Distributed Intelligent Systems – W07:An Introduction to Wireless

Sensor Networks from a Distributed Intelligent Systems Perspective

1

Outline

• Wireless Sensor Networks (WSN) as a special class of DIS

• Motivating applications• Technology• Tools used in this course

– Mica-z– 802.15.4 radio for e-puck robots– Webots extensions

• Closing the loop with multi-robot systems– Collective decisions as a benchmark– Multi-level modeling for WSN

2

A Special Class of Distributed Intelligent

Systems: Wireless Sensor Networks

3

WSN and DISWireless sensor networks:

– are 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– have nodes which can sense, act (typically no physical

movement), compute and communicate in an unattended mode

Are WSN a special class of Distributed Intelligent Systems?

4

WSN and DISThe potential is there but currently we observe:- Limited embedded intelligence/adaptation:

- sensing data are typically only collected for a particular application and rarely used to control node actions: WSN are typically data agnostic!

- no emphasis on local real-time perception-to-action loop- activity pattern (sensing, computing, networking) are typically

a priori scheduled- static nodes face lower unpredictability than mobile ones

- 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 5

WSN vs. Networked Multi-Robot Systems

Networking is common, sensor nodes = mobile robots without self-locomotion capabilities or mobile robots = sensor nodes with self-locomotion capabilities. So minimal difference? Not really …

• Mobility changes completely the picture of the problem: more unpredictability, noise, … .

• Self-locomotion even more so: real-time control loop at the node level + energy budget breakdown radically different

• Typically different objective functions and performance evaluation metrics

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Motivating Applications

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Motivation

• Micro-sensors, on-board processing, and wireless interfaces all feasible at very small scale– can monitor

phenomena “up close”

• Will enable spatially and temporally dense environmental monitoring

• Will enable precise, real-time alarm triggering

• Embedded networked sensing will reveal previously unobservable phenomena

Seismic Structure response

Contaminant Transport

Marine Microorganisms

Ecosystems, Biocomplexity

Adapted from D. Estrin, UCLA8

Pioneering deployments –The WISARD Project

(Flikkema, NAU, 2001 -)

– Microclimate measuring in the Redwood forest

– Impact of fine-scale ecological disturbances on diversity

– Micro-measurement of energy, water, carbon fluxes

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Pioneering deployments –Great Duck Island

• originally a 9 month deployment (2002)• 32 nodes: light, temp, humidity, barometer• in 2003, added ~200 nodes with various

sensors; still active– www.greatduckisland.net

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Application 1 - Permasense

• What is measured:– rock temperature– rock resistivity– crack width– earth pressure– water pressure

Pictures: courtesy of Permasense11

Application 1 - Permasense

• Why:“[…] gathering of

environmental data that helps to understand the processes that connect climate change and rock fall in permafrost areas”

Pictures: courtesy of Permasense12

Application 2 - GITEWS

• What is measured:– seismic events– water pressure

Pictures: courtesy of Deutsches GeoForschungsZentrum (GFZ)

German Indonesian Tsunami Early Warning System

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Application 2 - GITEWS

• Why:To detect seismic events which could cause a Tsunami. Detect a Tsunami and predict its propagation.

Pictures: courtesy of Deutsches GeoForschungsZentrum (GFZ) 14

Application 3 - Sensorscope

• What is measured:– temperature– humidity– precipitation– wind speed/direction– solar radiation– soil moisture

Pictures: courtesy of SwissExperiment 15

Application 3 - Sensorscope

Pictures: courtesy of SwissExperiment

• Why:Capture meteorological events with high spatial density.

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Application 4: Ecosystem MonitoringScience• Understand response of wild populations (plants and animals) to habitats

over time.• Develop in situ observation of species and ecosystem dynamics.

Techniques• Data acquisition of physical and chemical properties, at various

spatial and temporal scales, appropriate to the ecosystem, species and 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, UCLA17

WSN Requirements for Habitat/Ecophysiology Applications

• Diverse 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

• Wireless communication due to climate, terrain, thick vegetation.

• Self-Organization to achieve reliable, long-lived, operation in dynamic, resource-limited, harsh environment.

• Mobility for deploying scarce resources (e.g., high resolution sensors).

Source: D. Estrin, UCLA18

Enabling Technologies and Challenges

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Enabling Technologies

Embedded Networked

Sensing& Actuation

Control system w/small form factoruntethered nodes

Exploitcollaborativesensing, action

Tightly coupled to physical world

Embed numerous distributed devices to monitor and interact with physical world

Network devices to coordinate and perform higher-level tasks

Exploit spatially and temporally dense, in situ, sensing and actuation

Source: D. Estrin, UCLA20

Sensor Node Energy Roadmap

2000 2002 2004

10,000

1,000

100

10

1

.1

Aver

age

Pow

er (m

W)

• Deployed (5W)

• PAC/C Baseline (.5W)

• (50 mW)

(1mW)

Rehosting to Low Power COTS(10x)

-System-On-Chip-Adv Power ManagementAlgorithms (50x)

Source: ISI & DARPA PAC/C Program

21

Communication/Computation Technology Projection

Assume: 10kbit/sec. Radio, 10 m range.

