wireless sensor networks seminar - telecommunication networks

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Wireless Sensor Wireless Sensor Networks SeminarNetworks Seminar

Dario Rossi [email protected]

Seminar Outline• Wireless Sensor Networks (WSN)

– Brief history and overview• Application examples

– Trees, thiefs, birds, volcanos, icebergs...• The hardware

– Panoramic of available Motes– Application demo– Experimental measurements

• The software– TinyOS: Operating System– nesC: Programming Language– TOSSIM: Simulation Environment

Seminar Outline• Wireless Sensor Networks (WSN)

– Brief history and overview• Application examples

– Trees, thiefs, birds, volcanos, icebergs...• The hardware

– Panoramic of available Motes– Application demo– Experimental measurements

• The software– TinyOS: Operating System– nesC: Programming Language– TOSSIM: Simulation Environment

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• Early 1980s– Arpanet (=Internet) had 200 hosts – DARPA started the 1st WSN program on

Distributed Sensor Networks (DSN)– Mobile nodes were carried by trucks

• 21st Century– Internet has grown so big that its

tomography is a research field per se – DARPA funded the SensIT program– SmartDust sensors fit into 1 mm3

Sensor History

Sensor Keywords

• Sensor Hardware– Cheap, publicly-available, off-the-shelf

components, modular, integrated, power-efficient, extensible, tiny

• Sensor Software– Free, open-source, modular, abstract, power-

efficient, extensible, small footprint• Keywords are very similar

– This follows from high level of integration

Sensor Network Protocol Stack •Everything is integrated

–room for cross-layer optimization!

•Application requirements–drive routing choices...–and dictate hardware!

•Everything is tiny... –from the network stack...–to the operating system!

ApplicationTransportNetworkData linkPhysical

Power PlaneMobility Plane

Task Plane

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Faster, Smaller, Numerous• Moore’s Law

– “Stuff” (transistors, etc) doubling every 1-2 years

• Bell’s Law– New computing class

every 10 years

year

log

(peo

ple

per

co

mp

ute

r)

Streaming Data to/from the

Physical World

– Necessarily “cheap” • Cheap is relative:

last year 100-500$, now <100$, target << 1$– Necessarily small

• More survivable, ubiquitous, etc.– Necessarily many

• Economies of scale, finer measurement granularity. – Necessarily robust

• Common case: no maintenance– Necessarily low-power

• No battery refill, long-term applications

Sensor Devices

Sensor Network Design Factors

• Fault Tolerance of individual sensors• Scalability 102, 103, (... 106 !?)• Production Cost some cents• Hardware Constraints will likely remain• Power Consumption primary constraint• Network Topology dense and varying• Environment unattended, remote• Transmission Media RF,infrared,optical,...• Sensor Applications need specific solution

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Sensor vs Ad-hoc Networks• Sensors

– Are extremely power constrained– Have limited storage (RAM) and processing

(CPU) capabilities • Sensors are densely deployed

– Number of sensors >> ad hoc nodes – Sensors may not have global identification

• Possibly very frequent topology changes– prone to failure– application duty-cycle

Sensor Network Characteristics

• Sensor networks are composed...– By a large number of devices...– Highly cooperative...– Densely deployed... – Inside (or near) the phenomenon

• Sensor placement– Does not need to be engineered/predetermined– Random deployment on inaccessible terrains– Implies self-organizing capabilities

Sensor Network

Base-Remote Link

BaseStation

Gateway

Patch Network Motes

Transit Net

Data Service

Internet

•multi-hopcommunication

•to route data through a sink

•reachable over the internet

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Sensor Network

Base-Remote Link

BaseStation

Gateway

Patch Network MotesMotes

Transit Net

Data Service

Internet

Sensor Network

Base-Remote Link

BaseStation

GatewayGateway

Patch Network Motes

Transit Net Transit Net

Data Service

Internet

Sensor Network

Base-Remote Link

BaseBaseStationStation

Gateway

Patch Network Motes

Transit Net

Data Service

Internet

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Sensor Network

BaseBase--Remote Remote LinkLink

BaseStation

Gateway

Patch Network Motes

Transit Net

Data Service

Internet

ApplicationsApplications

Sensor Network Applications• Environment/Health

Monitoring– Habitat monitoring– Integrated biology– Structural monitoring

• Commercial, Control, Interactive– Product quality monitor– Intrusion detection– Pursuer-evader

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From cold icebergs...

