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2
Location
• Source of wireless signals–Wireless emitter
• Location of a mobile device– Some devices, e.g., cell phones, are a proxy of a
person’s location
• Used to help derive the context and activity information– Location based services
– Privacy problems
3
Location
• Well studied topic (3,000+ PhD theses??)
• Application dependent
• Research areas– Technology
– Algorithms and data analysis
– Visualization
– Evaluation
4
Representing Location Information
• Absolute– Geographic coordinates (Lat: 33.98333, Long: -86.22444)
• Relative– 1 block north of the main building
• Symbolic– High-level description
– Home, bedroom, work
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No one size fits all!
• Accurate
• Low-cost
• Easy-to-deploy
• Ubiquitous
• Application needs determine technology
Lots of technologies!
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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 pressureUltrasound
Wireless Technologies for Localization
Name Effective Range Pros Cons
GSM 35km Long range Very low accuracy
LTE 30km-100km
Wi-Fi 50m-100m Readily available; Medium range
Low accuracy
Ultra Wideband 70m High accuracy High cost
Bluetooth 10m Readily Available; Medium accuracy
Short range
Ultrasound 6-9m High accuracy High cost, not scalable
RFID & IR 1m Moderate to high accuracy
Short range, Line-Of-Sight (LOS)
NFC <4cm High accuracy Very short range
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Range Based Algorithms
• Rely on the distance (angle) measurement between nodes to estimate the target location
• Approaches– Proximity
– Lateration
– Hyperbolic Lateration
– Angulation
• Distance estimates– Time of Flight
– Signal Strength Attenuation
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Approach: Proximity
• Simplest positioning technique
• Closeness to a reference point
• Based on loudness, physical contact, etc
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Approach: Lateration
• Measure distance between device and reference points
• 3 reference points needed for 2D and 4 for 3D
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Approach: Hyperbolic Lateration
• Time difference of arrival (TDOA)
• Signal restricted to a hyperbola
14
Distance Estimation
• Multiple the radio signal velocity and the travel time– Time of arrival (TOA)
– Time difference of arrival (TDOA)
• Compute the attenuation of the emitted signal strength– RSSI
• Problem: Multipath fading
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Distance Estimation: TOA
• Distance– Based on one signal’s travelling time from target
to measuring unit
– d = vradio * tradio
• Requirement– Transmitters and receivers should be precisely
synchronized– Timestamp must be labeled in the transmitting
signal
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Distance Estimation: TDOA
• Distance– Based on time signals’ travelling time from target to
measuring unit
– d = vradio * vsound * (tradio- tsound) / (vradio – vsound))
• Requirement– Transmitters and receivers should be precisely
synchronized
– Timestamp must be labeled in the transmitting signal
– Line-Of-Sight (LOS) channel
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Distance Estimation: RSSI
• Distance– Based on radio propagation model
–
• Requirement– Path loss exponent η for a given environment is
known
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Range Free Algorithms
• Rely on target object’s proximity to anchor beacons with known positions– Neighborhood: single/multiple closest BS
– Hop-count: anchor broadcast beacons containing its location and hop-count
– Area estimation:
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Fingerprinting
• Mapping solution
• Address problems with multipath
• Better than modeling complex RF propagation pattern
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Fingerprinting: Steps
• Step1– Use war-driving to build up location fingerprints (i.e.
location coordinates + respective RSSI from nearby base stations)
• Step2– Match online measurements with the closest a priori
location fingerprints
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Fingerprinting: Example
SSID (Name) BSSID (MAC address) Signal Strength (RSSI)
linksys 00:0F:66:2A:61:00 18
starbucks 00:0F:C8:00:15:13 15
newark wifi 00:06:25:98:7A:0C 23
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Fingerprinting: Features
• Easier than modeling
• Requires a dense site survey
• Usually better for symbolic localization
• Spatial differentiability
• Temporal stability
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Summary of Localization Techniques
Measurement Scheme
Accuracy Special Requirement
Range-based TOA Moderate Synchronization, dense beacons
TDOA High Synchronization, LOS, dense beacons
AOA High Directional antenna
RSSI Moderate No
Range-free Neighborhood Low No
Area estimation Moderate Dense Beacons
Hop count Moderate Dense Beacons
Fingerprinting RSSI High No
25
Localization Systems
• Distinguished by their underlying signaling system– IR, RF, Ultrasonic, Vision, Audio, etc [13]
26
GPS
• Use 24 satellites
• TDOA
• Hyperbolic lateration
• Civilian GPS– L1 (1575 MHZ)• 10 meter acc.
