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Di t ib t d I t lli t S t W9Distributed Intelligent Systems – W9:An Introduction to Wireless
Sensor Networks from a Di t ib t d I t lli t Distributed Intelligent Systems PerspectiveSystems Perspective
OutlineOutline
• Wireless Sensor Networks (WSN) as ( )a special class of DIS
• Motivating applicationsT h l• Technology
• Tools used in this course– Mica-zMica z– Zigbee-compliant module for e-puck
robots– Webots extensionsWebots extensions
• Closing the loop with multi-robot systems
ll i d i i b h k– Collective decisions as a benchmark– Multi-level modeling for WSN
A Special Class of Di t ib t d I t lli t Distributed Intelligent
Systems: Wireless Systems: Wireless Sensor NetworksSensor Networks
WSN and DISWireless sensor networks:
– are spatially distributed systemsare spatially distributed systems– exploit wireless networking as main inter-node interaction
channel– typically consist of static, resource-constrained nodes– energy saving is a crucial driver for the design of WSN
h d hi h ( i ll h i l– have nodes which can sense, act (typically no physical movement), compute and communicate in an unattended mode
Are WSN a special class of Distributed pIntelligent Systems?
WSN and DISThe potential is there but currently we observe:- Limited embedded intelligence/adaptation:Limited embedded intelligence/adaptation:
- sensing data are typically only collected for a particular application and rarely used to take local actions (e.g., change of activity pattern in a node)
- no emphasis on local perception-to-action loopactivity pattern (sensing computing networking) are typically- activity pattern (sensing, computing, networking) are typically a priori scheduled
- static nodes face lower unpredictability than mobile onesp y
- Limited control distributedness- the fact that WSN are spatially distributed does not necessarily
mean distributed control: the existence of a sink allows for centralized control which in turn often promote energy saving
WSN vs Networked Multi Robot SystemsWSN vs. Networked Multi-Robot Systems
Networking is common sensor nodes = mobile robotsNetworking is common, sensor nodes mobile robots without wheel or mobile robots = sensor nodes with wheels. So minimal difference? Not really …y
• Mobility changes completely the picture of the problem: y g p y p pmore unpredictability, noise, … .
• Self-locomotion even more so: real-time control loop at h d l l b d b kd di llthe node level + energy budget breakdown radically
different• Typically different objective functions and performance• Typically different objective functions and performance
evaluation metrics
An Early Environmental M it i D l t Monitoring Deployment:
The Importance of The Importance of NetworkingNetworking
EPFL-UNILBiotracking ProjectBiotracking Project
(Freitag, Martinoli, Urzelai, 1995-1999)( g, , , )
Goals • Understanding better the overall• Understanding better the overall
behavior of migratory Wrynecks (endangered species) and therefore actively intervene for improving his survivability
• Monitoring nest passages, hunting g p g , gmovements, environmental cues (e.g., temperature inside and outside the nest)the nest)
EPFL UNIL Biotracking ProjectEPFL-UNIL Biotracking ProjectOverview of the monitoring systemOverview of the monitoring system
≈ 30 m N t
HMUHMUHMU
HMU
≈ 200 m Nest
HMU HMUHMU HMU
NMUHMUHMUHMU
NMU
HMU = hunting monitoring unit
Hunting zone
g gNMU = nest monitoring unit
EPFL-UNIL Biotracking ProjectEPFL UNIL Biotracking Project
Hunting Monitoring UnitHunting Monitoring Unit• Active radio transponders (low
duty cycle)y y )• RSSI-based distance estimation• No networking among HMU• Energy management based on
rough estimation of bird’s habits D t ll ti ith HP• Data collection with HP calculator/laptop
• Never tested in the field with tagged wrynecks
• 1 week energetic autonomy
EPFL-UNIL Biotracking ProjectEPFL UNIL Biotracking ProjectNest Monitoring Unit• Passive Integrated Transponders (PIT), 16-
bit bound to animal’s leg• Energy management based on rough gy g g
