Enhanced wireless indoor tracking system in multi-floor buildings with location prediction

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1 Enhanced wireless indoor tracking system in multi-floor buildings with location prediction Rui Zhou University of Freiburg, Germany June 29, 2006 Conference, Tartu, Estonia

2 Content Location based services System overview Tracking algorithm Improvements for multi-floor situation Location prediction for moving devices Privacy and deployment 2

3 Content Location based services System overview Tracking algorithm Improvements for multi-floor situation Location prediction for moving devices Privacy and deployment 3

4 Location based services -- services provided to the subscribers based on their locations Garden Supermarket Shopping mall Conference Exhibition Museum Library 4

5 Location determination technologies GPS: outdoors RFID: RFID tags and RFID reader Barcode: barcode tags and scanner Bluetooth Ultrasound. WiFi positioning Indoor positioning Make use of existing WLAN infrastructure No extra hardware No extra cost Signal strength footprint: Signal strengths from multiple access points 5

6 WiFi Positioning Place APs SS footprint at the location of mobile device collected Collect SS footprint at calibration locations Tracking algorithm calibration Setup radio map Database: radio map Find the closest match tracking Estimated location 6

7 Content Location based services System overview Tracking algorithm Improvements for multi-floor situation Location prediction for moving devices Privacy and deployment 7

8 System overview Pure software WLAN, no extra hardware and cost Client-server mode Server: Running tracking algorithm Client: Installed on mobile device Builder: Build radio map via calibration Radio map: Database storing signal strength footprint (i.e. signal strengths from multiple access points) 8

9 Retrieve signal strength Wireless Research API (WRAPI) from University of California Windows XP NDISUIO (Network Driver Interface Specification User Mode I/O) Independent of devices Create handle to NDISUIO driver Close the handle of NDISUIO driver Bind the handle to NDISUIO driver Get the list of network devices Get the list of access points and their signal strength Open the wireless adapter Scan the list of access points 9

10 Testbed 3 floors of Computing center of University of Freiburg Black dots: calibrated locations Lines connecting dots: frequent paths 9 access points, marked on the map (a) First floor (b) Ground floor (c) Basement 10

11 Content Location based services System overview Tracking algorithm Improvements for multi-floor situation Location prediction for moving devices Privacy and deployment 11

12 Deterministic tracking algorithm: nearest neighbor RADAR system, Microsoft research In radio map: Signal strength footprint (i.e. signal strengths from multiple access points) of every calibrated location Signal strength footprint: mean value of multiple samples of signal strength footprint Euclidean distance (Sm, Si)= (SSm,0 SSi,0)2 + (SSm,1 SSi,1)2 + + (SSm,n-1 SSi,n-1)2 Minimal Euclidean distance closest match / nearest neighbor nearest neighbor radio map current signal strength footprint 12

13 Probabilistic tracking algorithm: Bayesian law In radio map: signal strength probability distribution of every calibrated location m : number of calibrated locations Li : calibrated location i Ex = {ex,0, ex,1,, ex,n-1} current signal strength footprint. Lx : the estimated location Maximal probability estimated location 13

14 History-based tracking algorithm: (chosen!) Proved more accurate and stable Uses a series of temporal consecutive signal strength footprint For every signal strength footprint, determine its k most likely estimations of the unknown location, called k nearest neighbors According to Euclidean distance -> deterministic history based algorithm According to Bayesian law -> probabilistic history based algorithm h-depth history vector of k nearest neighbors will be formed Calculate the shortest path, the end node is the currently estimated location oldest k nearest neighbors of unknown location newest s0 s1 sh-1 sh h-depth history vector Estimated location 14

15 Experiments: Deterministic history based tracking algorithm outperforms nearest neighbor algorithm algorithm nearest neighbor historybased better average accuracy 2.65 m 2.27 m 14.3% wrong room 6.6% 2.0% 69.7% wrong floor 2.6% 1.3% 50% 15

16 Content Location based services System overview Tracking algorithm Improvements for multi-floor situation Location prediction for moving devices Privacy and deployment 16

17 Improvements for multi-floor situation Layout of the building Multi-floor handling Adjustment of unreasonable location estimation 17

18 Layout of the building Logical errors: wrong floor, wrong room, wrong side of a wall, are big errors In order to improve accuracy and reduce logical errors Express the layout of the building by a weighted connected graph: (Left table) Directly connected vertexes are directly adjacent calibrated locations. Edge/weight is their distance. Shortest path distance between any two calibrated locations are calculated by Dijkstra s shortest path algorithm. (Right table) Adjacent locations in same room or on same frequent path are assigned shorter distance. Shortest path distance is used in history-based tracking algorithm shortest path distance s0 s1 sh-1 sh h-depth history vector 18

19 Multi-floor handling Trajectory (shortest path) returned by history-based tracking algorithm (assume length is h): x0, y0 floor0 x1, y1 floor1 x2, y2 floor2 xh-2,yh-2 floorh-2 xh-1, yh-1 floorh-1 But sometimes:! ( floor0 == floor1 == floor2 == == floorh-2 == floorh-1 ) Solution: 2. Find the floor which occurs most in the trajectory, let it be floorx. 3. Loop i from 0 to h-1 if (floori == floorx) { keep the location; } else { remove the location; } 4. The handled trajectory is regarded as the final trajectory. 5. End node on the handled trajectory is regarded as currently estimated location 19

