Pervasive location aware computing
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Pervasive Location-Aware Computing. Hari Balakrishnan Networks and Mobile Systems Group MIT Laboratory for Computer Science http://nms.lcs.mit.edu/. Why you should care. Location-awareness will be a key feature of many future mobile applications Many scenarios in pervasive computing

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Pervasive Location-Aware Computing

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Pervasive location aware computing

Pervasive Location-Aware Computing

Hari Balakrishnan

Networks and Mobile Systems Group

MIT Laboratory for Computer Science

http://nms.lcs.mit.edu/


Why you should care

Why you should care

  • Location-awareness will be a key feature of many future mobile applications

  • Many scenarios in pervasive computing

    • Navigation

    • Resource discovery

    • Embedded applications, sensor systems

    • Monitoring and control applications

  • The design of good location-aware computing systems cuts across many areas of CS/EE

    • E.g., sensors, signal processing, networking, mobility, data management, graphics/visualization, planning, HCI, …

    • Most of the exciting stuff will happen in the next few years!


Computing

Computing

Input

Output

Processing


Networked computing

Processing

+

communication

Processing

+

communication

Processing

+

communication

Processing

+

communication

Network

Networked Computing


Networked context aware computing

Sensors

Processing

+

communication

Processing

+

communication

Processing

+

communication

Location

information

Processing

+

communication

Resource

information

Network

Actuators

Networked, Context-Aware Computing

Environmental

Context


Location aware applications

Location-Aware Applications

  • Human-centric

    • “Finding” applications

  • Embedded

    • Sensors & actuators

    • Devices

    • Monitoring and control

  • System should support both forms


This talk

This Talk

  • Cricket location infrastructure

  • Some applications

  • System architecture

  • Challenges for the future


Cricket

Cricket

  • Architecture for ubiquitous location-sensing

    • No single location-sensing technology works everywhere today, particularly indoors

  • Integrates variety of sensory information

    • GPS: wide-open outdoors

    • Wireless access info: coarse-grained info

    • RF + ultrasonic trilateration: indoors and in urban areas

  • Sensor-independent location API


Desired functionality

Desired Functionality

  • What space am I in?

    • Room 510, reception area, seminar room,…

    • How do I learn more about what’s in this space?

    • An application-dependent notion

  • What are my (x,y,z) coordinates?

    • “Cricket GPS”

  • Which way am I pointing?

    • “Cricket compass”

  • Goal: Linear precision of a few centimeters, angular precision of a few degrees


Design goals

Design Goals

  • Must determine:

    • Spaces: Good boundary detection is important

    • Position: With respect to arbitrary inertial frame

    • Orientation: Relative to fixed-point in frame

  • Must operate well indoors

  • Preserve user privacy: don’t track users

  • Must be easy to deploy and administer

  • Must facilitate innovation in applications

  • Low energy consumption


Cricket architecture

info = “a2”

info = “a1”

Cricket Architecture

Beacon

Estimate distances

to infer location

Listener

Autonomous: No central beacon control or tracking

Passive listeners + active beacons facilitates privacy

Straightforward deployment and programmability


Pervasive location aware computing

Machinery

B

Beacons on

ceiling

SPACE=NE43-510

ID=34

COORD=146 272 0

MOREINFO=

http://cricket.lcs.mit.edu/

Cricket

listener

Mobile device

Mobile device

Obtain linear distance estimates

Pick nearest to infer “space”

Solve for mobile’s (x, y, z)

Determine  w.r.t. each beacon and deduce

orientation vector


Determining distance

RF data

(spacename)

Determining Distance

Beacon

  • A beacon transmits an RF and an ultrasonic signal simultaneously

    • RF carries location data, ultrasound is a narrow pulse

Ultrasound

(pulse)

Listener

  • The listener measures the time gap between the receipt of RF and ultrasonic signals

    • A time gap of x ms roughly corresponds to a distance of x feet from beacon

    • Velocity of ultrasound << velocity of RF


Multiple beacons cause complications

Multiple Beacons Cause Complications

Beacon A

Beacon B

  • Beacon transmissions are uncoordinated

  • Ultrasonic signals reflect heavily

  • Ultrasonic signals are pulses (no data)

    These make the correlation problem hard and can lead to incorrect distance estimates

Incorrect distance

Listener

t

RF B

RF A

US B

US A


Solution

Solution

  • Carrier-sense + randomized transmission

    • Reduce chances of concurrent beaconing

  • Bounding stray signal interference

    • Envelop all ultrasonic signals with RF

  • Listener inference algorithm

    • Processing distance samples to estimate location


Bounding stray signal interference

RF A

US A

t

Bounding Stray Signal Interference

  • Engineer RF range to be larger than ultrasonic range

    • Ensures that if listener can hear ultrasound, corresponding RF will also be heard


Bounding stray signal interference1

S/b

t

r/v (max)

S r

b v

Bounding Stray Signal Interference

  • No “naked” ultrasonic signal can be valid!