K.Martinez, J.Hart, H.Ong, ''Environmental Sensor Networks,'' IEEE Computer 37(8), pg. 50-56, 2004.

...to hot volcanos...

G. Werner-Allen, K . Lorincz , M. Ruiz, O. Marcillo, J. Johnson, J. Lees, and M. Welsh. ‘‘ ’’, In IEEE Internet Computing, March/April 2006.

...to redwood trees...

36m

33m: 11132m: 110

30m: 109,108,107

20m: 106,105,104

10m: 103,102,101

Temperature vs. Time

8

13

18

23

28

33

7/7/039:40

7/7/0313:11

7/7/0316:43

7/7/0320:15

7/7/0323:46

7/8/033:18

7/8/036:50

7/8/0310:21

7/8/0313:53

7/8/0317:25

7/8/0320:56

7/9/030:28

7/9/034:00

7/9/037:31

7/9/0311:03

Date

Humidity vs. Time

3 5

4 5

5 5

6 5

7 5

8 5

9 5

Rel H

um

idit

y (

%)

1 0 1 1 0 4 1 0 9 1 1 0 1 1 1

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…to the interior of bird nests…

12:00 13:00 14:00 15:00 16:00 17:0039

39.5

40

40.5

41

Time

PIR

Tem

pera

ture

(°C

)

PIR and ambient temperature, Burrow 3, Aug. 4

12

13

14

15

16

Am

bien

t Te

mpe

ratu

re (

°C)

00:00 01:00 02:00 03:00 04:00 05:00 06:0010

15

20

25PIR and ambient temperature, Burrow 3, Jul 13

PIR

Tem

pera

ture

(°C

)

10

15

20

25

Am

bien

t Te

mpe

ratu

re (

°C)

...to vineyards...

Guess who’d collect data?

http://www.intel.com/technology/techresearch/research/rs01031.htm

…to meeting social networks…A.Chaintreau, P. Hui, J. Crowcroft, C. Diot , R. Gass, J.Scott, ‘ ’Impact of Human Mobility on the Design of Opportunistic Forwarding Algorithms’’, IEEE Infocom’06(pictures not from the paper)

wearable sensors

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…to real-time surveillance...

S. Oh, P. Chen, M.Manzo, and S. Sastry‘‘Instrumenting Wireless Sensor Networks for Real-Time Surveillance’’

…to structural monitoring.•Removing wiring lower the infrastructure cost (as well as easening the maintenance)

•Wireless sensors could replace many wires in many different contexts

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13

14

6

5`

15

118

Mote Layout

12

9

WSN Application Boundaries ?

Field Tools

SensorNetwork

Database

Internet

Data Display Monitor

Deployment

Configuration

ClientTools

Sensor Data

ConfigLog

Calib

AnalysisTools

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TinyOS World: Last Year

java

javac gcc avr-gcc, msp-gcc

nesC

Maté

Bom billa

Query VM

Tiny

DB

App

jApp

Pyth

on

Pow

Tiny

Viz

TOSS

IMscript

Panasonic

Panasonic

Panasonic

Panasonic

( )( )( )( )

...

...

Tython

TinyOS World: Today

java

javac gcc avr-gcc, msp-gcc

nesC

Maté

Bom billa

Tiny

DB

App

jApp

Pyt

Tiny

Viz

TOSS

IM

scriptPanasonic

Panasonic

Panasonic

Panasonic

( )( )( )( )

...

...

Tython

Middleware services and abstractionsMiddleware services and abstractionsDeluge, Nucleus, Drip/Drain, Marionette ...Deluge, Nucleus, Drip/Drain, Marionette ...

TinyOS World

• A few more examples:– TinyDB -> Query processor– Maté -> Virtual machine– Great Duck Island -> Real case of study

• Next time, we will dig into:– TinyOS -> Operating system– nesC -> Programming language– TOSSIM -> WSN simulator

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TinyDB: OverviewHigh level abstraction

•Data -centric programming•Prototype a declarative query processor (TinySQL)•Users treat whole network as a streaming database•Interact with sensor network as a whole

Pros and Cons•Burden shifted on the query-engine developer •Efficient, complete framework•Specialized, hard to customize

