1. crowdsourcing 2. unsupervised learning

Localization by Crowdsourcing

1. Rai A, Chintalapudi K K, Padmanabhan V N, et al. Zee: zero-effort crowdsourcing for

indoor localization, Mobicom 2012: 293-304.

2. Zheng Yang, Chenshu Wu, and Yunhao Liu, "Locating in Fingerprint Space: Wireless

Indoor Localization with Little Human Intervention", (PDF), ACM MobiCom 2012,

Istanbul, Turkey, August 22-26, 2012.

3. Chenshu Wu, Zheng Yang, Yunhao Liu, "Smartphones Based Crowdsourcing for

Indoor Localization", (PDF), IEEE Transactions on Mobile Computing (TMC), Vol. 14,

Issue 2,February, 2015, Pages 444-457.

4. Xinglin Zhang, Zheng Yang, Chenshu Wu, Wei Sun, Yunhao Liu, Kai Liu, "Robust

Trajectory Estimation for Crowdsourcing-Based Mobile Applications", IEEE

Transactions on Parallel and Distributed Systems (TPDS), Vol. 25, No. 7, July 2014,

Pages 1876-1885.

5. Ruipeng Gao, Mingmin Zhao, Tao Ye, Fan Ye, Yizhou Wang, Kaigui Bian, Tao Wang,

and Xiaoming Li. 2014. Jigsaw: indoor floor plan reconstruction via mobile

crowdsensing. (MobiCom '14). ACM, New York, NY, USA, 249-260.

1. Wang H, Sen S, Elgohary A, et al. No need to war-drive: unsupervised indoor

localization. MobiSys, 2012: 197-210.

2. Moustafa Alzantot and Moustafa Youssef. 2012. CrowdInside: automatic

construction of indoor floorplans. (SIGSPATIAL '12). ACM, New York, NY, USA,

99-108.

3. Yifei Jiang, Xin Pan, Kun Li, Qin Lv, Robert P. Dick, Michael Hannigan, and Li Shang.

2012. ARIEL: automatic wi-fi based room fingerprinting for indoor localization.

(UbiComp '12). ACM, New York, NY, USA, 441-450.

4. Bao X, Liu B, Tang B, et al. PinPlace: associate semantic meanings with indoor

locations without active fingerprinting, Ubicomp. 2015: 921-925.

5. Tachikawa M, Maekawa T, Matsushita Y. Predicting location semantics combining

active and passive sensing with environment-independent classifier,

ubicomp2016. ACM, 2016: 220-231.

Localization by Unsupervised Learning

Timeline of localization by crowdsourcing

and unsupervised learning

LiFS, Mobicom

WILL, Infocom

ZEE, Mobicom

2014

Robust CS,

TMC

Crowdsourcing

TMC

Jigsaw

Mobicom

Crowdsourcing

2012

Crowdinside,

sigspatial

ARIEL, uibcomp

No need for war drive,

Mobisys

2015 2016

Pinplace,

ubicomp2015

Predicting

location

semantics,

ubicomp2016

Unsupervised

learning

2014 2012 2015 2016

LiFS: Locating in Fingerprint Space with

little human intervention, [mobicom12]

• How to train RSS fingerprint of indoor path

automatically utilizing crowdsourcing?

LiFS

Stress-free Floor Plan

𝑑11 ⋯ 𝑑1𝑛⋮ ⋱ ⋮

𝑑𝑛1 ⋯ 𝑑𝑛𝑛

MDS

Walking distance

The MDS give every sample a new coordinate,

with which the Euclidian distance reflects the

walking distance in a real floor plan. 3D stress-free floor plan

Multidimensional Scaling (MDS) Torgerson (1952), Borg & Groenen, 1997

Classical MDS: Given a distance matrix among N

points, calculate the coordinates of these points in d

dimensional space.

0 20 20

20 0 20

20 20 0

D

1

2

3

x

x

x

?

1

22

1 2

1, ,

( , )D N ij i j

i j N

Stress x x x d x x

Minimize Stress:

Multidimensional Scaling (MDS)

• The distance matrix D2=[d2ij] can be converted to

a semi-definite matrix B by double centering:

where J called centering matrix

21'

2B JD J XX

11 *1 'n nJ I

n

Double

centering

Multidimensional Scaling (MDS)

• B is semi-positive definite, so by eigen-

decompositon:

1

1 112 2

'B XX V V

V V

1

2X V

Having d positive

eigenvalues

MDS Algorithm

1. Set up

2. Apply double centering ,

3. Determine the m largest eigenvalues

and corresponding eigenvector

4. Now,

(2) 2[ ]ijD d

(2)1

2B JD J

111J I

n

1 2, ,..., m

1 2, ,..., me e e

1/2

m mX E

Example of MDS

Fingerprint Collection

𝑓 = 𝑠1, 𝑠2, …… 𝑠𝑛 ,where 𝑠𝑖 is the RSS

of the ith AP.

