Location Systems for Ubiquitous Computing

Jeffrey Hightower and Gaetano Borriello

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

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

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

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

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

Scale

Coverage of system, the number of objects the system can locate per unit area per unit time

Communication bandwidth is important

Increasing infrastructure

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

Limitations

GPS does not work indoors

Interference

Characteristics of underlying technologies

Active Badge

Active Bat

Cricket

RADAR

Motionstar Magnetic Tracker

Easy Living

Smart Floor

Enhanced 911

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

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

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

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

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

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

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

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

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

Choosing a System Accuracy based comparison

Representing error distributions

Evaluation

Density of elements

Prototyping using a simulator

Quake iii