Transcript Wireless sensor networks for environmental monitoring

Wireless sensor networks for
environmental monitoring
Kris Steenhaut
Yann-Aël Le Borgne
ICCE 2010
11th of August, 2010
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Agenda
1) Technology and applications
2) Information processing for WSN
3) Case studies
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Part 1:
Technology
and
Applications
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Wireless Sensor Networks
Wireless sensors: Latest trend of Moore’s law (1965)
 Computing devices get
 Smaller
 Cheaper
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Smart Dust (Berkeley, 2000)
Image © 2000 Peter Menzel/Robo sapiens
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Wireless Sensor Networks
Sensor nodes can collect, process and communicate data
[Warneke et al., 2001; Akyildiz et al., 2002]
 Sensors: Light, temperature,
humidity, pressure, acceleration
 Radio: ~100 kbps, ~10 meters
 Microprocessor: ~10 MHz
 Memory: ~100 KB
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Wireless Sensor Networks
 Applications: Medical, interactive arts, ecology,
industry, disaster prevention....
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Research trend 2000-2009
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Network stack
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Physical layer: Hardware
Muneeb Ali,9 2008
Physical layer: Hardware
CPU
Memory
Radio
Mica-2
Tmote Sky
Imote2
Waspmote
2002
2005
2007
2009
ATmega128L
TI MSP430
Intel PXA271
ATmega1281
8-bit, 8MHz
16-bit, 8MHz
32-bit, 13-416MHz
8MHz
36 µW asleep
15 µW asleep
390 µW asleep
2 µW asleep
60 mW active
5.4 mW active
>31 mW active
27 mW active
4KB RAM
10KB RAM
32 MB RAM
8 KB RAM
128 KB Flash
48 KB Flash
32 MB Flash
128 KB Flash
CC1000
CC2420
Xbee-802.15.4
76 Kbps
250 Kbps
250 Kbps
100 µW sleep
60 µW sleep
<30 µW sleep
36 mW receive
63 mW receive
150 mW receive
75 mW xmit
57 mW xmit
135 mW xmit
2ms setup
1ms setup
2ms setup
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Companies
Crossbow
TelosB, MICAZ, Eko nodes
Sentilla (formerly MoteIV)
Tmote, JCreate
Sensinode
Nanostack and nanorouter
Libelium
Waspmotes and Meshlium.
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Network stack
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Great Duck Island (2002)
Photo: Peg Skorpinski
Photo: Peter Scott
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Tungurahua (2005)
http://fiji.eecs.harvard.edu/Volcano
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Ocean Tracking Network
(2010)
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http://oceantrackingnetwork.org/
Network stack
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Challenges: Energy
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Challenges: Energy
The radio is the most energy consuming module.
If run continuously with the radio, the lifetime is about 5 days.
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Challenges: Wireless
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Research Challenges
Time synchronisation
Multihop routing
Aggregation
Sleeping...
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Network stack
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Optimizing the MAC layer
Communication unit -> put to sleep if no
communication needed
MAC protocol -> enables communication
between neighbor nodes
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MAC layer
XMAC: Nodes’ radio unit periodically wakes
up for short period (non synchronised)
Poll is for me, I send an
Ack and wait for data
B has data for A
Poll is not for me,
I go back to sleep
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MAC layer
Classic paper S-MAC (UCLA)
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MAC layer
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MAC layer
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Read : MAC survey by Koen Langendoen
Multiple channels
If there’s a lot of traffic on the 802.11
wireless links, a lot of channels on the
802.15.4 spectrum will be unavailable
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Multiple Channels?
Multiple channels  more bandwidth
Parallel communications without
interference
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MuChMAC design
Frequency hopping scheme
Dynamic hopping scheme
Supports Broadcast & Unicast
Strict time synchronization??
Can be relaxed!
