Location Systems for Ubiquitous Computing

1. Location Systems for Ubiquitous Computing Jeffrey Hightower and Gaetano Borriello

2. Intro • Ubiquitous computing • a person wanting to know where he was when he did a particular task • Help rescue teams • Customize environment based on location of user • devices have already been developed • what they sense and how they go about achieving it • physical attribute used • size • power usage • type of results obtained

3. Physical Position and Symbolic Location • Physical • GPS - 47°39´17’’ N by 122°18´23’’ W • Symbolic • Abstract , relative to the position of a known object • Provide coarse grained location information • Derived from Physical-positioning systems • Linking real-time train positions to the reservation and ticketing database can locate a person on a train

4. Absolute versus Relative positioning • Absolute location systems – GPS uses a universal reference grid • Two GPS receivers at the same position will show the same reading • In Relative Systems, each receiver has its own frame of reference • Devices that use a particular transmitter form a grid relative to that transmitter • Absolute position can be transformed to a relative one – relative to another reference point

5. Localized Location Computation • Object we are interested in computes its own location • Ensures privacy • Does not require the object to transmit information for external systems to locate it • Burden on the object increases so it is better left to the external system

6. Accuracy and Precision • Depend on the distribution of error and the density of elements • Overlapping levels of positioning systems to obtain fine grained location information • Coping dynamically with failures • Suitability for application at hand

7. Scale • Coverage of system, the number of objects the system can locate per unit area per unit time • Communication bandwidth is important • Increasing infrastructure

8. Recognition • Recognition of located objects to carry out some action, like controlling the located device over the internet • Assigning unique IDs to the located objects • Combine contextual information

9. Limitations • GPS does not work indoors • Interference • Characteristics of underlying technologies

10. Active Badge • Active Bat • Cricket • RADAR • Motionstar Magnetic Tracker • Easy Living • Smart Floor • Enhanced 911

11. Active Badge • Uses diffuse infrared technology - flooding an area with infra-red light • Each badge emits signal with unique id every 10 seconds that is received by a network of sensors • Location is symbolic – restricted area like a room • Range of several meters • Has difficulty in presence of sunlight

12. Active Bat • Infers location based on time of flight of ultrasound pulse • Each bat emits an ultrasound pulse with unique id to a grid of receivers • At the same instant a controller resets the receiver • Orientation is calculated by analysis • Distance is computed from the time interval between the reset and receiving the pulse • Accurate to within 3cm • Paging • Requires large sensor infrastructure

13. Cricket • fixed ultrasound emitters and mobile receivers • time gap to receive the signal is also set in the pulse to prevent reflected beams • computation takes place at receiver • decentralized architecture • few centimeters of accuracy • computational and power burden

14. RADAR • Based purely in software, building on standard RF wireless LAN technology • Uses signal strength and signal to noise ratio from wireless devices • Employs multiple base stations with overlapping coverage • Requires wireless LAN support on objects being tracked • Generalization to multifloored buildings is a problem

15. Motionstar Magnetic Tracker • Uses electromagnetic sensing • Axial DC magnetic-field pulses are generated • Position and orientation are found from by measuring the response on the three axes • Less than 1mm spatial resolution and 0.1° orientation • Must be within 1-3 meters of transmitter • Motion capture for animation

16. Easy Living • System to keep track of a room's occupants and devices • Uses real-time 3D cameras to provide vision positioning • measures location to roughly 10 cm on the ground plane, and it maintains the identity of people based on color histograms • Difficult to maintain accuracy • Aimed for a home environment

17. Smart Floor • System for identifying people based on their footstep force profiles • Does not need device or tag • 93% overall user recognition • High cost factor

18. Enhanced 911 • Locates any phone that makes a 911 call • reported in most instances with an accuracy of 100 meters or less • Can be enhanced for use by cell phone users • Identifying areas of traffic congestion

19. Future Work • Integrating multiple systems • Overlapping levels off sensing • Increases accuracy • Ad Hoc Location sensing • Cluster of ad hoc objects • Relative or absolute • Correlation of multiple measurements • High scalability

20. Choosing a System • Accuracy based comparison • Representing error distributions • Evaluation • Density of elements • Prototyping using a simulator • Quake iii