1. Grupo de Procesado de Datos y Simulación
ETSI de Telecomunicación
Universidad Politécnica de Madrid
A Bayesian strategy to enhance the
performance of indoor localization systems
CONTEXTS 2011
Josué Iglesias, Ana M. Bernardos, José R. Casar
abernardos@grpss.ssr.upm.es

2. contents
 introduction
 application scenario
(sensor models)
 Bayesian enhancement strategy
 simulation results
 discussion and future works
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3. introduction
• smart environments (AmI, context-awared, etc.)
different heterogeneous technologies:
– WSN
– RFID
data fusion techniques
– bi-dimensional codes
– etc.
location estimation enhancement
• smart environments
indoor location services
(based on infrared, ultrasounds, video, radio frequency, etc.)
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4. contents
✓
 introduction
 application scenario
(sensor models)
 Bayesian enhancement strategy
 simulation results
 discussion and future works
User-Centric Technologies and Applications – CONTEXTS abernardos@grpss.ssr.upm.es 4 / 14

5. application scenario
x area id 4 6
WSN anchor node 1 RFID tag 3 RFID tag
1 proximity mote 1 proximity mote
transition sensors 2
1 RFID tray 1 RFID tray
t46
t34
t43
0 1 3 5
user t35
+ mobile mote
1 RFID tag 1 proximity mote
+ PDA + RFIDreader
1 RFID tray
existing location system objective: Bayesian fusion strategy
•symbolic location (zone-based)• output: 2)adding new proximity detection
•NZ = 6 zones (~ rooms) o(t)=0,1,…, NZ-1 sensors
•WSN network (ZigBee) • average error = 28.79% 3)adding new transition sensors
• accuracy model: between zones
•12 anchor nodes (2 x zone)
P(o(t)|Hk(t)) 4)information about the particular
[Hk(t)  real user location] deployment (possible transitions)
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6. sensor models
• passive RFID
proximity
P(dn(t)|cn(t))
• pressure mats
sensors • power-tuned ZigBee motes [dn(t)  proximity sensor state]
• etc. [cn(t)  1 if user in sensor proximity]
transition
• pair of pressure mats P(in(t)|rpq(t))
• power-tuned ZigBee motes [in(t)  transition sensor state]
sensors
• etc.
[rpq(t)  1 if user transition exists]
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7. contents
✓
 introduction
✓
 application scenario
(sensor models)
 Bayesian enhancement strategy
 simulation results
 discussion and future works
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8. Bayesian strategy
Dinamic Bayesian Network
real user location
hidden
states
sensor
observations
transition location proximity
sensors state system state sensors state
recursive Bayesian filter
• temporal hidden states transitions  Markovian evolution
• sensor observation independent (according the DBN graph)
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9. " 7(" ' -" ' / . # ' # " ' (&#&# M " *-A' # (" , -. , 78) "/ &, 6 +. 2 . =
# " +) % " $, &' # &# #
Bayesian strategy
! ! ! !! ! ! ! ! ! ! ! ! ! ! !!!!! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! !
!! !!
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transition model
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*(excepto para los de transición) [between zones]
subyacente
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modelado de la calidad de los sensores modelo de transición obtenido al calcular
# sensors model recursive component
(slightly different for transition sensors; see paper)subyacente
*(excepto para los de transición)
[obtained when calculating Hk(t-1)]
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10. contents
✓
 introduction
✓
 application scenario
(sensor models)
✓
 Bayesian enhancement strategy
 simulation results
 discussion and future works
User-Centric Technologies and Applications – CONTEXTS abernardos@grpss.ssr.upm.es 10 / 14

11. simulation results
5.2 Evaluation of the Bayesian location improvement algorithm in a real scenario
simulating a future Figure 5 the results of a simulation employing togethermodel proximity and
Finally, in
real deployment: • proximity sensor
both
transition sensors are presented. This test has been set tocalculated for mote-based sensors):
(empirically be run using our real
•6 zone deployment: 
–
deployment configuration (Figure 1). The number of–proximity(t)=0) = 1 has been set to
11 proximity sensors P(d (t)=0|c sensors
n n
– 4 transition sensors simulation (matching the number of sensors (t)=1|c (t)=1) = 0.978926
11 for this – P(d nowadays available for our
n n
•transition model: deployment). Only 4 transition sensors • have been sensor model placed in the
real transition employed,
– equidistributed if zones communicate – ranging from 85% to 100% of hit rate
•location system quality decided to place there several transitionsimulation scenario: reduce location
so it was • sensors, trying to
(empirically calculated) = 71.21% hit rate still working in the configuration of the transition sensors,
system errors. As we are – 1.000 trajectories
– 1.000 zone transition per trajectory
the obtained improvement is shown over several transition sensors qualities.
hit rate (%):
location system + transition model
+ 11 proximity sensors
+ 4 transition sensors
location system + transition model
~ + 16 %
+ 11 proximity sensors
location system + transition model
+ 4 transition sensors
location system + transition model
location system
Fig. 5. Real deployment influence over location estimation
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12. contents
✓
 introduction
✓
 application scenario
(sensor models)
✓
 Bayesian enhancement strategy
✓
 simulation results
 discussion and future works
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13. discussion & future works
• AmI environments make use of several heterogeneous
technologies (e.g., RFID, bi-dimensional codes, etc.) that can
be seamless processed to enhance already deployed location
systems
– cheap and feasible approach
– hit rate 71.21%  ~ 88%
• consider more types of sensors (e.g., RFID, pressure mats, etc.),
empirically obtaining its probabilistic models
• perform more tests with different sensor’s placements
(analysing the enhancement introduced by each kind of sensor)
• real implementation
– design supporting infrastructure
– mobile deployment?
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14. any question?
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