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+ Low power
Duty cycle
+ TDM (random wake-up moments in slot)
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Drift compensation
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Network stack
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Routing layer
Routing tree
Standard approach:
‘min’ route
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Routing layer
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Directed diffusion
Data centric routing based on Interests
Type=temperature
Location=[0,0,15,35]
Time= 00:00:00, 10:00:00
Interval=30s
Id=324
Type=temperature
Value=25.3°C
Location=[10,25]
Time=04:23:30
Id=323
Type=temperature
Value=23.4°C
Location=[40,25]
Time=04:23:30
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Directed diffusion
a) Query dissemination
b) Gradient setup
c) Path reinforcement
UCLA, 2000
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TinyDB (Berkeley,2002)
SQL-based querying
SELECT temperature FROM sensors
WHERE location=[0,0,15,35]
DURATION= 00:00:00, 10:00:00
EPOCH DURATION 30s
Id=324
Type=temperature
Value=25.3°C
Location=[10,25]
Time=04:23:30
Id=323
Type=temperature
Value=23.4°C
Location=[40,25]
Time=04:23:30
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Tiny Aggregation
1
2
3
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Berkeley, 2005
Tiny Aggregation
Aggregation: Fuse data along the routing tree
Example: Average of the measurements
SELECT AVG(temperature) FROM sensors
WHERE location=[0,0,15,35]
DURATION= 00:00:00, 10:00:00
EPOCH DURATION 30s
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Data aggregation
 Let si[t] be the measurement of node i at time t, 1<i<S
 Average:
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Network load
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Network load
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Data aggregation
Advantages of aggregation
Reduce communication
Network load does not depend on network size:
scalability
Changes load distribution: root node is no
longer the bottleneck
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Data aggregation
Operators:
Count, sum, average, min, max (SQL operators,
[Madden et al, 2005])
Distributed regression [Guestrin et al, 2004]
Principal component analysis [Le Borgne et al., 2009]
Middleware:
Tiny diffusion [Intanagonwiwat et al, 2000],
Tiny Aggregation (TAG) [Madden et al, 2005],
Dozer [Burri, 2007], ...
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Operating systems
TinyOS
Berkeley (1999)
NesC language
Based on components
Contiki
SICS (2003)
C language
Based on protothreads
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Part 2:
Information processing
for WSN
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Machine learning
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Machine learning:
methodology
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Modeling sensor data
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The model approximates
Modeling sensor data
the measurements with just
one parameter
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Replicated models
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Replicated models
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Replicated models
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Replicated models
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Replicated models
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Replicated models
Pros
Guarantees the observer with ɛ accuracy
Simple or complex models can be used
Cons
In most cases, no a priori information available.
Which model to choose?
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Replicated models
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Racing
At first, all models are in competition.
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Racing
As time passes, h1 statistically outperforms h6.
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Racing
h3 then outperforms h5.
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Racing
h3 is finally selected as the best one.
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Replicated models
Communication savings depends on accuracy
In practice, 50% communication savings for high
accuracy. Up to 95% savings for rough
approximations.
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Replicated models
Summary
Send model parameters instead of
measurements
Guarantees accuracy and communication
savings
Very low compuational overhead. Implemented
in TinyOS for Tmote Sky.
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Principal component aggregation
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Routing tree
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Data collection
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Data collection
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Data collection
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Data collection
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Data collection
Communication is much more important at
nodes close to the base station
The batteries of these nodes expire first
The root node is the bottleneck
Rest of the network disconnected!
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Data aggregation
 Solution: Remember aggregation
 Example: average
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Beyond average:
Principal component analysis
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Principal component analysis
PCA: Versatile technique for:
Compression,
Noise filtering,
Event detection,
Event classification
Particularly appropriate when data are
correlated (as in sensor networks)
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Principal component analysis
No noise
SNR=1
 3 phenomena, appearing
and disappearing
 The network is a 10*10 grid
of nodes
QuickTime™ and a
decompressor
are needed to see this picture.
QuickTime™ and a
decompressor
are needed to see this picture.
1 PC
QuickTime™ and a
decompressor
are needed to see this picture.
3 PCs
QuickTime™ and a
decompressor
are needed to see this picture.
 Dramatic reduction of dimensionality! Here, S=100, q=3
 Performs noise filtering
100 PCs
QuickTime™ and a
decompressor
are needed to see this picture.
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Principal component analysis
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Principal component
aggregation
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Principal component
aggregation
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Intel Laboratory monitoring
54 sensors recording temperature, humidity and light
2 months deployment.
