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Localization in Sensor Networking. John Quintero. Application-driven, data-centric sensor networks frequently require location information tied to sensor data: Wildlife Tracking Weather Monitoring Location-based Authentication. Applications. Triangulation

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Localization in Sensor Networking


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Presentation Transcript
applications
Application-driven, data-centric sensor

networks frequently require location

information tied to sensor data:

Wildlife Tracking

Weather Monitoring

Location-based Authentication

Applications
three techniques for determining location
Triangulation

Location determined using triangle geometry.

Scene Analysis

Observed features used to infer location.

Proximity

Detection of change near known location.

Three Techniques for Determining Location
triangulation lateration
Lateration is the calculation of position information based on distance measurements.

2D position requires three distance measurements.

3D position requires four distance measurements.

Triangulation: Lateration

d

d

d

triangulation lateration1
Measuring Distance

Direct measurement, eg: tape measure. Difficult to automate.

Time of flight measurement. Sound = 344 m/sec. Radio = 3 * 109 m/sec.

Challenges: multipath interference, clock synchronization. GPS atomic clocks synchronized to 10-13 seconds.

Triangulation: Lateration
triangulation attenuation
Decrease in signal intensity as distance from

transmitter increases.

Triangulation: Attenuation

Pr = P0 ( d / d0 )-n

n = Path-loss exponent (2, 4).

P0 = Power at reference distance d0.

Pr = Power at distance d.

P0

Pr

d0

d

triangulation attenuation1
Challenges:

Signal propagation issues, especially indoors:

shadowing, scattering, multipath propagation.

Triangulation: Attenuation
triangulation angulation
Angulation: using angles to determine distance with directional, or phased-array antennas.

2D position requires two angle + one distance measurement.

3D position requires two angle + one length + one azimuth measurement.

Triangulation: Angulation

d

scene analysis
Features of an observed scene from a particular vantage point used to infer location.

Static: observations matched to features recorded in a database with corresponding locations.

Differential: examine differences between two successive scenes to calculate location.

Passive observation => better privacy, low power requirents.

Requires compiling a database of features: extensive infrastructure.

Scene Analysis
proximity
Detecting an object when it is near a known location through observed changes at that location.

Physical contact: pressure sensors, capacitance field detector. Smart Floor.

Monitoring access point = ‘in-range’ proximity. Active Badge.

Automatic ID Systems: RFID badges, UPC scanning, phone & computer logs. Location of scanner, badge, computer, phone, identifies location of object.

Proximity
location properties
Physical vs Symbolic: lat, lon or “in the kitchen.”

Absolute vs Relative reference frame.

Accuracy or granularity eg: within 1 meter.

Precision or repeatability eg: within 1 meter 75% of the time.

Location Properties
location system properties
Scale - locate how many objects over what area?

Local sensor-based computation: better privacy, but higher computational, power, cost requirements.

Infrastructure-based computation: remove computational , power costs to the wired infrastructure. Allows smaller, cheaper sensors.

Cost

Location System Properties
research
Active Bat [Hightower] : Lateration using time of flight, ultrasonic with radio synchronization. Infrastructure-based computation, coordination; 9 cm accuracy.

Cricket [Priyantha ]:Lateration using time of flight, ultrasonic with radio synchronization. Sensor-based computation; no centralized coordination. Four-foot accuracy.

Research
research1
RADAR [Bahl] : uses signal strength for both lateration (4.3 meter accuracy), and scene analysis (3 meter accuracy).

Heterogeneous Sensor Network Systems

Use a combination of few high-powered beacon sensors broadcasting known location (GPS, etc) and many low-powered sensors to form a cooperative localization system.

Research
research2
Centroid localization schemes using received beacon positions. Range-Free[He] and GPS-Less [Bulusu] .Research

Propagation circles (or triangles) allow calculating location as the center position of all received signals.

X=(X1…XK)/k

Y=(Y1…YK)/k

ad hoc positioning system
Multihop localization schemes, APS[Niculescu],use a distance-vector flooding technique to determine the minimum hop count and average hop distance to known beacon positions.

Each beacon broadcasts a packet with its location and a hop count, initialized to one.

The hop-count is incremented by each node as the packet is forwarded.

Each node maintains a table of minimum hop-count distances to each beacon.

Ad-Hoc Positioning System
ad hoc positioning system1
Ad-Hoc Positioning System

30 m

60 m

30 + 60

3 + 4

= 12.9m

ad hoc positioning system2
A beacon can use the absolute location of another beacon along with the minimum hop count to that beacon to calculate the average distance per hop.

The beacon broadcasts the average distance per hop, which is forwarded to all nodes.

Individual nodes use the average distance per hop, along with the hop count to known beacons, to calculate their local position using lateration.

Ad-Hoc Positioning System
references
Dragos Niculescu, Badri Nath, Ad-Hoc Positioning System(APS) in Proceedings of IEEE GLOBECOM ‘01, Nov 2001.

Jeffrey Hightower, Gaetano Borriello, A Survey and Taxonomy of Location Systems for Ubiquitous Computing, IEEE Computer, Aug 2001.

N.B. Priyantha, A Chakraborty, H. Baladrishnan, The Cricket Location-Support System, in Proceedings of MOBICOM, ‘00, Aug 2000.

P.Bahl, V.N. Padmanabhan, RADAR: An In-Building RF-Based User Location and Tracking System, in Proceedings of IEEE INFOCOM ‘00, March 2000.

N. Bulusu, J. Heidemann, D. Estrin, GPS-less Low Cost Outdoor Localization for Very Small Devices, IEEE Personal Communications Magazine, Oct 2000.

Tian He, Chengdu Huang, Brian M. Blum, Range-Free Localization Schemes for Large-Scale Sensor Networks, MOBICOM 2003.

References