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SMART DUST

Outline

Overview of Sensor NetworksSmartdust NetworksRadio Smartdust NetworksSimulation ResultsConcluding Remarks

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Modern sensor nodes Smart Dust Sensor

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Wireless Sensor Networks

Processor Memory

Sensor

Interconnect

Radio

Communication

Computation

Data Storage

Sensing

Base station

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Applications of sensor networks

Motivating application: monitoring Mars (ΑΡΗΣ)Environmental monitoring:

Air, soil and water controllingHabitat and complex systems monitoringSeismic detectionVulcan activity detectionToxic waste control

Military surveillanceDemining action (ΑΦΑΛΑΤΩΣΗ)

« Networked sensors will revolutionize information gathering and processing both in urbanenvironments and in inhospitable terrain »

Challenges

Ad hoc deployment

Unattended operation

Untethered (χαλαρά !)

Dynamic changes

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Focus

Energy efficiencyLocalizationRouting

Energy in Wireless Networks

Processor Memory

Sensor

Interconnect

Radio

CommunicationEnergy

ComputationEnergy

Data StorageEnergy

SensingEnergy

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Energy efficiency

Computing subsystem (nJ/instruction)Communication subsystem (nJ/bit)Sensing subsystem (nJ/sampling)Power supply subsystem (1J/mm3)

Localization

Localization schema

Sensors with local positionBase station receives information

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Localization techniques

Coarse-grainedProximity based localization: trilaterationLinear programming techniquesBeacon placement techniques

• Random, Max, Grid

Fine-grainedTimingSignal strengthDirectionality

Routing

Flat routing protocolsSAR (Sequential Assignment Routing)Directed DiffusionSPIN (Sensor Protocols for Information via Negotiation)Adaptive Local Routing Cooperative Signal Processing

• Noncoherent Processing• Coherent Processing

Hierarchical Routing ProtocolsLEACH (Low Energy Adaptive Clustering Hierarchy)TEEN (Threshold sensitive Energy Efficient Sensor Network Protocol)PEGASIS (Power-Efficient Gathering in Sensor Information Systems)Two level clustering algorithm

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Example of routing protocol

The generated data is named by attributesThe sink request data by interest Intermediate nodes propagate the interestGradients are established for the interests expressed by the sink

Sink

Source

Event

InterestGradients

Data

Direct diffusion:

Diffusion protocols

Deficiencies of diffusion protocols:

A

B C

D

(a) (a)

(a) (a)

No clustering

Reverse communication

Implosion

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Outline 2

Overview of Sensor NetworksSmartdust NetworksRadio Smartdust NetworksSimulation ResultsConcluding Remarks

Analogy Buffon 1

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Buffon 2 Estimation of π

r

Area circle/Area square

= πr2 / 4r2 = π/4

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Mote The Smart Dust Project

Massively distributed sensor networkSensor = mote

sensing systemcomputational abilitypower supplylaser communication

Motes are inexpensive enough to be deployedby the millions.

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Problem:

The missing ingredient is the networking

LocalizationRoutingEnergy efficiency

Random topology makes the problemextremely difficultThe large number of motes makes it worse

Maybe not …

It is known that probabilistic methods and asymptotic behavior combine well…

If we could find the right model …

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Modeling the network

Random graph G(n,p)

Problem:

it does not capture the communication capability ofthe motes

Our model

Random scaled sector graphs

A mote can orient its laser beam in any direction of a prescribed scanning area of α radians and distance r

Directed Graph Model.

αr

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More realistic Distribution of motes

Motes are distributed at random but we need a reference system !!!!

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Precision and scalability

Imaginary squared gridof s cells (size s-1/2 vs s-1/2 )

This grid gives thereference position for the motes

s is the sensing precisionof the networks s controls the scalability of the network

We must guarantee atleast one mote per cell

Parameters of a Smart Dust Net

n: number of motesr: laser range of the

motesα: laser scanning angles: number of cells

po: probability mote isoperativepb: prob. motescommunicates with the BTSpc: prob. that two motes are aligned

BTS: Base station

Terrain = grid

αr

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Observations

Communication is unidirectional

We must assure minimum s and n to guarantee:

CoverageConnectivityBroadcast/routing algorithms

Our results: coverage

SizeTo assure complete coverage, n should be at

least

n = ((1+ ε) /po ) (s ln s)

with ε a constant depending on the parameters of the moteTherefore, n = O (s ln s)

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Our results: connectivity

ConnectivityIf

n = O (s ln s)with high probability:

For all x and y there is a directed path from x to y

and another from y to x.