Sensor Network

TinyDB

Query Trigger

Data

S.Madden, J.Hellerstein,W.Hong, ‘‘TinyDB: In Network Query Processing in TinyOS

TinyDB: Architecture

TinyDB: ArchitectureSense

Network

Catalog

Heart MemoryQueryEngine

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TinyDB: Queries in TinySQL

[ON event]SELECT attributesFROM (sensors | buffer)WHERE predicatesSAMPLE PERIOD const | ONCE[GROUP BY expression][INTO buffer][TRIGGER ACTION command]

TinyDB: A Simple Query

SELECT nodeId, roomNo, light, noiseFROM sensorsWHERE (light<200) AND (noise<200)SAMPLE PERIOD 30s

130

108

109

110

LightLight

2

19

2

19

IdId

178251

122171

189250

155170

NoiseNoiseRoomRoomEpochEpoch

“Find free rooms for a meeting.”

TinyDB: Powerful Queries• Create named buffer in Flash memory

CREATE BUFFER nameSIZE x

• Store results in buffer, and query over bufferSELECT a1, a2,… SELECT a3, ak, … FROM sensors FROM nameSAMPLE PERIOD d SAMPLE PERIOD d2INTO name

• Actions may follow queries SELECT tempWHERE temp > 50TRIGGER alarm

Efficient and complete butspecialized and hard to customize

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Mate: OverviewHigh level abstraction

– Bytecode virtual machine, – sensor execute custom code, chunked

in network packetsPros and cons:

– Custom applications can be built– Bytecode instructions are very

expressive, arbitrary primitive set– Self-forwarding code, network

reprogramming through flooding– VM impose severe overhead– Power efficiency compromised– VM itself cannot be updated

over the air

( )( )( )( )

( )( )( )( )

constants

nesCconfvodf

TinyOs VM

Scripter

Code

.. = - - - - -

----------------------- - - -

---------- =

P r o g r a m

VMBuilder

Once

Detec

t

Timer

Recv

Events

Language

S p e c i f i c a t i o n

«tscript»

err sort filt

led sqrt castsend m o d l o g

light mean logr

Primitives

Code Pkt

P.Levis and D.Culler , ‘‘Maté: A Tiny Virtual Machine for Sensor Networks," In Proc. of 10th ASPLOS

Mate: Performance

0%

20%

40%

60%

80%

100%

0 20 40 60 80 100120140160180200220240Time (seconds)

Per

cent

P

rogr

amm

edPick t=rand(0,T)Flood C unless rx

C before t

Overhead– High for simple

operations (and)– Low for complex

primitives (send)

Network programming

Mate: Code Capsule Example

gets # Push heap variable on stackpushc 1 # Push 1 on stackadd # Pop twice, add, push resultcopy # Copy top of stacksets # Pop, set heappushc 7 # Push 0x0007 onto stackand # Take bottom 3 bits of valueputled # Pop, set LEDs to bit patternhalt #

•Increment a counter + display it on Leds

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Mate: Code Capsule Example

gets # Push heap variable on stackpushc 1 # Push 1 on stackadd # Pop twice, add, push resultcopy # Copy top of stacksets # Pop, set heappushc 7 # Push 0x0007 onto stackand # Take bottom 3 bits of valueputled # Pop, set LEDs to bit patternhalt #

•Increment a counter + display it on Leds

0x1b0xc10x060x0b0x1a0xc70x020x080x00 Flexible and expressive but

inefficient and power wasteful

Great Duck Island (GDI)

R.Szewczyk , J. Polastre, A.Mainwaring, D. Culler, ‘‘Lessons from a sensor network expedition,’’ In Proc. of EWSN’04

GDI Motivation: Leach’s Storm Petrel

• Questions– What factors make a good nest? – What are the occupancy patterns

during incubation?– And during the breeding season?

• Methodology – Characterize the climate

• inside and outsize the burrow– Collect detailed occupancy data

• from occupied and empty nest– Validate a sample of sensor data

• with a different sensing modality

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GDI Sensor NodesMotesMica @ 900Mhz25 inside, 25 outside

Sensorlight, temperature, humidity, infrared

Packagingsealant (+ acrylic tubefor outside motes)

GDI: Sensor Data Confidence?