S = size of space advertisement

b = RF bit rate

r = ultrasound range

v = velocity of ultrasound

(RF transmission time) (Max. RF-US separation

at the listener)


Estimation algorithm windowed minmode

A

B

Actual distance (feet)

6

8

Mode (feet)

6

8

Mean (feet)

7.2

6.4

Majority

9

10

Estimation AlgorithmWindowed MinMode

A

Frequency

B

5

Distance

(feet)

5

10


Pervasive location aware computing

Orientation

B

Beacons on

ceiling

Orientation relative to B

on horizontal plane

Cricket listener with

compass hardware

Mobile device

(parallel to horizontal plane)


Trigonometry 101

d1

d2

z

Cricket

Compass

Trigonometry 101

Beacon

Idea: Use multiple ultrasonic sensors

and estimate differential distances

sin  = (d2 - d1) / sqrt (1 - z2/d2)

where d = (d1+d2)/2

Two terms need to be estimated:

1. d2 – d1

2. z/d (by estimating

coordinates)

Heading


Differential distance estimation

Beacon

d1

d2

L

t

f = 2p (d2 – d1)/l

Differential Distance Estimation

  • Problem: for reasonable values of parameters (d, z), (d2 - d1) must have 5mm accuracy

    • Well beyond all current technologies!

Estimate phase difference between ultrasonic waveforms!


Pervasive location aware computing

vt1

vt2

vt3

vt4

Coordinate Estimation

B

Beacons on

ceiling at known

coordinates

(x,y,z)

Four equations, four unknowns

Velocity of sound varies with temperature, humidity

Can be “eliminated” (or calculated!)


Beacon placement

Beacon Placement

Totally arbitrary beacon placement won’t demarcate spaces correctly

Room A

Room B

I am at

B


Correct beacon placement

Correct Beacon Placement

Room A

Room B

x

x

I am at

A

  • Position beacons to detect the boundary

  • Multiple beacons per space are possible


System configuration administration

System Configuration & Administration

  • Password-based authentication for configuration

  • Currently, coordinates manually entered

  • Auto-configuration algorithm being developed

  • MOREINFO database centrally managed with Web front-end

    • Relational DBMS

    • Challenge: queries that don’t divulge device location, but yet are powerful


Pervasive location aware computing

Cricket v1 Prototype

RF module (rcv)

RF module (xmit)

Ultrasonic

sensor

Ultrasonic

sensor

RF antenna

Listener

Beacon

Atmel

processor

RS232

i/f

Host software libraries in Java;

Linux daemon (in C) for Oxygen BackPaq handhelds

Several apps…


Pervasive location aware computing

Deployment


Some results

Some Results

  • Linear distances to within 6cm precision

  • Spatial resolution of about 30cm

  • Coordinate estimation to within 6cm in each dimension

  • Orientation to within 3-5 degrees when angle to some beacon < 45 degrees

  • Several applications (built, or being built)

    • Stream redirection, active maps, Viewfinder, Wayfinder, people-locater

    • Scalable location-aware monitoring (SLAM) apps: MIT library book tracking, asset management, MIT physical plant maintenance


Where am i active map

Where am I?(Active map)


What s near me find this for me resource discovery

What’s near me? Find this for me(Resource discovery)

“Print map on a color printer,”

and system sends data to nearest available free color printer and tells

you how to get there

Location by “intent”


How do i get to jorg s office

How do I get to Jorg’s office?


Large scale monitoring

Large-Scale Monitoring

Scale

(# sensors)

Power, thermal

Monitoring & control

107

Asset tracking

Fire detection

Assisted evacuation

106

Library usage

Motion detection

Leaks, floods

Lab equipment

monitoring

Cricket network

auto-configuration

105

Physical plant

Repair orders

HazMat response

Local navigation

104

Personal safety

Traffic, parking

Irrigation

Days/Hours

Minutes

Seconds

Response time


Requirements

Requirements

  • Ubiquitous location-sensing

  • Heterogeneous sensor networking/comm. protocols

  • Resource discovery

  • Event handling

  • Query processing

  • Spatial databases

  • Mapping and representation

  • Navigation

  • User interfaces


Pervasive location aware computing

Strawman Architecture

Cricket beacons

(Pervasive)

Events

Tag reader

Actions

Tagged books,

equipment

Event-handling

& resource discovery

network

Sensor

Proxy

Application event handlers

(Distributed)

Sensors & actuators

Data stores

Fixed sensor proxy (sensor integration, pruning)

Mobile sensor proxy


Summary

Summary

  • Location-aware computing poses numerous interesting challenges for CS

    • An important component of pervasive computing

    • Integrating real-world information

    • App spectrum from HCI  Embedded apps

  • Cricket provides location information for mobile, pervasive computing applications

    • Space, position, orientation

    • Flexible and programmable infrastructure

    • Deployment and management facilities


Collaborators

Collaborators

  • Bodhi Priyantha

  • Allen Miu

  • Ken Steele

  • Rafael Nogueras

  • Seth Teller

  • Steve Garland

  • Dorothy Curtis

  • Omar Aftab

  • Erik Demaine

  • Mike Stonebraker

    http://nms.lcs.mit.edu/


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