di t ib t d i t lli t s t distributed intelligent systems...

Di t ib t d I t lli t S t W10Distributed Intelligent Systems – W10: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

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 –The EPFL-UNIL Bird tracking Project

(Freitag Martinoli Urzelai 1995 1999)(Freitag, Martinoli, Urzelai, 1995-1999)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)

[Freitag, Martinoli, Urzelai, Bird Study, 2001]

EPFL-UNIL Biotracking ProjectEPFL UNIL Biotracking ProjectMonitoring Units• Nest and hunting zone• Bird tags: RFID

transponders (nest) andtransponders (nest) and active emitter (hunting)

• Energy management based on rough estimation ofon rough estimation of bird’s habits

• Male/female identification• Data collection with HP

calculator/laptop • 1 week energetic autonomy1 week energetic autonomy• No wireless networking

LessonsG l fi ld iGeneral field experience• Birds do not usually play the game as we would like to (e.g.,

camouflage issue)• Packaging: major issue (waterproof case, connectors, …)• Not low-stress monitoring (bird captured with nets, …); very

invasive technique but still … tagless techniques?q g q• Still much better than standard human-guided radio-telemetry

Specific field issuesSpecific field issues• Limited observation window: 1 month/year for testing the

equipment in the field with tagged Wrynecks; no failure admitted

Issue due to inexistent unit networking• Manual collection of data from local data loggersgg• No remotely controlled operation possible• No collaborative or centralized sink-based power-aware algorithms

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 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 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 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 ⎯⎯⎯⎯⎯⎯⎜


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).


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, ...


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


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).


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


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!

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.

42

[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

452008-11-06 : 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 (W1)Example 1: Selecting a Path (W1)

Choice occurs randomly

(Deneubourg et al., 1990)

l 2 S l i d S ( 1)Example 2: Selecting a Food Source (W1)

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 in week 12

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


Alternative Scenario: Networking S N d d R b tSensor Nodes and Robots


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

562008-11-06 : 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]

572008-11-06 : 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

582008-11-06 : Cianci

fading, OSI layers)

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• Microscopic

N ti l ( W6 d W7)• Non-spatial (see W6 and W7)• Discrete-spatial

C i i l• Continuous-spatial • Submicroscopic (formerly called module-based)

A area of interestN number of nodesN number of nodesD(N) distribution of nodesE events in the environmentPdet(r,Ie) probability of detection

60

det( e) p yPcom(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

61

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

62

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 (C) 1 36Submicroscopic (C) 1.36xPhysical System 1.0x

63

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

di t ib t d i t lli t s t distributed intelligent systems...

Documents