Transcript Cricket Location Support System

The Cricket Location-Support System
Nissanka B. Priyantha
Anit Chakraborty
Hari Balakrishnan
MIT Lab for Computer Science
http://nms.lcs.mit.edu/
Motivation
• Emergence of pervasive computing
environments
• Context-aware applications
– Location-dependent behavior
• User and service mobility
– Navigation via active maps
– Resource discovery
Cricket provides applications information about
geographic spaces they are in
Design Goals
• Preserve user privacy
• Operate inside buildings
• Recognize spaces, not just physical position
– Good boundary detection is important
• Easy to administer and deploy
– Decentralized architecture and control
• Low cost and power consumption
Traditional Approach
Controller/
Location database
ID = u ?
Base stations
ID = u ?
ID = u ?
ID = u ?
ID = u
Transceivers
• Centralized architecture
• User-privacy issues
• High deployment cost
Cricket Architecture
Beacon
Space
A
Space
B
Space
C
I am at
C
Listener
• Decentralized, no tracking, low cost
• Think of it as an “inverted BAT”!
Determining Distance
Beacon
RF data
(location name)
Ultrasound
(pulse)
Listener
•• AThe
beacon
listener
transmits
measures
an RF
theand
timean
gap
ultrasonic
between
signal
the receipt
simultaneously
of RF and ultrasonic signals
A time
gaplocation
of x ms data,
roughly
corresponds
to a
–– RF
carries
ultrasound
is a narrow
distance of x feet from beacon
pulse
– Velocity of ultra sound << velocity of RF
Uncoordinated Beacons
Beacon A
Beacon B
Incorrect distance
RF B
RF A
US B US A time
• Multiple beacon transmissions are
uncoordinated
• Different beacon transmissions can interfere
– Causing inaccurate distance measurements at
the listener
Handling Spurious Interactions
•
Combination of three different techniques:
– Bounding stray signal interference
– Preventing repeated interactions via
randomization
– Listener inference algorithms
Bounding Stray Signal Interference
RF A
US A
t
• RF range > ultrasonic range
– Ensures an accompanied RF signal with
ultrasound
Bounding Stray Signal Interference
S
b
r
v
S/b
- size of space string
- RF bit rate
- ultrasound range
- velocity of ultrasound
S
b
(RF transmission time)
t
r/v (max)
r
v
(Max. RF US separation
at the listener)
Bounding Stray Signal Interference
RF B
US B
RF A
US A
t
• Envelop ultrasound by RF
• Interfering ultrasound causes RF signals to
collide
• Listener does a block parity error check
– The reading is discarded
Preventing Repeated Interactions
• Randomize beacon transmissions:
loop:
pick r ~ Uniform[T1, T2];
delay(r);
xmit_beacon(RF,US);
• Erroneous estimates do not repeat
• Optimal choice of T1 and T2 can be calculated
analytically
– Trade-off between latency and collision probability
Inference Algorithms
• MinMode
– Determine mode for each beacon
– Select the one with the minimum mode
• MinMean
– Calculate the mean distance for each beacon
– Select the one with the minimum value
• Majority (actually, “plurality”)
– Select the beacon with most number of readings
– Roughly corresponds to strongest radio signal
Inference Algorithms
A
B
Frequency
5
5
Distance
(feet)
10
A
B
Actual distance (feet)
6
8
Mode (feet)
6
8
Mean (feet)
6.14
6.4
7
10
Number of samples
Closest Beacon May Not Reflect
Correct Space
Room A
Room B
I am at
B
Correct Beacon Positioning
Room A
Room B
x
I am at
A
• Position beacons to detect the boundary
• Multiple beacons per space are possible
x
Implementation
• Cricket beacon and listener
• LocationManager provides an API to
applications
• Integrated with intentional naming system for
resource discovery
Implementation
• Cricket beacon and listener
RF
RF
Microcontroller
Microcontroller
US
US
RS232
• LocationManager provides an API to
applications
• Integrated with intentional naming system for
resource discovery
Static listener performance
Room A
Room B
Interference
I1
L2
I2
Room C
L1
• Immunity to
% readings due to
interference
interference
of RF from
I1
– Four beacons
within
andeach
I2 with
ultrasound
others
range
fromRF
beacons
– Two
interference
sources
I1
I2
• Boundary detection
L1ability0.0%
0.0%
L2 – L1 only
0.3%two feet
0.4%
away from boundary
Inference Algorithm Error Rates
Error Rates Measured With Listener At L1
45
40
Error Rate (%)
35
30
MinMean
MinMode
Majority
25
20
15
10
5
0
10
20
30
40
50
60
Number of readings
70
80
90
100
Mobile listener performance
Location Algorithm Error Rates
Room B
20
18
Error Rate (%)
Room A
Room C
16
14
MinMean
MinMode
Majority
12
10
8
6
4
2
0
2
3
4
Sampling Interval
5
6
Comparisons
Bat
System
Active
badge
RADAR
Cricket
Attribute
Track user
location?
Yes
Yes
No, if client
has signal
map
No
Deployment
considerations
Centralized
controller +
matrix of
sensors
Centralized
database +
wired IR
sensors
RF signal
mapping
and good
radios
Space
naming
convention
Position
accuracy
Few cm
Room-wide Room-wide
~2 feet for
spatial
resolution
Summary
• Cricket provides information about
geographic spaces to applications
– Location-support, not tracking
– Decentralized operation and administration
• Passive listeners and no explicit beacon
coordination
– Requires distributed algorithms for beacon
transmission and listener inference
• Implemented and works!
– Decentralized
– Preserves user privacy
– Good granularity
– Component cost U.S. $10
Beacon positioning
Location X
Imaginary
Boundary
X1
X2
X3
• Imaginary boundaries
• Multiple beacons per location
Future work
• Dynamic transmission rate with carrier-sense
for collision avoidance.
• Dynamic ultrasonic sensitivity.
• Improved location accuracy.
• Integration with other technologies such as
Blue Tooth.
Inference algorithms
• Compared three algorithms
– Minimum mode
– Minimum arithmetic mean
– Majority
Minimizing errors.
• Proper ultrasonic range ensures overlapping
RF and ultrasonic signals
–
–
–
–
RF data 7 bytes at 1 kb/s bit rate
RF signal duration 49 ms
Selected ultrasonic range = 30ft < 49 ft
Signal separation < 49 ms
Minimizing errors.
• Interfering ultrasound causes RF signals to
collide
• Listener does a block parity error check
– The reading is discarded