hci2003 tutorial location sensing technology for pervasive …senu/pub/hci03_tutorial.pdf · 2003....

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HCI2003 TutorialLocation Sensing Technologyfor Pervasive Computing

이선우한림대학교, 정보통신공학부

2003년 2월 10일

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Tutorial Objectives

• What is “Pervasive computing”?• What is the vision of future computing?• What is Context?• Why is important to sense the location of

user?• What technology is used for sensing

location, especially indoor environment.• What are the leading edge research

projects?



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Agenda

1. Introduction of Pervasive Computing (PvC)– Introduction– Case study: MIT Oxygen project

2. Context-awareness– What is “Context” ?– Context-Aware Applications

3. Location-sensing technology– Survey & Taxonomy– Case studies§ Cricket (MIT), Nibble(UCLA), SpotON(UW)§ Dead-reckoning based method (me)



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I. Pervasive Computing

• Introduction• Case study

–MIT Oxygen project



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Meaning of Terminology?

Pervasive Ubiquitous



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Definition of PvC

• By whatis.com– A trend towards increasingly ubiquitous

connected computing devices in the environment– PvC devices are very tiny - even invisible - devices,

either mobile or embedded in almost any type of object imaginable

– All devices communicate through wire/wireless networks.

– Goal is to create a system that is pervasively and unobtrusively embedded in the environment, completely connected, intuitive, effortlessly portable, and constantly available.



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Taxonomy of research problem in PvC

Remote communicationFault ToleranceRemote Information Access

Mobile NetworkingAdaptive ApplicationEnergy-aware systemsLocation-aware systems

Smart spaceInvisibilityLocalized Scalability

DistributedSystems

MobileComputing

PervasiveComputing

* Reference: “Pervasive Computing: Vision and Challenges”, M. Satyanarayanan, CMU, IEEE Personal Communications, Aug. 2001.



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Research Projects for PvC

• “Oxygen” at MIT (http://oxygen.lcs.mit.edu/)• “Aura” at CMU

(http://www-2.cs.cmu.edu/~aura/)• “Endeavour” at UC Berkeley

(http://endeavour.cs.berkeley.edu/)• “Portolano” at U. of Washington

(http://portolano.cs.washington.edu/)• AT&T Research in Cambridge, IBM Watson

Research Center, Xerox PARC, etc.



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I. Pervasive Computing

• Introduction• Case study

–MIT Oxygen project



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Case study: Oxygen project

• Reference:Oxygen Brochure http://oxygen.lcs.mit.edu/publications/Oxygen.pdf

• People– MIT Lab. For Computer Science– MIT Artificial Intelligence Lab.

• Vision– Machine centered computers è Human centered– Computation will be pervasive, like batteries, power

sockets, and the oxygen in the air.– Bring computation to us, whenever, wherever– Boost human’s productivity



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Approach

Integrated technologies that address human needs



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System Technologies

• E21 Stationary devices – Called Enviro21(E21) – Embedded in offices, buildings,

homes, and vehicles– Sense and affect our immediate

environment

• H21 Hand-held device– Called Handy21 (H21)– Empower us to communicate and

compute no matter where we are.

Intelligent Room



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System Technologies

• N21 Networks– Flexible, decentralized networks– Supports dynamically changin configurations of

self-identifying mobile and stationary devices– Enable us to access the information and services

we need, securely and privately

• Software architecture– Supports change above the device and network

levels.– Matches current user goals with currently

available software services.



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User Technologies

• Spoken language, sketching and visual cues– Main modes of interaction with Oxygen– Can better recognize our intentions– Perceptual technologies are part of the core

• Knowledge access– Individualized access technologies offer improved

access to information-customized to the needs of user.



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User Technologies

• Automation– Provide natural, easy-to-use, customizable, and

adaptive mechanisms for automating and tuning repetitive information and control task.

• Collaboration– Help people engage in group activities, even

though they may be participating at different times, in different locations

– Trace group interactions, keeping an accessible, annotated trail of issues, decisions, etc.



