indoor localization based on response rate of bluetooth ... · indoor localization based on...
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Indoor Localization Based on Response Rate of Bluetooth Inquiries
Mortaza S. Bargh & Robert de Groote Telematica InstituutThe Netherlands
19 September 2008
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Outline
• Motivations• Approach/solution• Results• Conclusion
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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
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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)
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Bluetooth Inquiry Response Rate
• IRR (Inquiry Response Rate) = the percentage of inquiry responses to total inquiries in a given observation window
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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
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Our setting
detected by
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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
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Typical outputs of the classification processchoose a PMF (or room) that minimizes the divergence
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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
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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
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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
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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
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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
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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
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Location estimation results – (4)
• JS measure: – (2) partial dongle coverage– (3) training window
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• 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
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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]
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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
−= + −
−
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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
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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)
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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
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System overview
room fingerprint collection
radio map(room RR
fingerprints)
location fingerprintdetection
location estimation
location estimate(s)
movementdetection
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