Indoor Localization Based on Response Rate of Bluetooth Inquiries

Mortaza S. Bargh & Robert de Groote Telematica InstituutThe Netherlands

19 September 2008

Outline

• Motivations• Approach/solution• Results• Conclusion

Motivations

• Colleague Radar™ application– locate employees in the building for the colleagues

• Indoor localization– No GPS– Ongoing research

• Bluetooth being pervasive– Cell phones – (Always) with people– Have Bluetooth– Being discoverable

Indoor localization

• Successful indoor localization systems– Integrate smoothly with existing infrastructures– Preferably require no upgrade of user devices– Need no excessive hardware installation – Use existing technologies– Impose low power consumption on mobile devices– Use low cost infrastructure

• Bluetooth based approaches– Based on RSSI – Based on LQ (link quality)

Bluetooth Inquiry Response Rate

• IRR (Inquiry Response Rate) = the percentage of inquiry responses to total inquiries in a given observation window

�������������

�������

�����������

������� ��

��������

An experiment

position IRR average out of 50 inquiries

IRR variance out of 50 inquiries

1 48.4 0.9

2 48.5 0.6

3 49.4 0.5

4 46.9 0.7

5 43.1 2.3

6 36.8 4.8

7 33.3 2.3

8 NULL NULL

9 NULL NULL

• Each row: – 240 sliding windows (slides every ~ 5 seconds)– Window size = 50 inquiries

Our setting

detected by

A classification problem: location fingerprint

• Obtain location fingerprint L • Compare it with training fingerprints Tk (of room k=1, 2, …)

– Kullback-Liebler (KL) measure – Jensen-Shannon (JS) distance measure

���������������������������������������

����

������������

�������������

�������������

�������������

�����

����� �����

�����

�� �!"��##�$���������������%

Typical outputs of the classification processchoose a PMF (or room) that minimizes the divergence

Test 1: full coverage

D1

D3

1 at 10:00

2

at 11:00

3at 12:00

4

at 13:00

5at 14:00

6

at 15:00

7at 16:00

D1

D3

1 at 10:00

2

at 11:00

3at 12:00

4

at 13:00

5at 14:00

6

at 15:00

7at 16:00

rooms with donglesrooms without donglestest room

ref device

unknown device

Test 2: partial coverage

D1

Mortaza

1at 12:06

2

at 11:00

3

at 15:32

4

at 14:38

5

at 13:31

6at 17:33

at 16:34

D1

Mortaza

1at 12:06

2

at 11:00

3

at 15:32

4

at 14:38

5

at 13:31

6at 17:33

at 16:34

rooms with donglesrooms without dongles

test room

G12

ref device

unknown device

Location estimation results – (1)

• Full coverage • Using Kullback-Liebler (KL) divergence measure• Training window: 30’ and 5’

8486889092949698

100102

1 2 3 4

licalization window (minutes)

accu

racy

(%)

30' training

5' training

Location estimation results – (1)

• 2 problems with basic KL method:– sensitivity to the timing of training data: a drop of

accuracy to 83% (WT=30’) or to 77% (WT=5’)– sensitivity to BT dongle coverage: accuracy 15…45%

8486889092949698

100102

1 2 3 4

licalization window (minutes)

accu

racy

(%)

30' training

5' training

Location estimation results – (2)

• Using Jensen-Shannon (JS) distance measure

75

80

85

90

95

100

105

1 2 3 4

localization window (minutes)

accu

racu

(%)

KL 30' training KL 5' training

75

80

85

90

95

100

105

1 2 3 4

localization window (minutes)

accu

racu

(%)

KL 30' training KL 5' trainingJS 30' trainingJS 5' training

Location estimation results – (3)

• JS measure: (1) change of training data

75

80

85

90

95

100

105

1 2 3 4

localization window

accu

rac

(%)

JS 30' training

JS 5' training

75

80

85

90

95

100

105

1 2 3 4

localization window

accu

rac

(%) JS 30' training

JS 5' training

JS 30' fresh training

JS 5' fresh training

Location estimation results – (4)

• JS measure: – (2) partial dongle coverage– (3) training window

���"&''#$����������$����$

• IRR is a valid approach • Robust with respect to device change• Time consuming, but acceptable for some

application domains• Good training fingerprints are not necessarily the

most recent ones• Accuracy of two best estimates is almost 100%• Increasing observation window size increases

accuracy up to a limit • Better performance requires a dedicated Bluetooth

network

Conclusions

Measured network characteristics: Response Rate

• Response Rate (RR): – “the percentage of times that a given Access Point

was heard in all of the WiFi scans at a specific distance from that AP” [CHE05]

– “the frequency of received measurements over time from a given base station” [KJA 07]

Some formulas

• PMFs of observed location and room k: and• Kullback-Liebler (relative entropy) measure:

• Jensen-Shannon distance:

1

( || ) ( ( ) || ( ))M

k m k mm

D L T D L d T d=

=�

L kT

1 1( || ) ( || ) ( || )

2 21

( )2

k k

k

JSD L T D L M D T M

M L T

= +

= +

, ,, ,

, ,

1( ( ) || ( ) ) log (1 ) log

1k k

L m L mm k m L m L m

T m T m

p pD L d T d p p

p p

−= + −

−

Location estimation results – (1)

• Full coverage • Using Kullback-Liebler (KL) divergence measure• Training window: 30 minutes (30’)

95,596

96,597

97,598

98,599

99,5100

100,5

1 2 3 4

localization window (minutes)

accu

racy

(%)

top-1

top-2

Summary

• Localization of stationary users (at this stage)• Indoor localization for multi floor buildings with dense

deployment of BT sensors• Infrastructure-based and network-based • Direct location (without any transformation) • Network characteristics used: response rate

– the frequency of received measurements over time from a specific base station

• (we did not address privacy issues)

Test result summary

• JS measure– WL=3 minutes– WT=10 minutes

• Performance:– Good coverage

• Top-1: 97.82% � same device 99.84%• Top-2: 100% � same device 100%

– Partial coverage • Top-1: 75% � same device 99.27• Top-2: 99.88 � same device 100

System overview

room fingerprint collection

radio map(room RR

fingerprints)

location fingerprintdetection

location estimation

location estimate(s)

movementdetection