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Oxygen Applications

• Videos– H21 Visitor Guide Demo – Location detection method “Cricket” demo– Intelligent room demo– Multilingual speech interface demo– Sketching demo



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II. Context Awareness

• What is “Context” ?• Context-Aware Applications



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What is “Context” ?

• Definition by Schilit*– Computing context: network connectivity,

communication costs, comm. bandwidth, and nearby resources such as printers, displays, etc.

– User context: user’s profile, location, identities of nearby people, even the current social situation.

– Physical context: lighting, noise levels, traffic conditions, temperatures.

* Bill Schilit and etc. “Context-aware computing applications”, (URL:http://seattleweb.intel-research.net/people/schilit/)



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What is “Context” ?

• Definition by Dey*: – “Context” is any information that can be used to

characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves.èMakes it easier for a developer to enumerate the

context for a given application.*Reference: Anind K. Dey (http://www.cs.berkeley.edu/~dey/) “Understanding and Using Context”, Personal and Ubiquitous Computing Journal, vol.5, 2001.



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Definition of Context-aware computing

• A system is context-aware if it uses context to provide relevant information and/or services to the user, where relevancy depends on the user’s task.

• Features for context-aware applications– Presentation of information and services to user– Automatic execution of a service for a user– Tagging of context to information to support later

retrieval



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Classes of context-aware applications• Categorized by Schilit

– Proximate selection: a user-interface technique where the objects located nearby are emphasized or otherwise made easier to choose.

– Automatic contextual reconfiguration: a process of adding, removing, or altering the components due to context changes.

– Contextual information and commands, which can produce different results according to the context

– Context-triggered actions: simple IF-THEN rules used



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II. Context Awareness

• What is “Context” ?• Context-Aware Applications



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Survey of Context-Aware computing• Source: G. Chen, D. Kotz, Dartmouth C.S.

Tech. Report TR2000-381 (http://citeseer.nj.nec.com/chen00survey.html)

• Definition of C-A computing:– Active context awareness: an application

automatically adapts to discovered context, by changing the application’s behavior.

– Passive context awareness: an application presents the new or updated context to an interested user or makes the context persistent to retrieve later.



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Survey of Context-Aware computing

Help visitor to tour exhibition based on their location and interest

Visitor location and interestContentsATR MICC-MAP

When user enters a room, system displays the related info of presentation (name, title, etc.)

Attendee’s location, current time, schedule

PresentationFCE at

GeorgiaTech

Conference assistant

Dynamically map the user interface onto the nearest computer

User & Workstation location

NoneORLTeleporting

Help receptionist to forwards the callsUser locationNoneOlivetti

Res. Ltd.Call

Forwarding

DescriptionActive context

Passive contextGroupTitle



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Importance of Location for Sensing Context • Location may be used as a means of

understanding the overall context within the system is placed.

• Applications as key information– Navigation systems: all kinds of transportations

(Plane, Ships, Car, etc.)– Electronic Travel Aids (ETA) for handicapped

people – Spatially-based guidance systems: e.g.,CMAP,

GUIDE



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III. Location Sensing Technology

• Survey & Taxonomy• Case studies

– “Cricket” of Oxygen project in MIT Media lab.– “Nibble” of MUSE project in UCLA– “SpotON” of Portolan project in UW– Relative measuremnet based method (me)



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Location sensing technologySurvey and Taxonomy

• Reference: “Location Systems for Ubiquitous Computing”, J. Hightower, G.Borriello, Computer, Aug. 2001.(www.cs.washington.edu/homes/jeffro/)

• Provides good survey and taxonomy of location systems for pervasive computing



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Sensing Techniques: Triangulation• Lateration: compute the position

of an object by measuring its distance from multiple reference positions

• Measuring techniques– Direct– Time-of-Flight: GPS(radio),

ActiveBAT, Cricket (ultrasound)– Attenuation: SpotON

• Angulation



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Sensing Techniques: Scene Analysis

• Use features of a scene observed from a particular point to draw conclusions about the locations of the observer.