20 Adjustment of unreasonable location estimation If path distance between current estimated location and last estimated location > predefined threshold current estimated location is unreasonable estimated location Need to adjust the current estimated location First find the directly adjacent locations of the last estimated location The one which is in the direction of the current estimated location is chosen as the final current estimated location. final current location adjacent locations unreasonable estimation > threshold last estimated location current estimated location 20

21 Experiments: History based tracking algorithm with improvements for multi-floor situation is more accurate than pure history based tracking algorithm More stable! algorithm (deterministic history based) without layout and multi-floor with layout and multi-floor better average accuracy 2.27 m 2.08 m 8.4% wrong room 2.0% 1.3% 35% wrong floor 1.3% 0.65% 50% 21

22 Content Location based services System overview Tracking algorithm Improvements for multi-floor situation Location prediction for moving devices Privacy and deployment 22

23 Problems of moving devices When the user walks with several seconds stop, average accuracy is about 2 meters. When the user walks continuously without stop, change of the detected signal strength values and the calculation of location cannot keep up with the movement of the user, due to hardware and algorithm limitations. It may take a few more seconds to come to the new estimation, which results in inaccuracy during the lag. More quickly the user walks, more inaccurate the system will become. Deterministic history-based tracking algorithm Probabilistic history-based tracking algorithm 23

24 Location prediction If the system detects the change of the signal strength, the current location will be calculated / estimated using the tracking algorithm as normal Else, (i.e., if the system does not detect the change of the signal strength), the current location will be predicted. Estimated locations Predicted locations The final trajectory of the mobile device 24

25 Prediction algorithm n is the number of history locations on which the prediction is based Case 1: If the user does not change heading during this period velocity = (path distance between 1 and n) / (time span between 1 and n) predicted location = last location +/- velocity*(time span between n and currently predicted location) n-1 n Predicted location Case 2: If the user changes heading last at location m. velocity = (path distance between m and n) / (time span between m and n) predicted location = last location +/- velocity*(time span between n and currently predicted location) 1 2 m n-1 n Predicted location 25

26 Experiments: Accuracy for moving devices, with location prediction algorithm without prediction with prediction better average accuracy 4.16 m 3.67 m 11.8% 26

27 Content Location based services System overview Tracking algorithm Improvements for multi-floor situation Location prediction for moving devices Privacy and deployment 27

28 Privacy issues Knowledge of person s location enables reasoning about what he/she is doing and his/her interests. Few people like their locations known in all situations and privacies disclosed. Our tracking system is client-based. The user decides whether to be tracked and when. The user decides whether to disclose his/her location to others. User s location information is not stored on the server. Communication between server and clients are protected by VPN. 28

29 Deployment Placement of access points Calibration Apply constrains Tracking Place APs on all tracked floors for tracking and communication Each location should be able to receive signals from at least 3 APs 5 to 7 APs is optimal Collect signal strength footprints at calibration locations Distance between two adjacent calibration locations: 1 to 2 meters 200 to 300 samples for every calibration location Collect signal strength in all orientations Give denser calibration locations and more samples in areas which may be confused with other areas. Create radio map based on raw calibration data Apply the layout of the building Apply other available constrains Walk slowly or even stop for seconds for better accuracy 29

30 Discussion History-based v. non-history-based algorithm History-based algorithm is more accurate and stable Just because of history information, it needs more time for new correct estimation Non-history based algorithm is more prompt, although less accurate and less stable Conditions for location prediction User walks continuously on paths User does not change the heading often Orientation Received signal strengths differ in different orientations Integrating orientation would improve accuracy Getting orientation automatically is non-trivial Different wireless adapters Received signal strengths differ slightly with different wireless adapters Correlate different wireless adapters 30

31 Conclusions WLAN-based real-time indoor tracking system in multi-floor buildings Interior structure, multi-floor handling, location prediction are applied to improve accuracy and stability In experiments, the average accuracy for temporary static devices is around 2 meters, for slowly moving devices it is 3.7 meters. The system can determine the correct floor with the percentage of 99%. 31

32 References 1. W. Abdelsalam, Y. Ebrahim, S. Chau, M. Ahmed. A user modeling approach to improving estimation accuracy in location-tracking applications, The 3rd ACS/IEEE International Conference on Computer Systems and Applications. (2005). 2. P. Bahl, V.N. Padmanabhan. RADAR: An In-Building RF-Based Location and Tracking Systems, in IEEE INFOCOM Tel-Aviv, Israel. (2000). 3. P. Bahl, V.N. Padmanabhan, A. Balachandran. Enhancements to the RADAR User Location and Tracking System, Microsoft Research: Redmond, WA. (2000). 4. K. Kaemarungsi. Design of Indoor Positioning Systems Based on Location Fingerprinting Technique, Doctoral dissertation, University of Pittsburgh. (2005). 5. J. Krumm, E. Horvitz. LOCADIO: Inferring Motion and Location from Wi-Fi Signal Strengths, Proceedings of Mobiquitous 2004, First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, pp. 4-14, Boston, MA, USA. August (2004). 6. A. M. Ladd, K. E. Bekris, et al. Robotics-based Location Sensing using Wireless Ethernet, in 8th International Conference on Mobile Computing and Networking. Atlanta, GA, USA. (2002). 7. M. Youssef, A. Agrawal, A. U. Shankar. WLAN location determination via clustering and probability distributions, in Proceedings of IEEE PerCom 03, Fort Worth, TX, Mar. (2003). 8.. Wireless Indoor Tracking System (WITS), Aktuelle Trends in der Softwareforschung, Tagungsband zum doit Software-Forschungtag. dpunkt.verlag Heidelberg, Germany. To appear. (2006). 32

33 Thanks for your attention! 33

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