estimation of bird’s habits and coupling of light barrier with PIT reader
• Male/female identificationMale/female identification• Data collection with HP calculator/laptop • Tested in the field with tagged Wrynecks• 1 week energetic autonomy
[Freitag, Martinoli, Urzelai, Bird Study, 2001]
LessonsCommon field experience• Birds do not usually play the game as we would like to
( fl )(camouflage, …) • Packaging: major issue (waterproof case, connectors, …)• Not low-stress monitoring (bird captured with nets, …); very
i i h i b ill l h i ?invasive technique but still … tagless techniques?• Still much better than standard human-guided radio-telemetrySpecific field issuesSpecific field issues• Limited observation window: 1 month/year for testing the
equipment in the field with tagged Wrynecks; no failure admittedIssue due to inexistent unit networkingIssue due to inexistent unit networking• Manual collection of data from local data loggers using calculator
(or laptop)N t ti ibl• No remote operation possible
• No collaborative, power-saving algorithms possible
Motivating Applications
Motivation
What if we could monitor events which …
– have a large spatial and temporal distributionrequire in situ measurements– require in-situ measurements
– take place in hard to access placest d t hi h d t b il bl i– generate data which need to be available in
real-time
Motivation
What would we need for that?A device whichA device which …
i h di t ib t f it– is cheap – so we can distribute many of it – is reliable – so we can measure for a long time
li l b / l ll d– uses little power – battery/solar cell powered– has a radio – so it can communicate– can potentially move – so it can potentially
relocate
A Scientific Motivation
• Micro-sensors, on-board processing and wirelessprocessing, and wireless interfaces all feasible at very small scale– can monitor
phenomena “up close”
• Will enable spatially and
Seismic Structure response
Contaminant Transport
temporally denseenvironmental monitoring
• Embedded networked sensing will reveal
Marine Microorganisms
Ecosystems, Biocomplexity
sensing will reveal previously unobservable phenomena
Source: D. Estrin, UCLA
Pioneering deployments –g p yThe WISARD Project
(Flikkema NAU 2001 )(Flikkema, NAU, 2001 -)
Microclimate meas ring in– Microclimate measuring in the Redwood forestImpact of fine scale– Impact of fine-scale ecological disturbances on diversityd ve s ty
– Micro-measurement of energy, water, carbon fluxesgy, ,
Pioneering deployments –Pioneering deployments Great Duck Island
• originally a 9 month deployment (2002)• 32 nodes: light temp humidity barometer32 nodes: light, temp, humidity, barometer• in 2003, added ~200 nodes with various
sensors; still activesensors; still active– www.greatduckisland.net
Application 1 - Permasense
• What is measured:– rock temperaturerock temperature– rock resistivity– crack widthcrack width– earth pressure
water pressure– water pressure
Pictures: courtesy of Permasense
Application 1 - Permasense
• Why:“[…] gathering of[…] gathering of
environmental data that helps to understand the processes that
t li tconnect climate change and rock fall in permafrost areas”in permafrost areas
Pictures: courtesy of Permasense
Application 1 - Permasense
– spatial distribution?– temporal distribution?p– in-situ measurements?– take place in hard to p
access places?– generate data which g
need to be available in real-time?
Pictures: courtesy of Permasense
Application 2 - GITEWSGerman Indonesian Tsunami Early Warning System
• What is measured:– seismic events
y g y
seismic events– water pressure
Pictures: courtesy of Deutsches GeoForschungsZentrum (GFZ)
Application 2 - GITEWS
• Why:To detect seismicTo detect seismic events which could cause a Tsunami. Detect a Tsunami and predict its propagation.
Pictures: courtesy of Deutsches GeoForschungsZentrum (GFZ)
Application 2 - GITEWS
– spatial distribution?– temporal distribution?p– in-situ measurements?– take place in hard totake place in hard to
access places?– generate data which g
need to be available in real-time?