• Two types– Static analysis:features are looked up in a

predefined dataset that maps them to locations.– Differential analysis tracks the difference between

successive scenes to estimate location.

• Using visual image, radio signal strength



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Sensing Techniques:Proximity• Determine when an object is “near” a

known location• 3 approaches

– Detecting physical contact: using pressure sensors, touch sensors, capacitive field detectors

– Monitoring wireless cellular access points: ex) Active Badge System, Xerox ParcTAB using infrared cells, CMU Wireless Andrew using 802.11 network

– Observing automatic ID systems: POS, login histories, etc.



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Location System Properties

• Physical position and symbolic location– 36°22’11’’N by 127°22’45’’E, 68.3m elevation – In the kitchen, next to a mailbox, etc.

• Absolute versus Relative– A shred reference for all objects (GPS)– Have its own reference (rescuer’s device)

• Localized Location Computation– GPS, online map, print map, etc.– Bar codes, RF ID tags, track shipment, etc.



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Location System Properties

• Accuracy and PrecisionIn spec. of GPS: within 10m for approx. 95%

• ScaleIn worldwide, within a metropolitan, throughout a

campus, in a particular building, within a room

• Recognition• Cost• Limitations



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Survey: properties



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Survey: Classification criteria 2



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Survey: Classification criteria 1



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III. Location Sensing Technology

• Survey & Taxonomy• Case studies

– “Cricket” of Oxygen project in MIT Media lab.

– “Nibble” of MUSE project in UCLA– “SpotON” of Portolan project in UW– Relative measuremnet based method (me)



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Case Studies 1: “Cricket” project

• Web source: http://nms.lcs.mit.edu/projects/cricket/

• Cricket is indoor location system for pervasive computing environments, such as those envisioned by MIT's Project Oxygen

• Two version1. Cricket2. Cricket Compass



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Cricket: Overview

• Using listeners that hear and analyze information from beacons spread throughout the building

• Use a combination of RF and ultrasound media to determine the current position of the listener.

• Reference: Nissanka B. Priyantha, Anit Chakraborty, Hari Balakrishnan, The Cricket Location-Support system, Proc. 6th ACM MOBICOM, Boston, MA, August 2000.



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System architecture

BeaconAttached to environments

(ceiling/wall)

ListenerAttached to static/mobile

nodes



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Goals of Cricket

• User privacy• Decentralized administration• Network heterogeneity• Cost• Room-sized granularity



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Determining location

• Fail to use attenuation of RF-signal strength for location sensing

• Use a combination of RF and ultrasound hardware to enable a listener to determine the distance to beacons– Used fact: the speed of sound is much smaller than the

speed of light(RF) in air– A beacon send concurrently info. About the location over

RF, together with an ultrasound pulse.– The listner hears the RF signal, turns on its ultrasound

receiver, then measure the time-of-flight.

• Try to find a closest beaconèLocation info.



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Reducing interference

• Use decentralized beacon network è Easy to configure and manage èCause RF transmissions from different beacons to collide in close proximity.

• To avoid collision, using randomization.• Not use fixed/deterministic transmission

schedule, transmission times are chosen randomly with a uniform distribution within an interval [R1,R2]ms.



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Results

• Granularity (resolution)– Boundary performance: 0.5 feet– Location granularity of 4x4 feet,

by placing the beacons in a 4x4 feet grid

• Benefits– Not a tracking system– It scales well as the number of

devices increases– Its decentralized architecture

makes it easy to deploy



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Qualitative comparison



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Cricket Compass: Overview

• Added features– Position information § (x, y, z) coordinates within a space

– Orientation information§ direction at which device faces

• Reference: Nissanka B. Priyantha, Allen Miu, Hari Balakrishnan, Seth Teller, The Cricket Compass for Context-Aware Mobile Applications, Proc. 7th ACM MOBICOM, Rome, Italy, July 2001.

