The Horus WLAN Location Determination System

Transcription

1 The orus WLAN Location Determination ystem Moustafa Youssef and Ashok Agrawala niversity of Maryland, College Park

2 Location Determination Technologies GP Cellular-based ltrasonic-based: Active Bat Infrared-based: Active Badge Computer vision: Easy Living Physical proximity: mart Floor Not suitable for indoor Does not work equire specialized hardware calability orus 2005, Moustafa Youssef 2

3 WLAN Location Determination Triangulate user location eference point Quantity proportional to distance WLAN Access points ignal strength= f(distance) oftware based orus 2005, Moustafa Youssef 3

4 oadmap Motivation Location determination technologies Introduction Noisy wireless channel orus components Performance evaluation Conclusions and future work orus 2005, Moustafa Youssef 4

5 WLAN Location Determination (Cont d) ignal strength= f(distance) Does not follow free space loss se lookup table adio map adio Map: signal strength characteristics at selected locations orus 2005, Moustafa Youssef 5

6 WLAN Location Determination Taxonomy WLAN Location Determination ystems Ad-hoc Mode Infrastructure Mode [Lundberg02] Cell of rigin ignal trength Time of Arrival Daedalus Model-based adio-map Based PinPoint Classification Wheremops Deterministic Probabilistic Example adar orus orus 2005, Moustafa Youssef 6

7 WLAN Location Determination (Cont d) [-50, -60] 5 [-53, -56] 13 ffline phase Build radio map adar system: average signal strength nline phase Get user location Nearest location in signal strength space (Euclidian distance) [-58, -68] orus 2005, Moustafa Youssef 7

8 orus Goals igh accuracy Wider range of applications Energy efficiency Energy constrained devices calability Number of supported users Coverage area orus 2005, Moustafa Youssef 8

9 oadio-map Motivation Location determination technologies Introduction Noisy wireless channel orus components Performance evaluation Conclusions orus 2005, Moustafa Youssef 9

10 ampling Process Active scanning end a probe request eceive a probe response 2n-1. Probe equest 2n. Probe esponse Channel n 3. Probe equest... Channel 2 4. Probe esponse 1. Probe equest 2. Probe esponse Channel 1 orus 2005, Moustafa Youssef 10

11 ignal trength Characteristics Temporal variations ne access point Multiple access points patial variations Large scale mall scale orus 2005, Moustafa Youssef 11

12 Temporal Variations: ne Access Point Environment changes sing average only leads to loss of information orus 2005, Moustafa Youssef 12

13 Number of amples Collected Temporal Variations: Multiple Access Points 300 eceiver ensitivity Average ignal trength (dbm) 0 Number of access points changes over time Choose the strongest access points orus 2005, Moustafa Youssef 13

14 Temporal Variations: Correlation Independence assumption is wrong orus 2005, Moustafa Youssef 14

15 ignal trength (dbm) patial Variations: Large-cale Distance (feet) Desirable! orus 2005, Moustafa Youssef 15

16 patial Variations: mall-cale Multipath effect orus 2005, Moustafa Youssef 16

17 oadio-map Motivation Goals Introduction Noisy wireless channel orus components Performance evaluation Conclusions orus 2005, Moustafa Youssef 17

18 orus Components Basic algorithm Correlation handler Continuous space estimator mall-scale compensator Locations clustering orus 2005, Moustafa Youssef 18

19 Basic Algorithm ffline phase adio map: signal strength histograms nline phase Bayesian based inference orus 2005, Moustafa Youssef 19

20 Basic Algorithm: Example (x, y) (x i, y i ) P(-53/L1)=0.55 [-53] P(-53/L2)= orus 2005, Moustafa Youssef 20

21 sing Multiple amples Need to average multiple samples to increase accuracy Independence assumption is wrong orus 2005, Moustafa Youssef 21

22 Correlation andler Autoregressive model Estimate correlation degree Estimate distribution of the average of n correlated samples orus 2005, Moustafa Youssef 22

23 Var(A)/Var(s) Correlation andler: Var(A)/Var(s) Independence assumption underestimates true variance a orus 2005, Moustafa Youssef 23

24 Continuous pace Estimator Enhance the discrete radio map space estimator Two techniques Center of mass of the top ranked locations Time averaging window orus 2005, Moustafa Youssef 24

25 mall-scale Compensator Perturbation Technique Detect small-scale variations sing previous user location Perturb signal strength vector (s 1, s 2,, s n ) (s 1 d 1, s 2 d 2,, s n d n ) Typically, n=3-4 is chosen relative to the received signal strength d i orus 2005, Moustafa Youssef 25

26 Number of amples Collected Locations Clustering educe computational requirements se access points that cover each location se the q strongest access points 300 eceiver ensitivity Average ignal trength (dbm) orus 2005, Moustafa Youssef 26

27 orus Components Continuous-pace orus ystem Components Correlation Modeler adio Map Builder adio Map and clusters Clustering Applications Location API Estimator mall-cale Compensator Discrete-pace Estimator Correlation andler Estimated Location (-50,-67,-80) ignal trength Acquisition API Device Driver (-45,-63,-63) (MAC, ignal trength) orus 2005, Moustafa Youssef 27

28 oadio-map Motivation Location Determination technologies Introduction Noisy wireless channel orus components Performance evaluation Conclusions and future work orus 2005, Moustafa Youssef 28

29 Testbeds A.V. William s 4 th floor, AVW 224 feet by 85.1 feet MD net (Cisco APs) 21 APs (6 on avg.) 172 locations 5 feet apart Windows XP Prof. FLA rinoco/compaq cards orus 2005, Moustafa Youssef 3rd floor, 8400 Baltimore Ave 39 feet by 118 feet Linkys/Cisco APs 6 APs (4 on avg.) 110 locations 7 feet apart Linux (kernel 2.5.7) 29

30 Avg. Num. of per. per Loc. Est. orus-adar Comparison orus adar Median Avg tdev Max orus (all components) orus (basic) adar orus 2005, Moustafa Youssef 30

31 adar with orus Techniques Average distance error enhanced by more than 58% Worst case error decreased by more than 76% orus 2005, Moustafa Youssef 31

32 oadio-map Motivation Location Determination technologies Introduction Noisy wireless channel orus components Performance evaluation Conclusions orus 2005, Moustafa Youssef 32

33 Conclusions The orus system achieves its goals igh accuracy through different modules Low computational requirements through the use of clustering techniques calability in terms of the coverage area through the use of clustering techniques calability in terms of the number of users through the distributed implementation Modules can be applied to other WLAN location determination systems orus 2005, Moustafa Youssef 33

34 ther orus elated Invention of the year award (MD 2004) 3 Patents pending Licensed by Fujitsu Cited in New York Times Washington Times oftware Drivers: mwvlan, mwavelan, morinoco MAPI orus 2005, Moustafa Youssef 34

35 For More Information verall system [Mobiys05] Basic algorithm [Percom03] Locations clustering [Percom03] mall-scale compensator [WCNC03] ptimality Analysis [CND04] Correlation handler [InfoCom04] Continuous space estimator [ICCCN04] ser profile [IJM05] Drivers/API s orus 2005, Moustafa Youssef 35

36 Thank You!? orus 2005, Moustafa Youssef 36