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Intel Laboratory monitoring
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Intel Laboratory monitoring
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Intel Laboratory monitoring
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Intel Laboratory monitoring
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Part 3:
Case studies
ETRO/VUB
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Areas of Competence
Environmental sound monitoring
Data Aggregation
Embedded computing
OS porting – SW-HW tools
MAC and Routing protocols
Reconfigurable computing
Low power design
Contiki – Tiny OS
Cross layer optimization for
low power
wireless sensor nodes
Enabler for novel applications
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Current project:
IWT-SBO project
Intelligent
Distributed
Environmental
Assessment
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IDEA
brings
More information
at a cheaper price.
air (UFP)
tiny noise
reference
noise
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IDEA
brings
Bottom up: alerting functionality
and instant validation
sensor error
car
truck
train
nature
talking
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industry
IDEA
brings
Top down:
interpolation and detailed querying
i
LAden = 64 dBA
details
origin
PM10 = 34  g/m3
details
origin
UFP = ...
details
origin
Origin:
Quality : medium
Interpolation based on traffic data
Closest measurement : 120 m
Closest high-end : 890 m
Time stamp : 1-2-2009 to 1-3-2009
...
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IDEA
brings
Top down:
interpolation and detailed querying
Tracing sensor details
Start: 6/3/2009 11:22
Location: //serv1.gent.be/tr23.xml
Tracing sensor details
Start: 6/3/2009 11:23
Location: //serv1.gent.be/tr24.xml
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IDEA: Main work areas
 Work will concentrate on 3 main work
areas:
1. Hardware level enhancements for mobile
wireless sensor nodes
2. Network level enhancements and protocol
design for self organizing wireless networks
3. Setup of a demonstrator as proof of concept
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Hardware Enhancements
 Single Board Computer, minimum
processing capability, minimum price/node
 System runs Windows Embedded,
Windows XP or Linux (Voyage: slimmed
down Debian)
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Outdoor tests of microphones
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Self-Testing microphone
Mic + circuitry
Schematics of aluminum casing,
finished parts and finished product.
Micro Speaker
Microphone design
with screw able cap,
10mm micro speaker
inside cap.
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Field test of Self-Testing Microphone
Ref Mic
Test Mic
Added windshield for
outdoor testing. Results
compared with reference
microphone.
Self-Testing system first
version has a separate jack
for the microphone
and
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speaker.
System Architecture
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System Architecture
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ISEM
Intelligent Sensorwebs for
Environmental Monitoring
IWOIB project: Prospective Research for Brussels
Start date: March 2010
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Challenges
 A breakthrough in the creation of Urban
sound-level monitoring on a big scale with:




High spatial and temporal accuracy
Heterogeneous nodes
Ultra low-cost autonomous nodes (20€ to 50€)
Use public RF band to lower communication
cost
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Integrating smaller nodes:
ISEM
250 W
5W
0.05 W
0.05 W
+
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Adding computing power
to the Sentilla motes
Static Hybrid RC systems
Two phase operation
Configuration
Computation
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Adding embedded energy
scavenging to the motes
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Adding embedded energy
scavenging to the motes
Harvesting Method Power Density
Solar Cells
15 mW/cm3
Piezoelectric
330uW/cm3
Vibration
116uW/cm3
Thermoelectric
40 uW/cm3
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ISN: Applications for WSN’s
 Interoperable Sensor Networks
 Explore/improve existing standards in
WSN world and build application test
cases
 ITEA Consortium
 O&O project IWOIB, start 1st July 2010
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PROJECT DRIVERS&GOALS
ISN/EU OBJECTIVES
Simplify deployment
and integration
Simplify application
development
Simplify monitoring and
management
Enable efficient use
of data
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ISN-Brussels
WirelessHART
ISN
Box
6loWPAN
ZigBee
Gateway
Vertical markets
• Energy efficiency and comfort in building
• Renewable energy
...
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VUB Greenhouse Monitoring
30 Tmote Sky
ISN
Box
Gateway
Expected lifetime: > 6 months
Measured parameters: temperature, humidity,
light
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Acoustic Comfort Monitoring
ISN Box
Measured parameters:
•Background noise
•Reverberation time
•Speech intelligibility
•etc.
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Acoustic Comfort Monitoring
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Photovoltaic Monitoring
ISN Box
Measured parameters:
•Irradiation
•Module temperature
•Ambient temperature
•Wind speed
•etc.
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Photovoltaic Monitoring
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Conclusions
WSN today
Technology available on the market
 Time for deployment!
WSN future
Technology?
Applications?
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Thank you for your
attention!
Question?
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