Lokale Netzstrukturen

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Lokale Netzstrukturen

Einführung

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Motivation

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Moore’s Law

SS 2017 Lokale Netzstrukturen ‐Einführung 3

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Exploiting Moore‘s Law wrt. Scale

Size Number

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How to Network many Devices?

• Small (and possibly mobile devices)  wireless networking

• “Classical” wireless Networking uses base stations

–

Example: Wireless LAN

–

Example: Mobile Phones

• Why always relying on an infrastructure?

–

Less maintenance cost without relying on an infrastructure

–

Rapid installation of a network if no infrastructure is available

–

Communication would be for free

–

Not involving a far away base station may even save communication bandwidth

• Try to construct a network without infrastructure, using networking abilities of the participants

• Simplest example: Laptops in a conference room – a single‐hop ad hoc network

• More sophisticated example: multihop ad‐hoc networks

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Ad‐Hoc Networking Examples

• Factory floor automation

 Disaster recovery

 Car-to-car

communication

ad hoc ad hoc



Military networking: Tanks, soldiers, …



Finding out empty parking lots in a city, without asking a server



Search-and-rescue in an avalanche

Personal area networking (watch, glasses, PDA, medical appliance, …)

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The Wireless Sensor Network Idea

Sensor Node Sensor Network

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Example: Environmental Monitoring

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Example: Precision Agriculture

• Example: LOFAR project

– Fighting Phytophtora using micro‐climate – Temperature and relative humidity

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Example: Forest‐Fire Detection

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Example: Exploration of Unknown Territory

Example: CotsBots, Berkeley, Pister et al.

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Example: Traffic Telematics

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More Examples

Building Automation

Home Automation

Industrial Automation

Logistics

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Die Idee der drahtlosen Sensor‐Aktuator‐Netze

Beispiel Gebäudeautomatisierung

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Idee: Mobile autonome Roboter‐Sensor‐Netze

Beispiel: Überwachung eines kontaminierten Gebietes

Beispiel: Exploration von unerforschtem schwer zugänglichem Gebiet

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Idee: Kombinierte mobile Roboter‐ und Sensor‐(Aktuator)‐Netze

Mobile Roboter als drahtlose Support‐Knoten oder Data‐Mules

Mobile Roboter für Deployment und Maintenance von drahtlosen Sensor‐

(Aktuator)‐Netzen

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Herausforderungen

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Drahtlose Kommunikation = Unzuverlässige Kommunikation

LOS-Weg

NLOS-Weg



Typisches Fading‐Verhalten Hauptursache: Mehrwegeausbreitung

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Hoher Pfadverlust  Multihop

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Sender Receiver

Große Kollisionsdomänen  Multihop

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Große Kollisionsdomäne  Broadcast‐Stürme

Redundante Übertragungen

Auslieferungsrate Kollisionen &

Netzdichte niedrig

hoch

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Limitierender Faktor Batteriekapazität

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Energie‐Effizienz  Multihop

10 m

1 nJoule/Bit

…

Bluetooth‐Beispiel



100m in einem Hop: 100nJ/Bit



100m in zehn Hops: 10nJ/Bit

Distanz

Signalstärk e

100 m

100 nJoule/Bit

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Energieeffizienz  Schalf‐Wach‐Zyklen

P

_sleep

P

_active

t P

“Traditionelle” MAC-Verfahren:

P

_sleep

P

_active

P

Ein ideales energieminimales MAC-Verfahren:

Power Consumption

Power Savings

TX/RX TX/RX TX/RX

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Energieeffizienz  In‐Network‐Processing

S3 Sink: compute

max(d1,d2,d3) S1

S2

send(d1)

send(d2)

send(d3)

S3 Sink

S1

S2

send(d1)

send(d2)

compute

m = max(d1,d2,d3) send(m)

Beispiel: Maximum‐Berechnung

Kommunikationseinsparungen durch Datenaggregation

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Energieeffizient und kleine Größe  Limitierte Ressourcen

BTNode

Mica2

Mica2Dot

Tmote Sky

Serial attached Flash in kB

Internal RAM in kB

Flash RAM in kB

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Energieeffizient und kleine Größe  Limitierte Ressourcen

CC1000 CC1021 CC2420 TR1000 XE1205

Bit Rate [kbps]

76.8 153.6 250 115.2 1.2 - 152.3

Sleep Mode [uA]

0.2 - 1 (osc.

core off)

1.8 (core off) 1 0.7 0.2

RX [mA] 9.3 (433MHz) / 11.8

(868MHz)

19.9 19.7 3.8 (115.2kbps)

14 TX Min [mA] 8.6 (-20dBm) 14.5 (- 20dBm)

8.5 (-25dBm) 33 (+5dBm)

TX Max [mA] 25.4 (+5dBm)

