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Location in Pervasive Computing Shwetak N. Patel University of Washington More info: Special thanks to Alex Varshavsky and Gaetano Borriello.

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Presentation on theme: "Location in Pervasive Computing Shwetak N. Patel University of Washington More info: Special thanks to Alex Varshavsky and Gaetano Borriello."— Presentation transcript:

1 Location in Pervasive Computing Shwetak N. Patel University of Washington More info: Special thanks to Alex Varshavsky and Gaetano Borriello for their contribution to this content design: use: build: ubicomp lab university of washington Computer Science & Engineering Electrical Engineering

2 2 Location A form of contextual information Persons physical position Location of a device Device is a proxy of a persons location Used to help derive activity information

3 3 Location Well studied topic (3,000+ PhD theses??) Application dependent Research areas Technology Algorithms and data analysis Visualization Evaluation

4 4 Location Tracking

5 5 Representing Location Information Absolute Geographic coordinates (Lat: , Long: ) Relative 1 block north of the main building Symbolic High-level description Home, bedroom, work

6 6 No one size fits all! Accurate Low-cost Easy-to-deploy Ubiquitous Application needs determine technology

7 7 Consider for example… Motion capture Car navigation system Finding a lost object Weather information Printing a document

8 Others aspects of location information Indoor vs. outdoor Absolute vs. relative Representation of uncertainty Privacy model 8

9 Lots of technologies! 9 Ultrasonic time of flight E-911 Stereo camera Ad hoc signal strength GPS Physical contact WiFi Beacons Infrared proximity Laser range-finding VHF Omni Ranging Array microphone Floor pressure Ultrasound

10 10 Some outdoor applications Car Navigation Child tracking Bus view E-911

11 11 Some indoor applications Elder care

12 Outline Defining location Methods for determining location Ex. Triangulation, trilateration, etc. Systems Challenges and Design Decisions Considerations

13 13 Approaches for determining location Localization algorithms Proximity Lateration Hyperbolic Lateration Angulation Fingerprinting Distance estimates Time of Flight Signal Strength Attenuation

14 14 Proximity Simplest positioning technique Closeness to a reference point Based on loudness, physical contact, etc

15 15 Lateration Measure distance between device and reference points 3 reference points needed for 2D and 4 for 3D

16 16 Hyperbolic Lateration Time difference of arrival (TDOA) Signal restricted to a hyperbola

17 17 Angulation Angle of the signals Directional antennas are usually needed

18 18 Determining Distance Time of flight Speed of light or sound Signal strength Known drop off characteristics 1/r^2-1/r^6 Problems: Multipath

19 19 Fingerprinting Mapping solution Address problems with multipath Better than modeling complex RF propagation pattern

20 20 Fingerprinting SSID (Name)BSSID (MAC address)Signal Strength (RSSI) linksys00:0F:66:2A:61:0018 starbucks00:0F:C8:00:15:1315 newark wifi00:06:25:98:7A:0C23

21 21 Fingerprinting Easier than modeling Requires a dense site survey Usually better for symbolic localization Spatial differentiability Temporal stability

22 22 Reporting Error Precision vs. Accuracy

23 23 Reporting Error Cumulative distribution function (CDF) Absolute location tracking systems Accuracy value and/or confusion matrix Symbolic systems

24 24 Location Systems Distinguished by their underlying signaling system IR, RF, Ultrasonic, Vision, Audio, etc

25 25 GPS Use 24 satellites TDOA Hyperbolic lateration Civilian GPS L1 (1575 MHZ) 10 meter acc.

26 26 Active Badge IR-based Proximity

27 27 Active Bat Ultrasonic Time of flight of ultrasonic pings 3cm resolution

28 28 Cricket Similar to Active Bat Decentralized compared to Active Bat

29 29 Cricket vs Active Bat Privacy preserving Scaling Client costs Active Bat Cricket

30 30 Ubisense Ultra-wideband (UWB) 6-8 GHz Time difference of arrival (TDOA) and Angle of arrival (AOA) cm

31 31 RADAR WiFi-based localization Reduce need for new infrastructure Fingerprinting

32 32 Place Lab Beacons in the wild WiFi, Bluetooth, GSM, etc Community authored databases API for a variety of platforms RightSPOT (MSR) – FM towers

33 33 ROSUM Digital TV signals Much stronger signals, well-placed cell towers, coverage over large range Requires TV signal receiver in each device Trilateration, 10-20m (worse where there are fewer transmitters)

34 34 Comparing Approaches Many types of solutions (both research and commercial) Install custom beacons in the environment Ultra-wideband (Ubisense), Ultrasonic (MIT Cricket, Active Bat), Bluetooth Use existing infrastructure GSM (Intel, Toronto), WiFi (RADAR, Ekahau, Place Lab), FM (MSR)

35 35 Limitations Beacon-based solutions Requires the deployment of many devices (typically at least one per room) Maintenance Using existing infrastructure WiFi and GSM Not always dense near some residential areas Little control over infrastructure (especially GSM)

36 36 Beacon-based localization

37 37 Wifi localization (ex. Ekahau)

38 38 GSM localization Tower IDs and signals change over time! Coverage?

39 39 PowerLine Positioning Indoor localization using standard household power lines

40 40 Signal Detection A tag detects these signals radiating from the electrical wiring at a given location

41 41 Signal Map 1 st Floor 2 nd Floor

42 42 Example

43 43 Passive location tracking No need to carry a tag or device Hard to determine the identity of the person Requires more infrastructure (potentially)

44 44 Active Floor Instrument floor with load sensors Footsteps and gait detection

45 45 Motion Detectors Low-cost Low-resolution

46 46 Computer Vision Leverage existing infrastructure Requires significant communication and computational resources CCTV

47 47 Other systems? Inertial sensing HVACs Ambient RF etc.

48 Considerations Location type Resolution/Accuracy Infrastructure requirements Data storage (local or central) System type (active, passive) Signaling system 48

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