Pictures: courtesy of Deutsches GeoForschungsZentrum (GFZ)
Application 3 - Sensorscope
• What is measured:– temperaturetemperature– humidity– precipitationprecipitation– wind speed/direction
solar radiation– solar radiation– soil moisture
Pictures: courtesy of SwissExperiment
Application 3 - Sensorscope
• Why:Capture meteorologicalCapture meteorological events with high spatial density. y
Pictures: courtesy of SwissExperiment
Application 3 - Sensorscope
– spatial distribution?– temporal distribution?p– in-situ measurements?– take place in hard totake place in hard to
access places?– generate data which g
need to be available in real-time?
Pictures: courtesy of SwissExperiment
Application 4: Seismic• Interaction between ground motions and
structure/foundation response not well understood.
C i i k i ll– Current seismic networks not spatially dense enough to monitor structure deformation in response to ground motion, to sample wavefield without spatial aliasing.
• Science– Understand response of buildings and
underlying soil to ground shaking D l d l t di t t t– Develop models to predict structure response for earthquake scenarios.
• Technology/Applications– Identification of seismic events that causeIdentification of seismic events that cause
significant structure shaking.– Local, at-node processing of waveforms.– Dense structure monitoring systems.
• WSN will provide field data at sufficient densities to develop predictive models of structure, foundation, soil response.Source: D. Estrin, UCLA
Field Experiment• 38 strong-motion seismometers in 17-story steel-frame Factor Building.38 strong motion seismometers in 17 story steel frame Factor Building.• 100 free-field seismometers in UCLA campus ground at 100-m spacing
⎜⎯⎯⎯⎯⎯⎯⎯ 1 km ⎯⎯⎯⎯⎯⎯⎜
Source: D. Estrin, UCLA
Application 5: Contaminant Transporti• Science
– Understand intermedia contaminant transport and fate in real systems.p y
– Identify risky situations before they become exposures. Subterranean d l t
Soil Zone
Water Well
deployment.• Multiple modalities (e.g., pH,
redox conditions, etc.)Volatization
SpillPath
, )• Micro sizes for some
applications (e.g., pesticide transport in plant roots)Dissolution transport in plant roots).
• Tracking contaminant “fronts”.• At-node interpretation of
i l f i k (i fi ld
Groundwater
potential for risk (in field deployment).
Source: D. Estrin, UCLA
ENS Research Implications
• Environmental Micro-Sensors– Sensors capable ofSensors capable of
recognizing phases in air/water/soil mixtures.Sensors that withstand
Contaminantplume
– Sensors that withstand physically and chemically harsh conditions.
ip
– Microsensors.• Signal Processing
– Nodes capable of real-timeNodes capable of real time analysis of signals.
– Collaborative signal processing to expendprocessing to expend energy only where there is risk.Source: D. Estrin, UCLA
Application 6: Ecosystem Monitoringpp y gScience• Understand response of wild populations (plants and animals) to habitats
iover time.• Develop in situ observation of species and ecosystem dynamics.
TechniquesTechniques• Data acquisition of physical and chemical properties, at various
spatial and temporal scales, appropriate to the ecosystem, species and habitat.habitat.
• Automatic identification of organisms(current techniques involve close-range human observation).)
• Measurements over long period of time,taken in-situ.
• Harsh environments with extremes in temperature, moisture, obstructions, ...
Source: D. Estrin, UCLA
Field Experiments • Monitoring ecosystem
processes– Imaging ecophysiology andImaging, ecophysiology, and
environmental sensors– Study vegetation response to
climatic trends and diseasesclimatic trends and diseases.• Species Monitoring
– Visual identification, tracking and pop lationtracking, and population measurement of birds and other vertebratesAcoustical sensing for
Vegetation change detection
– Acoustical sensing for identification, spatial position, population estimationestimation.