• Useless for biologists– no new science about birds behavior

• Useful for engineers– insights on how to build sensors

GDI: Temperature and Light• Light measurements

– Uncalibrated– Binary reading– Sensor health measure

• Temperature– Reasolution

• Observed: 2 °C• Expected: 0.5 °C

– Measured temperature• Inside the enclosure• Rather than ambient• Fine on cloudy days

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GDI: Sensor Failures• Humidity

– High readings = rain, recoverable– Low readings = fatal failure (26 motes)

• Light– Loss of diurnal patterns, high reading– 7 cases, 6 correlate with humidity failures

• Temperature– Persistent reading of 0– 22 failures, all a subset of humidity failures

0 10 20 30 40 50 60 700

0.2

0.4

0.6

0.8

1

Pop

ulat

ion

attr

ition

(%

)

Time (days)

Failed humidity sensorTotal node population

days

GDI: Packet Loss Patterns


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Challanges still exists• GLACSWEB, GDI

– Sensor lasted less than expected– Packet losses, clocks drift, power

drain...

• Volcano– Network reprogramming failure

caused several long hikes

• The vineyard– Lassie the dog never came back !!

HardwareHardware

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Crossbow Mote History

$$ +

Network Embedded Systems Technology

Program

• Embedded networked sensors– Basic board has radio, processor, memory– Sandwich sensor boards in layers– Open-source hardware/software concept– Modular design for fast development

• Sensors have many usages– Application driven requirements– Hardware has to be modular and extensible

• Sensors have limited resources– Power / CPU / Memory– Hardware has to be power efficient

Hardware Design Principles

Sensor Node Components

Power Unit

ProcessorStorage

SenseUnit

ADC

Transceiver

MobilizerLocation Finder

Power GeneratorGenericNode

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Sensor Node Components

Power Unit

ProcessorStorage

SenseUnit

ADC

Transceiver

MobilizerLocation Finder

Power GeneratorSinkNode

Several Pieces of Hardware

RadioMotes

Sensing Boards

ProgrammingBoards

Processing, Connectivity

EnvironmentalData Gathering

Gateway,Development

Motes Evolution

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Radio Stack Evolution1 RENE

• RFM TR1000• 10Kbps (AM)• Exports Bit-level interface• Con: Software -intensive• Pro: Simple hardware allows

easy software optimization

3 MICA2• ChipCon 1000 (CC1K)• 38.4 Kbps (FSK)• 433/916 Mhz• Exports Byte-level interface• Hardware bit synchronization

2 MICA• RFM TR1000 • 40Kbps (FM)• 433/916 Mhz• Edge hardware detection• Byte-2- bit hardware conversion• 4x gain on the same stack

4 MICAz/TELOS• ChipCon 2420 (CC2420)• 250Kbps (QPSK/QAM)– 2.4GHz ISM band– Export packet-level interface• Hardware intensive • Direct Sequence Spread

Spectrum (DSSS)

CC2420 Radio– Standard compliance

• IEEE 802.15.4 (16 channels)• Zigbee Alliance (WiFi equivalent

in WSN context)– Fast data rate, robust signal

• 250kbps : 2Mchip/s : DSSS• 2.4GHz, Offset QPSK• -94dBm sensitivity• Low Voltage Operation (1.8V min)

– Lot of features• 128byte packet buffer• Automatic address decoding • Automatic acknowledgements• Hardware link quality indicator• Hardware encryption/authentication

•Radio vs. Flash–250kbps radio sending 1 byte

•Energy : 1.5µ J•Duration : 32µ s

–Atmel flash writing 1 byte•Energy : 3µ J•Duration : 78µ s

Power Consumption Profile

Mica2 motes

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Power Consumption Example

• MicaZ – 0.2 ms wakeup – 30 µW sleep

– 33 mW active– 45 mW radio

– 250 kbps– 2.5V min

• Telos – 0.006 ms wakeup– 2 µW sleep


– 250 kbps– 1.8V min

Supporting mesh networking with a pair of AA batteries reportingdata once every 3 minutes using synchronization (<1% duty cycle)

328 days 945 days453 days

• Mica2– 0.2 ms wakeup – 30 µW sleep


– 19 kbps– 2.5V mi

Next Generation Motes

Sensing Boards• MTS101

Basic sensor board • MTS300/MTS310

Multi sensor board • MTS400/420

Environmental monitoring and GPS (MICA2)• MTS510

Light/Accel/Microphone (MICA2DOT) • MDA300/500

Data acquisition board (MICA2, MICA2DOT)

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Sensing Boards• Data acquisition board