θ

Mobile device(x, y, z)



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You Are Here… Great, now what?!

You are here



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Point-and-Use Application



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Design Goals

• Compact, integrated, self-contained• Should not rely on motion to determine

heading (as in GPS navigation systems)• Robust under a variety of indoor

conditions• Low infrastructure cost; easy to deploy• Enough accuracy for mobile applications

(5o accuracy)



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Determining the locations

Beacons onceiling

Mobile deviceCricket listenerwith RF and ultrasonic

sensors

ZX

Y

RF + UltrasonicPulse

(x1,y1,z1)

(x0,y0,z0)

(x2,y2,z2)

( x, y, z)

(x3,y3,z3)

vt3 to solve for unknown speed of sound

vt3vt0vt1 vt2



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Definition of Orientation

Mobile device

Beacons onceiling

Orientation relative to B

θ

BBeacons onceiling

ZX

Y (x1,y1,z1)

(x0,y0,z0)

(x2,y2,z2)(x3,y3,z3)

(on horizontal plane)

(on horizontal plane)



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Approach: Use Differential Distance to Determine Orientation

sin θ = (d2 - d1) / sqrt (1 - z2/d2)where

d = (d1+d2)/2

Assume: Device rests on horizontal planeMethod: Use multiple ultrasonic sensors;

calculate rotation using measured distances d1, d2, z

Need to measure:a) (d2 - d1)b) z/d

d1 d2 z

θ

Beacon

S2S1

d

L



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Problem: Measuring (d2 – d1) directly requires very high precision!

• Consider a typical situation– Let L = 5cm, d = 2m, z = 1m, θ = 10º– (d2 – d1) = 0.6cm

• Impossible to measure d1, d2 with such precision– Comparable with the wavelength

of ultrasound ( λ = 0.87cm)

d1 d2 z

θ

Beacon

S2S1

d

L



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Solution: Differential Distance (d2-d1) from Phase Difference (φ)

• Observation: The differential distance (d2-d1) is reflected as a phase difference between the signals received at two sensors

d2d1

t

φ = 2π (d2 – d1)/λ

Beacon Estimate phase difference between ultrasonic waveforms to find (d2-d1)!

S1 S2∆t



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Problem: Two Sensors Are Inadequate• Phase difference is periodic à ambiguous

solutions• We don’t know the sign of the phase difference

to differentiate between positive and negative angles

• Cannot place two sensors less than 0.5λ apart– Sensors are not tiny enough!!!– Placing sensors close together produces inaccurate

measurements



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Solution: Use Three Sensors!

d1

t

L12 = 3λ/2

d2 d3

L23 = 4λ/2

• Estimate 2 phase differences to find unique solution for (d2-d1)

• Can do this when L12 and L23are relatively-prime multiples of λ/2

• Accuracy increases!

Beacon

S1 S2 S3



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Cricket Compass v1 Prototype

RF module (xmit)RF antenna

Ultrasonictransmitter

BeaconSensor Module

Ultrasound Sensor Bank 1.25 cm x 4.5 cm



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Result

• Accurate to 3° for ± 30°, 5° for ± 40°• Error increases at larger angles



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Conclusion

The Cricket Compass provides accurate position and orientation information for indoor mobile applications– Orientation information is useful– Novel techniques for precise position and phase

difference estimation to obtain orientation information– Prototype implementation with multiple ultrasonic

sensors

Orientation accurate to within 3-5 degrees



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Case Studies 2: “Nibble” project

• The Nibble location system is an indoor location system for mobile devices (i.e. a laptop) equipped with a wireless network (IEEE 802.11) card(http://mmsl.cs.ucla.edu/nibble/)

• Developed in the Multimedia Systems Laboratory at the Department of Computer Science, UCLA

• Stand-alone version of a "fusion service" as part of the Multi-use Sensor Environment (MUSE) project

• Basic idea: Scene analysis



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Related works

• Received Signal Strength Information(RSSI) base location sensing techniques– Microsoft Research’s RADAR§ Scene analysis and triangulation technique

– “Determining User Location For Context Aware Computing Through the Use of a Wireless LAN Infrastructure”, by J. Small, et. Al., Project Aura report (http://www-2.cs.cmu.edu/~aura/docdir/small00.pdf)§ Table-based training technique



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Overview

• Use the signal quality received from access points that can be detected at each location and incrementally builds a Bayesian network which can be used to calculate the most likely location for a signal quality "signature."