Avian monitoring Virtual field observations
Source: D. Estrin, UCLA
WSN Requirements for H bit t/E h i l A li tiHabitat/Ecophysiology Applications
• Diverse sensor sizes (1-10 cm) spatial sampling intervalsDiverse sensor sizes (1-10 cm), spatial sampling intervals (1 cm - 100 m), and temporal sampling intervals (1 ms -days), depending on habitats and organisms.
• Naive approach → Too many sensors →Too many data.– In-network, distributed information processing
Wi l i ti d t li t t i thi k• Wireless communication due to climate, terrain, thick vegetation.
• Self-Organization to achieve reliable, long-lived, operation g , g , pin dynamic, resource-limited, harsh environment.
• Mobility for deploying scarce resources (e.g., high l ti )resolution sensors).
Source: D. Estrin, UCLA
E bli T h l i Enabling Technologies and Challengesand Challenges
Enabling TechnologiesEmbed numerous distributed devices to monitor and interact with physical world
Network devices to coordinate and perform higher-level tasks
Embedded NetworkedControl system w/ ExploitControl system w/Small form factorUntethered nodes
collaborativeSensing, action
Sensing& Actuation
Tightly coupled to physical world
Exploit spatially and temporally dense, in situ, sensing and actuation
Source: D. Estrin, UCLA
Sensor Node Energy RoadmapSource: ISI & DARPA PAC/C Program
Sensor Node Energy Roadmap10,00010,000
1,0001,000
r (m
W) • Deployed (5W)
• PAC/C Baseline
Rehosting to Low Rehosting to Low Power COTSPower COTS(10x)(10x)
100100
1010ge P
ow
er • PAC/C Baseline
(.5W)
• (50 mW) --SystemSystem--OnOn--ChipChipAd PAd P1010
11Avera
g --Adv Power Adv Power ManagementManagementAlgorithms (50x)Algorithms (50x)
20002000 20022002 20042004
.1.1
(1mW)
20002000 20022002 20042004
Communication/ComputationSource: ISI & DARPA PAC/C Program
Communication/Computation Technology Projectiongy j
1999 (Bl t th 2004(Bluetooth
Technology)2004
(150nJ/bit) (5nJ/bit)C i ti (150nJ/bit) (5nJ/bit)1.5mW* 50uW
~ 190 MOPSComputation
Communication
Assume: 10kbit/sec. Radio, 10 m range.Assume: 10kbit/sec. Radio, 10 m range.
(5pJ/OP)Computation
Large cost of communications relative to computation Large cost of communications relative to computation continuescontinues
Free Space Path Loss
• Signal power decay in air:
• Proportional to the square of the distance d• Proportional to the square of the frequency f
– high frequency = high loss– low frequency = low bandwidth
Friis LawsFriis Laws
• Basic Friis law (open environment) Pr = received powerP t itt dBasic Friis law (open environment) Pt = transmitted powerGt = gain transmitting antennaGr= gain receiving antennaλ = signal wavelength g gR = distance emitter-receiver
f = c/λ!
• Modified Friis law (cluttered, urban environment)
n between 2 and 5!
Sample Layered Architecture
Resource constraints call
User Queries, External Database
constraints call for more tightly integrated layers
In-network: Application processing, Data aggregation, Query processing
Open Question:
Can we define an Ad i l G R i
Data dissemination, storage, caching
Can we define anInternet-like architecture for such application-
Adaptive topology, Geo-Routing
specific systems??