25.1 (+5dBm)

17.4 (0dBm) 12

(+1.5dBm)

62 (+15dBm)

Source: http://www.btnode.ethz.ch/Projects/SensorNetworkMuseum

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Mobilität

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Modellbildung

Modellierung einer einzigen Verbindung

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Path Loss

S _RX

S _RX =

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Path Loss: A Geometric Explanation

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Considering Attenuation

S _RX =

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Further Effects

• Reflection & Refraction

• Diffraction

• Scattering

• Doppler Shift

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Log‐Distance Path Loss Model

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Considering Shadowing: Lognormal Model

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Modeling the Time Varying Nature

• Consider mobile sender receiver pair

• Modeling received signal strength as R.V. X

• Considering probability P[X <= x] (the CDF)

• Example: Rayleigh Fading Model

– No line of sight

– Exponential distributed CDF

• Example: Ricean fading model

– Dominant line of sight

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Modellbildung

Ein einfaches Energiemodell

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Required Transmission Power

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Zero Power with Infinite Relays?

• Direct transmission

• Transmission with n relays

s t

d

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Additive Constant Power Consumption

• Direct transmission

• Transmission with n relays

s t

d

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Modellbildung

Vereinfachte Graphmodelle

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The Wireless Network Graph

• Wireless network as a graph G=(V,E) – V = set of nodes

– E = set of node pairs which can reach each other

Example:

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Which Nodes are Connected?

• N

₀

= noise at the receiver

• S

_RX

= received signal strength

• Correct reception above certain BER:

• For constant N

₀

follows correct reception iff



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The Unit Disk Graph UDG(V)

• Nodes u and v connected iff |uv| ≤ R

• Example: transmission range of node u ?

u

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Is this correct?

• Antenna radiation patterns

• Scattering, diffraction, refraction

• Multipath propagation

• Interference

• Obstacles

u

Transmission range of u looks rather like:

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0 30 60 90 120 150 180 210 240 270 300 330 360 0

30 60 90 120 150 180 210 240 270 300 330 360

0 30 60 90 120 150 180 210 240 270 300 330 360

Measurement: LQI

Stationary Node Moving Node

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Measurement: Loss Probability

15

10

5 0 0 5 10 15

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Three Regions of Communication

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A Generalization: Quasi UDG

Of particular interest:

Transmission range varying between unique r

_min

and r

_max

r _min r _max

u

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Lokale Netzalgortihmen

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Large Scale Wireless Networks

 On board powered devices

 Wireless communication

 No network infrastructure

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Large Scale Wireless Networks

Example: sensor networks

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Large Scale Wireless Networks

Example: ad hoc networks

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Large Scale Wireless Networks

Example: robotic networks

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Large Scale Wireless Networks

 Limited comm. range

 Regulatory constraints

 Implied network graph

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Large Scale Wireless Networks

Data communication:

unicast, multicast, broadcast, anycast,

geocast, …

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Large Scale Wireless Networks

Topology control:

neighbor elimination, backbone con- struction, virtual overlays, relocation,…

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Localized Protocol Design

Localized: achieve a network wide objective with local decisions only Request neighbors

?

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Localized Protocol Design

Localized: achieve a network wide objective with local decisions only Non-localized: Local change may require a network wide decision

Inform network

General hope that localized protocols:

save resources,

support arbitrary network scale, deal better with dynamics,

still work when full network view is not possible

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Zusammenfassung

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Quo Vadis

• In der Vorlesung betrachten wir die genannten Netze/Systeme – aus der Graphen‐Sicht und

– unter vereinfachten Modellannahmen

• Pro: Beweisbare Aussagen

• Pro: Übersichtliche Algorithmen

• Contra: Algorithmen unter diesen Annahmen sind nicht praxistauglich!?!?

– Aber: Formalen Aussagen sagen uns was prinzipiell geht und was nicht

– Aber: Verfahren bilden eine gute Ausgangsbasis, um für die Praxis erweitert zu werden

• Dennoch: ganze Thematik ist ein noch junges Forschungsfeld – Es klafft eine große Lücke zwischen Theorie und Praxis

– Motivation für unsere Forschungsanstrengungen sowohl in der Theorie als auch in der Praxis

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Vorlesungsabschnitte

• Generell: wir befassen uns mit lokalen Graphstrukturen – Knoten brauchen höchstens ihre k‐Hop‐Nachbarn zu kennen

– Besondere Form: reaktive Verfahren, in denen gar kein Nachbar bekannt sein muss – Viele lokale Verfahren setzen Positionsinformation voraus

• Vorlesungsplan

– Topologiekontrolle (Entfernen von Kanten, Entfernen von Knoten, Virtuelle Overlays) – Datenkommunikation (Unicast, Multicast, Broadcast, Geocast, Anycast)