• Discriminate between locations roughly 10 feet apart

• Performance is highly dependant on various factors: the building topology, number of access points, path effects, noise, etc.



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Requirements and Using

• Requirements1. A Lucent Orinoco (or WaveLAN) IEEE 802.11

card2. The Lucent Client Manager software tool 3. The Xerces XML Parser and Xalan XML tools jar

files. (Downloaded from http://xml.apache.org)4. Java JDK 1.2 or above

• Using NibbleLet’s visit to homepage



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Case Studies 3: “SpotON” project

• Ad-hoc Location Sensing using Radio Signal Strength (http://portolano.cs.washington.edu/projects/spoton/)

• SpotON is a Portolano research project, Dept. of CSE, U.Washington

• Target scenario: a strategy room can be temporarily “SpotON enabled” simply by attaching one or more tags to the walls and interior. Tagged people and objects inside the room can then be located relative to one another.



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Example: Traditional approach

Static Base stations and Controller(s)

ThingThing

BSBS

BSBS

BSBS

Controller

Application



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Infrastructure Application

Infrastructure Application

Mobile/Wearable Application

Mobile/Wearable Application

ThingThing

New Approach:Ad-hoc Location Sensing

ThingThingThingThing

ThingThing

ThingThingThingThing



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Tags: hardware

• A SpotON sensor tag is attached to each thing we wish to locate

ThingThing



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Distance measurement

• Inter-tag distance estimated using received radio signal strength information (RSSI)

• Calibration increases estimate accuracy



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Location Computation

• We compute tag locations by convolving all cluster distance estimates and maximizing location likelihood.

• Accuracy – Increases with cluster size – Put more sensors where precision is needed



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Case Studies 4:

• Reference: S.-W. Lee “Activity and Location Recognition Using Wearable Sensors”, IEEE Pervasive Computing, July-September, pp. 24-32, 2002.

• Relative measurement-based location sensing methods

• Suggested approaches– Hybrid sensing technique– Relative measurement based technique



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Motivation

• Want to track the location of user continuously and high resolution(2~3m)

• Required– Install many Tx (or Rx) in active beacon

systems– High computing power for image processing in

vision based system

è Want to make wearable and inexpensive location finding method



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Motivation

• Basic Idea– Common using hybrid approach-relative and absolute

position measurements in mobile robot fields– Why don’t use relative measurement to find out

the location of a human in indoor environments ? • Considered problems

– How to detect an incremental human locomotion (distance & direction) ?èHuman locomotion is by WALKING– How to correct an accumulated position errors ?èCombine with the conventional beacon system



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Prototype I: Hardware

Sensing module

Notebook PC- DAQ & Signal processing- Navigation algorithm

. Step detection

. Step recognition

. Dead-reckoning algorithm

. Correction algorithm

Digital compass

Infrared light detector

Bi-axial accelerometer



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Approach 1: overview

• Hybrid position measurements– Dead-reckoning method based on pace tracking

for Relative information§ Step detection algorithm with acceleration data§ Step size estimation based on walking speed§ Heading measurement using digital compass

– Absolute information: using simple IR beacon system

• Walking behavior classification– 3 discrimination: “level”, “up”, and “down”– Use to detect a stairway for error correction



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Dead-reckoning method ?

x

y

One step distance

Heading

Starting position

Currentposition

Procedure for determining the present location of an object by advancing some previous position through known heading and distance information.



