[CONTEXTS'11] A bayesian strategy to enhance the performance of indoor localization systems

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[CONTEXTS'11] A bayesian strategy to enhance the performance of indoor localization systems

  1. 1. Grupo de Procesado de Datos y Simulación ETSI de Telecomunicación Universidad Politécnica de Madrid A Bayesian strategy to enhance theperformance of indoor localization systems CONTEXTS 2011 Josué Iglesias, Ana M. Bernardos, José R. Casar abernardos@grpss.ssr.upm.es
  2. 2. contents  introduction  application scenario (sensor models)  Bayesian enhancement strategy  simulation results  discussion and future worksUser-Centric Technologies and Applications – CONTEXTS abernardos@grpss.ssr.upm.es 2 / 14
  3. 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.)User-Centric Technologies and Applications – CONTEXTS abernardos@grpss.ssr.upm.es 3 / 14
  4. 4. contents ✓  introduction  application scenario (sensor models)  Bayesian enhancement strategy  simulation results  discussion and future worksUser-Centric Technologies and Applications – CONTEXTS abernardos@grpss.ssr.upm.es 4 / 14
  5. 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 trayexisting 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) User-Centric Technologies and Applications – CONTEXTS abernardos@grpss.ssr.upm.es 5 / 14
  6. 6. sensor models • passive RFIDproximity 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]User-Centric Technologies and Applications – CONTEXTS abernardos@grpss.ssr.upm.es 6 / 14
  7. 7. contents ✓  introduction ✓  application scenario (sensor models)  Bayesian enhancement strategy  simulation results  discussion and future worksUser-Centric Technologies and Applications – CONTEXTS abernardos@grpss.ssr.upm.es 7 / 14
  8. 8. Bayesian strategyDinamic Bayesian Network real user location hidden states sensor observations transition location proximity sensors state system state sensors staterecursive Bayesian filter • temporal hidden states transitions  Markovian evolution • sensor observation independent (according the DBN graph)User-Centric Technologies and Applications – CONTEXTS abernardos@grpss.ssr.upm.es 8 / 14
  9. 9. " 7(" -" / . # # " (&#&# M " *-A # (" , -. , 78) "/ &, 6 +. 2 . = # " +) % " $, & # &# # Bayesian strategy ! ! ! !! ! ! ! ! ! ! ! ! ! ! !!!!! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! !! !! " -" / . # &% " (" # " = 1- 2 8) # transition model !"!#$ %! ! ! ! ! & # modelado de la calidad de los sensores modelo de transición obtenido al calcular *(excepto para los de transición) [between zones] subyacente # # ! ! ! ! ! ! ! "#!! " # ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! # ! !! # # ! !" #$ %! ! ! ! ! & 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)]1" +(. *# " # "*) % . # &% " %/ " . 2 - &/ . , # " # *(&# +) &+-A # B, &7 $) " *# *# 2 Z # &# &% -" , # . / , (&/ 1- 7 ## / " " *. # " +. $&, +) 8) E&% "# - ) " % *" " / -+" # C# . # 1% C" # #&# %++-A # " # >M . 4 / 2 -2 ## 2 B&,0. 7$) " / " # , # # & (" " , % # &# -/ &, # " #&# (-/ &/ # " # # *(># &M -3& / . # # &# " # *" Z(-%2 . $&, E&% 8) % +& 8) *" " 2 -2 " +&/ 2 9 / 14&# . B&B-% &/ # Technologies and Applications$%# " # *(># (, " #. *# . , " *# C# 4 $, User-Centric 8) " # 1" +(-E&2 " (" # 2 – " 8) " -/ C# " +) CONTEXTS " abernardos@grpss.ssr.upm.es % E&% a# L #
  10. 10. contents ✓  introduction ✓  application scenario (sensor models) ✓  Bayesian enhancement strategy  simulation results  discussion and future worksUser-Centric Technologies and Applications – CONTEXTS abernardos@grpss.ssr.upm.es 10 / 14
  11. 11. simulation results 5.2 Evaluation of the Bayesian location improvement algorithm in a real scenariosimulating 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 User-Centric Technologies and Applications – CONTEXTS abernardos@grpss.ssr.upm.es 11 / 14
  12. 12. contents ✓  introduction ✓  application scenario (sensor models) ✓  Bayesian enhancement strategy ✓  simulation results  discussion and future worksUser-Centric Technologies and Applications – CONTEXTS abernardos@grpss.ssr.upm.es 12 / 14
  13. 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?User-Centric Technologies and Applications – CONTEXTS abernardos@grpss.ssr.upm.es 13 / 14
  14. 14. any question?User-Centric Technologies and Applications – CONTEXTS abernardos@grpss.ssr.upm.es 14 / 14

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