MAC, Time, Location
Phy: comm sensing actuation SPPhy: comm, sensing, actuation, SP
Source: D. Estrin, UCLA
Design Customization and Validation
• Spatial and Temporal ScaleE t t
Systems Taxonomy
Load/Event Models
Metrics
• Efficiency– Extent– Spatial Density (of
sensors relative to stimulus)
– Data rate of stimulii
• Frequency– spatial and
temporal density f
y– System
lifetime/System resources
Resol tion/FidelitData rate of stimulii• Variability
– Ad hoc vs. engineered system structure
– System task variability
of events• Locality
– spatial, temporal l ti
• Resolution/Fidelity– Detection,
Identification• Latency
– Mobility (variability in space)
• Autonomy– Multiple sensor
modalities
correlation• Mobility
– Rate and pattern
y– Response time
• Robustness– Vulnerability to
modalities– Computational model
complexity• Resource constraints
– Energy, BW
node failure and environmental dynamics
• ScalabilityEnergy, BW– Storage, Computation
Scalability– Over space and
timeSource: D. Estrin, UCLA
Further Hardware and Software Modules used
in this Course
MICA mote family
• designed in EECS at UCBerkeley• manufactured/marketed by Crossbowmanufactured/marketed by Crossbow
– several thousand producedused by several hundred research groups– used by several hundred research groups
– about CHF 250/piecei t f il bl• variety of available sensors
MICA logical architectureMICA logical architecture
• division into 6 basic sections:division into 6 basic sections:– all we need for a simple sensor network
Fl hFlashMemory(4 Mbit)
Radio &Antenna Sensor(s)Processor
3 LEDs UART
MICAz
• Atmel ATmega128L– 8 bit microprocessor, ~8MHz
k k– 128kB program memory, 4kB SRAM– 512kB external flash (data logger)
• Chipcon CC2420• Chipcon CC2420– 802.15.4 (Zigbee)
• 2 AA batteries– about 5 days active (15-20 mA)– about 20 years sleeping (15-20 µA)
• TinyOS
Sensor board
• MTS 300 CA– Light (Clairex CL94L)Light (Clairex CL94L)– Temp (Panasonic ERT-J1VR103J)– Acoustic (WM-62A Microphone)Acoustic (WM 62A Microphone)– Sounder (4 kHz Resonator)
Operating systemAn operating system (OS) is an interface between hardware and user applications.It is responsible for the management andIt is responsible for the management and coordination of tasks and the sharing of the limited resources of the computer system.A typical OS can be decomposed into the followingA typical OS can be decomposed into the following entities:
Scheduler, which is responsible for the sharing of the processing unit (microprocessor or microcontroller)processing unit (microprocessor or microcontroller)Device drivers, which are low-level programs that manage the various devices (sensors, actuators, secondary memory storage devices, etc.). Memory management unit, which is responsible for the sharing of the memory (virtual memory).Optional: Graphical User Interface, File System, S it t
Most “OS” for embedded systems include these two
entities only!Security, etc.
y
TinyOS: description
• Minimal OS designed for Sensor Networks• Event driven executionEvent driven execution• Programming language: nesC (C-like syntax
but supports TinyOS concurrency model)but supports TinyOS concurrency model)• Widespread usage on motes
– MICA (ATmega128L)– TELOS (TI MSP430)
• Provided simulator: TosSim
The epuck ZigBee-Compliant Radio p g p
• Custom module designed specifically for short range• Software controllable (~5cm-5m)• TinyOS radio stack • Interoperable with MICAz, etc.
