Localization in Sensor Networking

1. Localization in Sensor Networking John Quintero

2. Application-driven, data-centric sensor networks frequently require location information tied to sensor data: Wildlife Tracking Weather Monitoring Location-based Authentication Applications

3. 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

4. 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

5. 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

6. 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

7. Challenges: Signal propagation issues, especially indoors: shadowing, scattering, multipath propagation. Triangulation: Attenuation

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. Ad-Hoc Positioning System 30 m 60 m 30 + 60 3 + 4 = 12.9m

18. 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

19. 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