AWS/ML初心者による、Wi-Fi AP RSSI + AWS Machine Learningを使ったIndoor Location実践の紹介。

1. Moving story of AWS/ML
beginner engineer
Fujitsu Software Technologies
Global Knowledge Center
9 Dec. 2016
根木
neki.atsushi@jp.fujitsu.com

2. • Development / Porting
• Embedded Linux Kernel
• Wi-Fi MW/Drv for Android
• SSD firmware

3. Q.
Is it possible to identify indoor
location by using Wi-Fi AP
signal strength ?

4. Yes, it is.
• Cisco Meraki
• CSI based indoor location
• CSI : Channel State Info
and so on..

5. But…
• Difficult !! ( For me !! )
• You’ll need special hardware,
skill and privilege.

6. Cutting corners !
• Using Machine Learning
• To calculate position from AP
signals
• I’ve just started this trial since
this week.

7. Scenario
606
607

8. Let’s training!

9. Training!

10. Training Data 1
mac_
001
ch_0
01
rssi_
001
ssid_
001
mac_
002
ch_0
02
rssi_
002
ssid_
002
mac_
003
ch_0
03
rssi_
003
ssid_
003
mac_
004
ch_0
04
rssi_
004
ssid_
004
mac_
005
ch_0
05
rssi_
005
ssid_
005
mac_
006
ch_0
06
rssi_
006
ssid_
006
mac_
007
ch_0
07
rssi_
007
ssid_
007
mac_
008
ch_0
08
rssi_
008
ssid_
008
mac_
009
ch_0
09
rssi_
009
ssid_
009
mac_
010
ch_0
10
rssi_
010
ssid_
010
floor
84:8
E:DF:
ED:1
B:B1
11 -19 nekia
p
00:23
:26:7
3:58:
29
6 -58 FST
-
WIFI
-
NET
WOR
K
00:23
:26:7
3:58:
2E
6 -59 00:23
:26:7
3:58:
28
6 -60 00:23
:26:7
3:58:
2F
6 -61 58:7
F:66:
0A:4
6:2E
3 -65 W01_
587F
660A
462E
E0:1
8:77:
70:6
E:50
9 -65 fstso
umu
00:23
:26:7
7:F9:
29
1 -67 FST
-
WIFI
-
NET
WOR
K
00:23
:26:7
7:F9:
2F
1 -68 00:23
:26:7
7:F9:
2E
1 -68 601
84:8
E:DF:
ED:1
B:B1
11 -19 nekia
p
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 601
84:8
E:DF:
ED:1
B:B1
11 -19 nekia
p
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 602
84:8
E:DF:
ED:1
B:B1
11 -19 nekia
p
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 602
84:8
E:DF:
ED:1
B:B1
11 -19 nekia
p
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 603
84:8
E:DF:
ED:1
B:B1
11 -19 nekia
p
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 603
84:8
E:DF:
ED:1
B:B1
11 -19 nekia
p
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 603
84:8
E:DF:
ED:1
B:B1
11 -25 nekia
p
00:23
:26:7
3:58:
2F
6 -55 00:23
:26:7
3:58:
2E
6 -57 00:23
:26:7
3:58:
29
6 -58 FST
-
WIFI
-
NET
WOR
K
E0:1
8:77:
70:6
E:50
9 -63 fstso
umu
00:23
:26:7
3:58:
28
6 -63 58:7
F:66:
2A:1
4:7B
11 -63 W01_
587F
662A
147B
00:23
:26:7
7:F9:
29
1 -66 FST
-
WIFI
-
NET
WOR
K
58:7
F:66:
0A:4
6:2E
3 -66 W01_
587F
660A
462E
DC:F
B:02:
DD:A
7:E0
10 -72 WTH
DIYK
ETB
KM-
24G
604
• Just serializing the following:
• 10 Wi-Fi AP Info
• floor index <-- this is target !

11. Result 1

12. Training Data 2
• Gathered more training data.
• about 10 times more

13. Training Data 2
• Gathered more training data.
Training 1

14. Result 2

15. Training Data 3
• Changed data structure.
84:8E:DF:ED:1B:B1 FA:8F:CA:32:CF:88 00:23:26:73:58:29 00:23:26:77:F9:29 00:23:26:77:F9:28 00:23:26:77:F9:2F 00:23:26:73:58:2E 00:23:26:73:58:28 00:23:26:73:58:2F floor
-19 -44 -51 -66 -63 -65 -61 -59 -60 601
-19 -46 -49 -61 -255 ・・・ -255 -61 -59 -60 601
-19 -43 -50 -66 -255 -255 -51 -50 -60 601
-18 -47 -47 -62 -255 -255 -60 -60 -59 601
1 data consist from about 130 AP RSSI + floor

16. Result 3
Result 2

17. Further research
• Launch an application to track location.
• Add other sensor infos as training data
• BLE beacon
• Geomagnetic field sensor

18. It’s extremely EASY!!
Let’s dive in !
• You can just start it without any special knowhow.
• You don’t need much money (it takes only $5, so far).
• You don’t need any special machine.

20. Appendix:
Result Score

21. Result 1

22. Result 2

23. Result 3