• Qualified with numerous external environmental probes

– Humidity– Soil moisture– PAR light– Wind speed, direction

• 8 Analog Inputs • 8 Digital Input/Output • 2 Relay Channels • Selectable Sensor Excitation of 2.5, 3, 5V

CrossbowMDA300

Sensing Boards

• Multi Sensor Board– Light, Temperature – Microphone, Sounder – Tone Detection Circuit – Accelerometer, Magnetometer (MTS310)– Compatible with MICA, MICA2, MICAz, …

CrossbowMTS300/310

Sensing BoardsCrossbowMTS310

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Programming Boards & Gateway

• MIB - Programming and Interface Boards– Provides interface between a mote and a PC– Parallel port (older)– Serial RS232 (common)– Ethernet (newer)

• Stargate - Networked Single Board Computer– Interfaces Sensor Networks to the Internet & WWW– 400 Mhz Intel Xscale running Linux based PC – Compact Flash, Ethernet, USB Host, infrared and

additional interfaces through PCMCIA.

• Programming and Serial Interface Board– Mote Connector + Serial Connector– Fast program downloading (115 Kbaud) and

communication (57.6 Kbaud) over RS-232 – Mote leds mirrored on board (easy debug) – Wall Power Supply

CrossbowMIB510



CrossbowMIB510

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• Ethernet Interface Board– Base Station/Ethernet Gateway – Remote In-System Programming – Full TCP/IP Protocol – Power Over Ethernet (POE) Ready


CrossbowMIB600

Demo Time!Demo Time!

•Compile TinyOS Application•Reprogram Motes•Getting raw data on a PC•Sensing demo•Routing demo

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Experimental Experimental MeasurementsMeasurements

Ganesan, Woo, et al

Experimental Measurements

• Evaluate performance of current technology• With special emphasis on mobilitymobility...

• …using the static scenarios results as reference

( )( ) ( )( )

( )( ) ( )( ) ( )( )

( )( )( )( )

Different speeds

Different topologies


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• Evaluate performance of current technology• With special emphasis on mobilitymobility...

( )( ) ( )( )

( )( ) ( )( ) ( )( )

( )( )( )( )

Different speeds

Different topologies

C-F.Chiasserini, C.Casetti, D.Rossi, “An empirical study on communication performance of stationary and mobile sensors”, Sensor Networks and Configuration: Fundamental, Techniques, Platform and Experiments, N.Mahalik Ed., Springer-Verlag, Germany, April 2006


Measurement Framework•• Develop a measurement Develop a measurement frameworkframework

– Transmitter/Receiver sensor application– Packet trace collection and analysis

•• Adopt a rigourous Adopt a rigourous methodologymethodology– Experiments should be repeatable

• Controlled testbed environment

– Experiment must be repeated• Topology, individual sensors, weather and daytime sampling

•• Build a measurement Build a measurement databasedatabase–– Publicy available Publicy available –– 1.5 millions packets up to now1.5 millions packets up to now

Performance Metrics• Radio channel metrics

– Received Signal Strength Indication (RSSI)

• MAC layer metrics– Number of transmission attempts

• Application level metrics– Packet loss probability– Loss burst length

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Parameters Evaluated

•• HardwareHardware Mica2 vs MicaZ•• EnvironmentEnvironment Indoor vs outdoor•• InterferenceInterference Day vs night •• TopologyTopology Circle vs array•• Network sizeNetwork size 1 to 8 sensors•• MobilityMobility Stationary vs mobile

–– StationaryStationary Distance–– MobileMobile Speed

Single Sensor Experiments:

( )( )( )( )

( )

( )

( )

( )RadiusTX RX

Single Sensor Experiments:Evidence...

( )( )( )( )

( )

( )

( )

( )RadiusTX RX

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Single Sensor Experiments:

Single Sensor Experiments:...and causes

Dynamic Array Topology

( )( )( )( )

( )( )( )( )

( )( )( )( )

( )( )( )( )

Speed vRX

…TX1 TXnTX2

RX

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Experimental Projects• Carry on similar measurements

– Comparing TelosB with MicaZ– Stress mobility by adotping motor vehicles

• Probabilistic dissemination schemes based on distance between communicating pairs– Infer distance through bayesian framework

• Investigate the impact of Forward Error Correction (FEC)– When does redundance overhead is worth ?– Look at CRC failure transmitting known random

sequences

wireless sensor networks seminar - telecommunication networks

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