50
[Cianci et al, SAB-SRW06]
802.15.4 / Zigbee
• Emerging standard for low-power wireless monitoring and control– 2.4 GHz ISM band (84 channels), 250 kbps data rate
• Chipcon/Ember CC2420: Single-chip transceiver– 1.8V supply
• 19.7 mA receiving• 17 4 mA transmitting17.4 mA transmitting
– Easy to integrate: Open source drivers– O-QPSK modulation; “plays nice”
with 802.11 and Bluetooth
Comparison to other standardsComparison to other standards
Communication Plug-In for WebotsCommunication Plug In for Webots
• OmNET++ engine• OSI framework• Custom Layers
- 802.15.4 Zi B- ZigBee
• Physical communication model:- semi-radial disk with noisesemi radial disk with noise- channel intensity fading
532008-11-06 : Cianci
Collective DecisionsCollective Decisions
Collective Decisions
• A general benchmark for testing distributed intelligent algorithmsg g
• Feasible without mobility relying exclusively on networkingexclusively on networking
Understanding Collective Decisions• Local rules and appopriate amplification and/or
coordination mechanisms can lead to collective decisionsdecisions
• Modeling to understand the underlying mechanisms and generate ideas for artificial systems
Modeling
Ideas forIndividual behaviors and local interactions
Global structuresand collective
decisions
Ideas forartificialsystems
Example 1: Selecting a Path (W2)Example 1: Selecting a Path (W2)
Choice occurs randomly
(Deneubourg et al., 1990)
l 2 S l i d S ( 2)Example 2: Selecting a Food Source (W2)
Example 3: Selecting a ShelterExample 3: Selecting a Shelter• Leurre: European project focusing on mixed insect-robot
• A simple decision-making i 1 2 h l
societies (http://leurre.ulb.ac.be)
scenario: 1 arena, 2 shelters• Shelters of the same and different
darknessdarkness• Groups of pure cockroaches (16),
mixed robot+cockroaches (12+4)• Infiltration using chemical
camouflage and statistical behavioral model
[Halloy et al., Science, Nov. 2007]
behavioral model• More next week
Example 4: Selecting a DirectionExample 4: Selecting a Direction
Converging on the direction of rotation (clockwise or anticlockwise):• 11 Alice I robots• local com, infrared based• Idea: G. Theraulaz (and A. Martinoli); implementation: G. Caprari, W. Agassounon
Set up and Collective Decision AlgorithmSet-up and Collective Decision Algorithm
[Cianci et al, SAB-SRW 06]
Some Results
[Cianci et al, SAB-SRW 06]
Alternative Scenario: Networking S N d d R b tSensor Nodes and Robots
[Cianci et al, SAB-SRW 06]
Example 5:Example 5:Assessing Acoustic Events
• Non-trivial event medium– Unpredictable in both
space & time– Generality
• Applicable to other similar media• Applicable to other similar media
– Highly localized• Facilitates experimentation
– No or weak assumptions about the underlying acoustic field
642008-11-06 : Cianci
acoustic field
[Cianci et al., ICRA 2008]
The Physical Set-upe ys ca Set up
R li i f h• Realization of the general case– 1.5 x 1.5m tabletop arena
• Multiple elements– Nodes (Robots)– Radio
S d– Sound– Independent event source
[potentially mobile]
652008-11-06 : Cianci
[p y ]
Submicroscopic Model (Webots)Submicroscopic Model (Webots)• Realistic simulation in
WebotsWebots• Calibrated modules
internal to nodesinternal to nodes– e-puck
(wheels, distance sensors,…)
– Sound propagation (Image-Source)
R di i i– Radio communication (OmNET++; semi-radial disk with noise, channel intensity
662008-11-06 : Cianci
fading, OSI layers)
Dedicated Webots Pluginsg• Acoustic Model
Time delayed• Radio Model
– OmNET++ engine– Time-delayed, attenuated signals
– Image-source
– OmNET++ engine• OSI framework• Custom Layers– Image-source
reflections• Also for floor
– 802.15.4 & ZigBee
d2
d1
67
Measurement Confidence in Acoustic Event Detection