Let 𝑑𝑖𝑗 denotes the distance between

the positions of 𝑓𝑖 and 𝑓𝑗, it is measured

by the number of footsteps during the

movement.

To avoid accumulation of measurement

errors, we adopt the individual step counts

as the metric of walking distance.

Fingerprint preprocessing

• Clustering

• Cluster fingerprints from the same or close locations.

• Parameter is determined by fingerprint samples collected at a

given location (when phones are not moving).

Distance matrix

• Shortest-path selection

• More than one path passing through two fingerprints

• Simply select the shortest one as the distance between them.

• Floyd-Warshall algorithm to compute all-pair shortest

paths of fingerprints.

Fingerprint space construction

• According to distance matrix, transform all points in to a

d-dimension Euclidean space, i.e., the fingerprint space,

using MDS.

Mapping

• Mapping the fingerprint space to the stress-free floor plan to obtain

fingerprint-location database

Mapping

• The mapping seems easy for humans, for computers, however,

it is non-trivial.

• Their solution: Mapping corridors first, then rooms.

Corridor Recognition

• Build the Minimum Spanning Tree(MST) that connects all

fingerprints in 𝐹.

• Corridors F𝑐: Fingerprints collected at corridors reside in core

positions in fingerprint space, which have relatively large centrality

values.

• Rooms 𝐹𝑅𝑖: Remove corridor points from the fingerprint space and

cluster the remaining points into 𝑘 clusters

Corridor Recognition

Reference Point Extraction

• Reference Point Mapping: Find keys from the doors!

• Find the set of corresponding points 𝑃𝐷 = {𝑝1, 𝑝2, … , 𝑝𝑘} in the floor

plan, which denote the set of sample locations in the corridor that

are the closest to every door.

Reference Point Mapping

• Mapping 𝐹𝐷 to 𝑃𝐷

1 2 1 1, , , ,k i i il l l l l p p

' ' ' '

1 2 1 1' , , , ,k i i il l l l l f f

'' '' '' ''

1 2 1 1'' , , , ,k i k i k il l l l l f f

Vector similarity:

Space Transformation

i iy Rx T

ix A coordinate in fingerprint space

A coordinate in floor plan space iy

𝑅 𝑇 and are calculated by LSQ

Room-level Transformation • Using doors and room corners as reference points, the fingerprints

and sample locations are linked by the transformation matrix.

Locating Performances

No Need to War-Drive:

Unsupervised Indoor Localization (Unloc)

1. Wang H, Sen S, Elgohary A, et al. No need to war-drive: unsupervised indoor localization. MobiSys,

2012: 197-210.

Track human walking trajectory (called dead reckon) – Using accelerometer and electronic compass

Cannot work in GPS-denied area

Successfully used for outdoor localization

– Use GPS reset points (landmarks) and dead-reckon inbetween

There are landmarks indoor

We can find landmarks if we see through the “eye of sensors”

Indoor landmarks

Key Idea: Certain locations in an indoor environment present

identifiable key signatures on one or more sensing dimensions.

Seed landmarks (SLM)

• UnLoc looks into the floorplan of the building and identifies some

“seed landmarks”.

• Examples:

• Stairs

• Elevators (start/stop)

• Escalator

• Building entrances (indoors/outdoors)

• The location of the SLMs are known.

Organic Landmarks (OLMs)

• Any indoor environment will offer some ambient

signatures across one or more seeing directions

• Magnetic Domain – metals may produce unique fluctuations on

magnetometer.

• WiFi Based – overheard WiFi base-stations.

• They cannot be known a priori, and will vary across

different buildings. They have to be learnt dynamically.

Finding OLMs by clustering sensor data

Framework of Unloc

Ideal case

Simultaneously locating and marking

Locating landmarks

Decision tree for detection SLMs

Online video:

https://www.youtube.com/watch?v=VhkXC6TnWzk

Summary

Fingerprint-based Localization

Localization by Acoustic Features

Localization by Lighting Features

Localization by Channel State Indicator (CSI)

Localization by Environment Features

Crowdsourcing and unsupervised learning

Crowdsourcing

Unsupervised learning