Localization algorithms

Motes communicating with BTS receive theirposition

Motes knowing their position performs a scanning along its angle sending coordinates

Every mote that does not know its coordinatewaits to receive four positions and computescoordinates

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Observations

The probability that after a constant number of steps all operative interior motes know their position, rapidlyapproaches 1.

For instance for po = 0.75

For 1000 motes after 5 steps: 91.9% of motes know their positionFor 2000 motes after 5 steps: 96.2% of motes know their positionFor 5000 motes after 5 steps: 98.1% of motes know their positionFor 10000 motes after 5 steps: 98.2% of motes know their positionFor 15000 motes after 5 steps: 98.3% of motes know their position

Broadcasting BTS to motes

A mote has an information that must becommunicated to all the remaining motes in the network

Broadcast:

All motes at level i perform a full scan sending the message and its level number and sleeps

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Routing motes to BTS

A set of communicating motes have acommunication system that allows them tocollect and send information to a master monitoring system.

Other motes will send their measurement and act as routers.

Energy consumption

Broadcast:The energy consumption per mote is that of a scanning plus the energy lost in awakening and sleeping.

Routing:The energy is bounded by a scanning per communicating mote plus transmission to the BTS

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Outline 3

Overview of Sensor NetworksSmartdust NetworksRadio Smartdust NetworksSimulation ResultsConcluding Remarks

Radio Communication Model

Optical vs Radio Transmission:Sector (α)Circle (R)Line (L)

L

R

α

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Collisions

Every mote in the red area has interference

How to deal with collisions?

How to deal with collisions?

Direct approach is impossible

Geometric approach becomes too complex

To take profit of the random power !

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Our broadcast with collisions

Basic principle:

If a mote receives a message without collisionResends the messageTurns itself off

else the other alive motes just wait

Energy consumption: only one operation per mote

Our probabilistic broadcast

Basic principle:If a mote receives a message without collision

then with probability p• resends the message• turns itself off

else with probability 1-p • just wait

else all the other alive motes just waitEnergy consumption: only one operation per moteLess collisions but 1/p slowdown

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Our ln n broadcast

Basic principle:If a mote receives a message without collision then chooses randomly a k smaller than ln n

• waits for clock cycle number k• sends the message• turns itself off

else all the other alive motes just wait

Energy consumption: only one operation per moteLess collisions and only ln n slowdown

Outline 4

Overview of Sensor NetworksSmartdust NetworksRadio Smartdust NetworksSimulation ResultsConcluding Remarks

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n=2000, r=0.1, 30° K=2

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K=3 K=4

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K=5 K=6

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K=7 K=8

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K=9 K=10

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K=11 K=12

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K=13 K=14

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K=15 K=16

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K=17 K=18

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K=19 K=20

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K=21 K=22

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K=23 K=24

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Last step K=25 2000, 0.1, 30°, 0.75

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K=2 K=3

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K=4 K=5

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K=6 K=7

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K=8 K=9

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K=10 K=11

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K=12 K=13

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K=14 K=15

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K=16 K=17

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K=18 K=19

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K=20 K=21

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K=22 K=23

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K=24 K=25

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K=26 K=27

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K=28 K=29

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K=30 Last step K=31

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N=2000, 0.1, 30°, 0.5 K=2

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K=3 K=4

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K=5 K=6

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K=7 K=8

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K=9 K=10

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Last step K=11 N=2000, 0.1, 30, ln n

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K=2 K=3

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K=4 K=5

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K=6 K=7

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K=8 K=9

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K=10 K=11

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K=12 K=13

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K=14 K=15

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K=16 K=17

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K=18 K=19

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K=20 Last step K=21

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N=2000, 0.1, 360°, ln n K=2

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K=3 K=4

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K=5 K=6

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K=7 K=8

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K=9 K=10

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K=11 K=12

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K=13 K=14

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K=15 K=16

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K=17 K=18

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K=19 K=20

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K=21 K=22

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K=23 K=24

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Last step K=25 Outline 5

Overview of Sensor NetworksSmartdust NetworksRadio Smartdust NetworksSimulation ResultsConcluding Remarks

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Open research areas

Distributed processingData organized systemsMassive programming

Specialized algorithmsAggregationAdaptative reliabilityEnergy mappingAutoconfiguration

Synchronization issuesExperimental infrastructureEvolutive modeling

Very rich research area

When we imagine this

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And see this Other people see this