Localization in Sensor Networking

John Quintero

Applications

Application-driven, data-centric sensornetworks frequently require locationinformation tied to sensor data:

• Wildlife Tracking• Weather Monitoring• Location-based Authentication

Three Techniques for Determining Location

• TriangulationLocation determined using triangle geometry.

• Scene AnalysisObserved features used to infer location.

• ProximityDetection of change near known 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.

d

d

d

Triangulation: Lateration

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: Attenuation

Decrease in signal intensity as distance fromtransmitter increases.

P0

Prd0

d

Pr = P0 ( d / d0 )-n

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

P0 = Power at reference distance d0.

Pr = Power at distance d.

Triangulation: Attenuation

Challenges:

Signal propagation issues, especially indoors:

shadowing, scattering, multipath propagation.

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.

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.

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.

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 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

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

• 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• Centroid localization schemes using received beacon

positions. Range-Free [He] and GPS-Less [Bulusu] .

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

30 m 60 m

30 + 60 3 + 4

= 12.9m

Ad-Hoc Positioning System

– 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.

ReferencesDragos 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.