Large cost of communications relative to computation continues

1999 (Bluetooth

Technology)2004

(150nJ/bit) (5nJ/bit)1.5mW* 50uW

~ 190 MOPS(5pJ/OP)

Computation

Communication

Source: ISI & DARPA PAC/C Program

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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

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Generalization: Friis Laws

• Basic Friis law (open environment) Pr = received powerPt = transmitted powerGt = gain transmitting antennaGr= gain receiving antennaλ = signal wavelength R = distance emitter-receiver

• Modified Friis law (cluttered, urban environment)

n between 2 and 5!

f = c/λ!

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Communication Power Budget: Not only Transmission

• In general transceiver power consumption dominated by listening (radio on)

• ChipCon CC2420: 19.7 mA in receiving, 17.4 mAtransmitting @ 0 dBm (2.4 GHz)

• SemTech SX 1211: 3 mA in receiving, 25 mA @ +10 dBm in transmitting (900 MHz)

• Synchronization is key• Low power listening protocols are key for

saving power 25

Hardware and Software Modules used in this

Course

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MICA mote family

• designed in EECS at UCBerkeley• manufactured/marketed by Crossbow

– several thousand produced– used by several hundred research groups– about CHF 250/piece

• variety of available sensors

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MICAz

• Atmel ATmega128L– 8 bit microprocessor, ~8MHz– 128kB program memory, 4kB SRAM– 512kB external flash (data logger)

• Chipcon CC2420– Respect 802.15.4 at the physical/MAC layer and

therefore can support Zigbee-compliant stacks • 2 AA batteries

– about 5 days active (15-20 mA)– about 20 years sleeping (15-20 µA)

• TinyOS 28

Sensor board

• MTS 300 CA– Light (Clairex CL94L)– Temp (Panasonic ERT-J1VR103J)– Acoustic (WM-62A Microphone)– Sounder (4 kHz Resonator)

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TinyOS: description

• Minimal OS designed for Sensor Networks• Event-driven execution• Programming language: nesC (C-like syntax

but supports TinyOS concurrency model)• Widespread usage on motes

– MICA (ATmega128L)– TELOS (TI MSP430)

• Provided simulator: TosSim30

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The epuck 802.15.4 Radio

• Custom module designed specifically for short range• Software controllable (~5cm-5m)• Radio stack implemented in TinyOS (not fully ZigBee

compliant) • Interoperable with MICAz, etc.

[Cianci et al, SAB-SRW06] 31

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 transmitting

– Easy to integrate: Open source drivers– O-QPSK modulation; “plays nice”

with 802.11 and Bluetooth32

Comparison to other standards

33

Communication Plug-In for Webots

342008-11-06 : Cianci

• OmNET++ engine• OSI framework• Custom Layers

- 802.15.4 - ZigBee

• Physical communication model:- semi-radial disk with noise- channel intensity fading

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An Example of Mobile Sensor Network:

The Wireless Connected Swarm

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The Wireless Connected Swarm• Idea: using the communication channel as a crude

binary sensor (“I can communicate” or “I cannot communicate”)

• Two algorithms – α-algorithm: maintain the number of direct connections

around α (parameter)– β-algorithm: maintain a minimal node connectivity

regulated by β (parameter)• Add the possibility of sensing an environmental cue

(e.g., light) to modulate the individual nodes’ β parameter and generate heterogeneity in the swarm and therefore targeted movement (indirect distributed taxis) 36

The α-algorithm

37

In simulation: • radial disk communication• Proximity sensors for robot

avoidance• Unbounded arena

A: connected robots, different headingB: unconnected robotsC: 180º turns for reacquiring connection D: new random heading

B+C+D A

The α-Algorithm: Microscopic Simulator

38[Nembrini et al, SAB 2002]Note: fragility of the algorithm in maintaining connectivity

The α-Algorithm: Webots and Real Robots

39[Pereira et al, IROS 2013]

The β-algorithm

40β = 1 β = 4

• Connection recovery (coherence maneuver) as before but based on a different communication-based perceptual input

• If a robot (A) lose the connection with another specific node (B), it looks of how many of its neighboring nodes (C and D) still have this specific node (B) as neighbor

• If this number is less or equal than β than it start a coherence maneuver; once recovered random heading

The β-algorithm: Microscopic Simulator

41[Nembrini et al, SAB 2002]

Note: red robots perceive light and raise their β to infinite

Collective Decisions

42

Collective Decisions

• A general benchmark for testing distributed intelligent algorithms

• Feasible without mobility relying exclusively on networking

43

Modeling

Individual behaviors and local interactions

Global structuresand collective

decisions

• Local rules and appopriate amplification and/or coordination mechanisms can lead to collective decisions