27
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Place Lab• “Beacons in the wild”–WiFi, Bluetooth, GSM, etc
• Community authored databases
• API for a variety of platforms
• RightSPOT (MSR) – FM towers
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Computer Vision• Leverage existing infrastructure
• Requires significant communication and computational resources
• CCTV
Performance Metrics
• Accuracy – Mean distance error (RMSE)
• Precision– Variation in accuracy over many trials (CDF of RMSE)
• Robustness– Performance when signals are incomplete
• Cost– Hardware, energy
34
Performance Evaluation
System/Solution
Wireless Technologies
Accuracy Precision Robustness Cost
Active Badge [1]
IR 3cm 90% Poor Low
Cricket[2] Ultrasound 5cm 90% Poor Medium
BeepBeep [3] Sound 4cm 95% Poor High
Virtual Compass [4]
Bluetooth+ WiFi RSSI
3.19m 90% Good Medium
APIT [5] WiFi RSSI 0.4 * radio range
Medium Low
DV-Hop [6] WiFi RSSI 3.5m 90% Medium Low
35
Performance Evaluation
System/Solution
Wireless Technologies
Accuracy Precision Robustness Cost
Centroid [7] WiFi RSSI 3.5m 90% Good Low
Amorphous [8] WiFi RSSI 0.2* radio range
Medium Low
RADAR [9] WiFi RSSI 5.9m 95% Good Low
Horus [10] Bluetooth+ WiFi RSSI
2.1m 90% Good Low
SurroudSense [11]
WiFi RSSI 90% N/A Good High
Ekahau [12] WiFi RSSI 2m 50% Good Low
36
E-V Loc: Goal
• Find a specific person’s accurate location based on his electronic identifier and visual image
- Publication:Boying Zhang, Jin Teng, Junda Zhu, Xinfeng Li, Dong Xuan, and Yuan F. Zheng, EV-Loc: Integrating
Electronic and Visual Signals for Accurate Localization, to appear in ACM MobiHoc’12.
37
E-V Loc: Problem Formulation
• Input: a target object’s electronic identifier EID*, a set (in a short time span) of E Frames with clear EIDs and the corresponding V Frames with possibly vague VIDs
• Output: the target object’s accurate position together with its visual appearance VID*
38
E-V Loc: Nature of Our Solution
• E-V matching – Uses electronic and visual signals as target object’s
location descriptors in E frames and V frames
– Matches the corresponding E and V location descriptors using Hungarian algorithm
40
42
E-V Loc: Incremental Hungarian algorithm• Find the best match between the EIDs and VIDs in
each pair of E and V frame• Iteratively perform the matching until a threshold is
satisfied• The threshold is derived based on the variance model
of EIDs and VIDs
43
E-V Loc:Localizing with Indistinct VIDs• Multi-dimensional best match problem
Between EIDs and VIDs Among VIDs
44
E-V Loc: Two-dimensional Hungarian Algorithm• Finding correspondence between different VIDs in
neighboring frames• Based on the correspondence, generating a consistent
set of VIDs in all frames• Using incremental Hungarian algorithm to perform
the match
References
1. Roy Want, Andy Hopper, Veronica Falcao, and Jonathan Gibbons. The active badge location system. ACM Transactions on Information Systems, 10(1):91–102, 1992.
2. N. B. Priyantha, A. Chakraborty, and H. Balakrishnan. The cricket locationsupportsystem. In Proc. of ACM MobiCom, pages 32–43, 2000.
3. C. Peng, G. Shen, Y. Zhang, Y. Li, and K. Tan. BeepBeep: ahigh accuracy acoustic ranging system using COTS mobiledevices. In ACM SenSys, pages 1–14, 2007.
4. N. Banerjee, S. Agarwal, P. Bahl, R. Chandra, A. Wolman, and M. Corner.Virtual compass: relative positioning to sense mobile social interactions. InPervasive, 2010.
5. T. He, C. Huang, B. Blum, J. Stankovic, and T. Abdelzaher. Range-free localizationschemes for large scale sensor networks. In Proc. of ACM MobiCom,pages 81–95, 2003.
References
6. D. Niculescu and B. Nath. DV based positioning in ad hoc networks. Journalof Telecom. Systems, 2003.
7. N. Bulusu, J. Heidemann, and D. Estrin. Gps-less low cost outdoor localizationfor very small devices. IEEE Personal Communications Magazine, 7(5):28–34,October 2000.
8. R. Nagpal. Organizing a global coordinate system from local information on anamorphous computer. In A.I. Memo 1666. MIT A.I. Laboratory, August 1999.
9. P. Bahl and V. N. Padmanabhan. RADAR: an in-building rf-based user locationand tracking system. In Proc. of IEEE INFOCOM, March 2000.
10. M. Youssef and A. Agrawala. The Horus WLAN location determination system.In Proc. of ACM MobiSys, June 2005.
11. M. Azizyan, I. Constandache, and R. Roy Choudhury. Surroundsense: mobilephone localization via ambience fingerprinting. In Proc. of ACM MobiCom,2009.
References
12. http://www.ekahau.com/
13. Shwetak N. Patel , Location in Pervasive Computing, University of Washington.