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Location recognition using incremental motion

• When detects a “level” step, updates current location with heading measurement– North & east accumulator

èBasic measurement unit: one step size• Error correction

– Based on simple beacon signal– By detecting a stairway via the classification of

walking behavior



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Results: a big square path

• Total length: 128[m]

• Installed 3 IR transmitters

• Average error: – East: 0.83[m] – North: 1.83[m]



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Merits of approach 1

• Gives continuous tracking capability• Reduces the quantity of information from

beacon system, 1 bit• Classification capability corrects an errors

and expands the tracking performance to multi-story environments.

• Reduces the effort for building an infrastructure of conventional beacon system



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Problems of approach 1

• Error sources: – Heading error is most significant, moreover

most difficult to detect in robust and accurate.

– Need to increase step recognition ratio (detection & classification)

– Measurement of one step size accurately.



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Approach 2: Overview

• To avoid a heading detection problem in approach 1

• Basic Idea– Focus on location transition in room scale(2~4m)– Topological description of location transition§ For example, the situation when a user gets a cup of coffee.Ø Going path: standing→2 steps north→40 steps east → 3 steps

south → 6 steps west, then he/she gets to the coffee areaØ Returning path has the same steps but in the reverse order and

reverse azimuth heading.

– Learning based method



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Block diagram

Sensing BlockSensing Block • Read the data of sensors• Executes pre-processing

Unit Motion Recognizer

Unit Motion Recognizer • Detect unit motion

• Classify behavior: Level，Up, Down

Location RecognizerLocation

Recognizer

• Keep track of the walking behaviors and heading measurement.

• Use fuzzy reasoning to find matched sequence from a trained data base.

• Determine a location transition



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Results: unit motion recognition

• Test data – Level: 1852 steps– Up/Down: 156 steps

• Result

91.708.3Up

098.11.9Down

1.70.298.1Level

UpDownLevelUnit [%]



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Selected locations

#0: My seat

#2: Coffee area

#1: Printer room

#3: Toilet

#4: Experiment room

#5: Library#6: Cash machine



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Result: location recognition

60256 → 0

80150 → 6

86170 ↔ 5

852150 ↔ 3

8

13

14

# of visits

8810 ↔ 4

10000 ↔ 2

9310 ↔ 1

accuracy(%)# of failurePath

• Average recognition ratio: 86.7 [%]

• Found limitations:– Main error source: wrong

recognition between “level” and “up”

– Shows location transition before the user goes to the destination



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Prototype 2

• Objectives: improve the performance of approach 2

• Hardware: design for more comfortable usage

• Method– Try to find a general model of walking behavior

for multi-user =>improving UMR– Try to find a simpler and better path descriptor for

location recognition



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Hardware

• Designed for PDA• Sensing module

– PIC 16F84– Add a new sensor:

gyroscope



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Use of prototype 2



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Overview

• Unit motion recognition– Five behaviors: sitting, standing, walking on level

ground, ascending, descending a stairway– For level behavior, recognize more detail: slow,

normal, fast– Use different check point and different feature

vector

• Location recognition– Use geometrical path descriptor, (x,y,z)– Direct use of dead-reckoning (same as approach

1)



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Location recognition: result

• Average recognition ratio is 91.8% for 4 rounding paths and 2 complex paths

• Improve one limit of approach 2

• There remains “drift”problem

86.72150 → 4

78.63154 → 0

93.81160→1→ 2→0

1000140→2→ 3→0

853203 → 0

951200 ↔ 3

1000220 ↔ 2

22

22

22

# of visits

95.512 ↔ 0

95.511 → 0

10000 → 1

accuracy(%)# of failurePath



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Tutorial Summary

• Provide a broad range of information (mainly web resources) about pervasive computing and brief explanation of location sensing technologyè Help to imagine our life style in near

future, then recognize potential problems evolved in our life, finally make a right decision for our researches.



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hci2003 tutorial location sensing technology for pervasive …senu/pub/hci03_tutorial.pdf · 2003....

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