• In some situations, event d i i ldetection may trigger a costly process – (i.e. human intervention, fire ( ,
brigade, etc…)• A simple consensus mechanism
may help limit false positivesmay help limit false positives– Require k nodes to agree on
detection before reportingHere k=2 shown– Here, k=2 shown
• Test against “desirable” & “undesirable” event sources of diff l i i i i
Detected by more nodes = louder
M i i t it ith bidifferent relative intensities– Decoy = {50%, 75%, 95%}
* Target intensity
– Measuring intensity with a binary sensor
Hierarchical Suite of ModelsHierarchical Suite of Models• Macroscopic (see W6)• Microscopic
• Non-spatial (see W6)• Discrete-spatial• Continuous-spatial
• Submicroscopic (Webots + plug-ins) A area of interestA area of interest
N number of nodesD(N) distribution of nodesE events in the environment
69
Pdet(r,Ie) probability of detectionPcom(r,It) probability of communication
Performance Metric
Discrete Event Sources⎞⎛⎞⎛⎞⎛⎞⎛ E
false negatives false positives measurements messages
⎟⎟⎠
⎞⎜⎜⎝
⎛⋅⋅
−+⎟⎟⎠
⎞⎜⎜⎝
⎛⋅⋅
−+⎟⎟⎠
⎞⎜⎜⎝
⎛−+⎟⎟
⎠
⎞⎜⎜⎝
⎛=
msstotfp
fp
totE FTN
PLFTN
SEE
EEEM 1
/1
),max(1),,,( det δγβαδγβα
false negatives false positives measurements messages
70
Validation ResultsValidation Results• All four modeling levels
presented agree quite closely with the results from the real system– Avg & Std over 20 runs
shown for each:• 100 events (real & module-
based)based)• 1,000 events (continuous &
discrete spatial)• 10,000 events (discrete , (
non-spatial)– Additional experimentation
using the models should therefore remain applicable( )⎟
⎟⎠
⎞⎜⎜⎝
⎛−+=⎟
⎠⎞
⎜⎝⎛ fpdet
E EEE
EEM
max1
21
210,0,
21,
21
71
therefore remain applicable to the target system
( )⎟⎠
⎜⎝⎠⎝ totfptot EEE ,max2222
[Cianci et al., ICRA 2008]
Comparison of Execution TimesModel Speed Factor
Non-spatial Microscopic (Matlab) 90.81xDiscrete Spatial Microscopic
(Matlab)23.27x
Continuous Spatial Microscopic (Matlab)
17.07x
S b i i Mi i (C) 1 36Submicroscopic Microscopic (C) 1.36xPhysical System 1.0x
72
Potential 10x for Matlab -> C implementation
Conclusion
Take Home MessagesTake Home Messages• WSNs represent a very promising technology for a
number of applicationsnumber of applications• Commonalities and synergies between distributed,
networked multi-robot systems and WSNs are appearing but their potential need still to be further investigated and formalized
• Collective decisions represent interesting benchmarks• Collective decisions represent interesting benchmarks for testing distributed intelligent algorithms on WSNs
• A first multi-level modeling attempt for WSN has beenA first multi level modeling attempt for WSN has been carried out using a framework similar to that used for swarm robotic systems, an effort potentially allowing f f l i ti ti f liti dfor formal investigation of commonalities and synergies of hybrid robotic/static WSNs
Additional Literature – Week 9• Permasense http://www.permasense.ch• GITWES – the German Indonesian Tsunami Early Warning System
http://www.gitews.de ttp://www.g tews.deftp://ftp.cordis.europa.eu/pub/fp7/ict/docs/sustainable-growth/workshops/workshop-20070531-jwachter_en.pdf
• Sensorscope http://www.sensorscope.ch/• Mobicom 02 tutorial:
http://nesl.ee.ucla.edu/tutorials/mobicom02/• Course list:
http://www-net.cs.umass.edu/cs791 sensornets/additional resources.htmhttp://www net.cs.umass.edu/cs791_sensornets/additional_resources.htm• TinyOS:
http://www.tinyos.net/• Smart Dust Project
htt // b ti b k l d / i t /S tD t/http://robotics.eecs.berkeley.edu/~pister/SmartDust/• UCLA Center for Embedded Networking Center
http://www.cens.ucla.edu/• Intel research Lab at Berkeleyy
http://www.intel-research.net/berkeley/• NCCR-MICS at EPFL and other Swiss institutions
http://www.mics.org