• Modeling to understand the underlying mechanisms and generate ideas for artificial systems

Ideas forartificialsystems

Understanding Collective Decisions

44

Choice occurs randomly

(Deneubourg et al., 1990)

Example 1: Selecting a Path (W1)

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Example 2: Selecting a Food Source (W1)

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[Halloy et al., Science, Nov. 2007]

• A simple decision-making scenario: 1 arena, 2 shelters

• Shelters of the same and different darkness

• Groups of pure cockroaches (16), mixed robot+cockroaches (12+4)

• Infiltration using chemical camouflage and statistical behavioral model

• More in week 13

Example 3: Selecting a Shelter• Leurre: European project focusing on mixed insect-robot

societies (http://leurre.ulb.ac.be)

47

Example 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

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[Cianci et al, SAB-SRW 06]

Set-up and Collective Decision Algorithm

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Some Results

[Cianci et al, SAB-SRW 06] 50

Alternative Scenario: Networking Sensor Nodes and Robots

[Cianci et al, SAB-SRW 06] 51

522008-11-06 : Cianci

Example 5:Assessing Acoustic Events

• Non-trivial event medium– Unpredictable in both

space & time– Generality

• Applicable to other similar media

– Highly localized• Facilitates experimentation

– No or weak assumptions about the underlying acoustic field

[Cianci et al., ICRA 2008] 52

532008-11-06 : Cianci

The Physical Set-up

• Realization of the general case– 1.5 x 1.5m tabletop arena

• Multiple elements– Nodes (Robots)– Radio– Sound– Independent event source

[potentially mobile]

53

542008-11-06 : Cianci

Submicroscopic Model (Webots)• Realistic simulation in

Webots• Calibrated modules

internal to nodes– e-puck

(wheels, distance sensors,…)

– Sound propagation (Image-Source)

– Radio communication (OmNET++; semi-radial disk with noise, channel intensity fading, OSI layers)

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Measurement Confidence in Acoustic Event Detection

• In some situations, event detection may trigger a costly process – (i.e. human intervention, fire

brigade, etc…)• A simple consensus mechanism

may help limit false positives– Require k nodes to agree on

detection before reporting– Here, k=2 shown

• Test against “desirable” & “undesirable” event sources of different relative intensities– Decoy = {50%, 75%, 95%}

* Target intensity

Detected by more nodes = louder

– Measuring intensity with a binary sensor

55

56

Hierarchical Suite of Models• Microscopic

• Non-spatial (see also Week 8)• Discrete-spatial• Continuous-spatial

• Submicroscopic (called module-based in ICRA 2008)

A area of interestN number of nodesD(N) distribution of nodesE events in the environmentPdet(r,Ie) probability of detectionPcom(r,It) probability of communication

56

57

Performance Metric

false negatives false positives measurements messages

⋅⋅

−+

⋅⋅

−+

−+

=

msstotfp

fp

totE FTN

PLFTN

SEE

EEEM 1

/1

),max(1),,,( det δγβαδγβα

Edet : the number of events reportedEtot : the total number presentedEfp : the number of false positives reportedN : the number of nodes in the networkS : total number of measurements (whole network)T : length of experiment (time)Fs : sampling frequencyLs : samples per measurementP : total number of messages (whole network)Fm : maximum message transmission rate 57

58

Validation 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)• 1,000 events (continuous &

discrete spatial)• 10,000 events (discrete

non-spatial)– Additional experimentation

using the models should therefore remain applicable to the target system

( )

−+=

totfp

fp

tot

detE EE

EEEM

,max1

21

210,0,

21,

21

[Cianci et al., ICRA 2008] 58

59

Comparison of Execution TimesModel Speed Factor

Non-spatial Microscopic (Matlab) 90.81xDiscrete Spatial Microscopic

(Matlab)23.27x

Continuous Spatial Microscopic (Matlab)

17.07x

Submicroscopic (C) 1.36xPhysical System 1.0x

Potential 10x for Matlab -> C implementation59

Conclusion

60

Take Home Messages• WSNs represent a very promising technology for a

number 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 for testing distributed intelligent algorithms on WSN (static and mobile)

• A 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 for formal investigation of commonalities and synergies of hybrid robotic/static WSNs 61

Additional Literature – Week 7• Permasense http://www.permasense.ch• GITWES – the German Indonesian Tsunami Early Warning System

http://www.gitews.de ftp://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.htm• TinyOS:

http://www.tinyos.net/• Smart Dust Project

http://robotics.eecs.berkeley.edu/~pister/SmartDust/• UCLA Center for Embedded Networking Center

http://www.cens.ucla.edu/• Intel research Lab at Berkeley

http://www.intel-research.net/berkeley/• NCCR-MICS at EPFL and other Swiss institutions

http://www.mics.org62