs. Floor plan rooms

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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

Notes on High Dimension Fingerprint

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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

Conclusion