bio-inspired research for a new generation...

Copyright © 2010 National Institute of Information and Communications Technology. All Rights Reserved.

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Bio-Inspired Research For a New

Generation Network

Dr. Hideo MIYAHARA

President of National Institute of Information

and Communications Technology (NICT),

Japan

Prof. Naoki WAKAMIYA

Graduate School of Information Science and

Technology, Osaka University, Japan

ISITCE 2010

19-20 August 2010, Pohang, Korea


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Cadillac ‘59

Chevrolet ’59

Mercury

Subaru 360 ‘59

Ford Thunderbird ‘59

Car development


3

Higher global air

and ocean

temperatures.

Rising global

average sea level.

Less snow and ice.

Ch

an

ges:

1961-1

990

Source : IPCC Fourth Assessment Report (AR4) "Climate Changes 2007,“http://www.ipcc.ch/

Climate Changes


4

0

1

2

3

4

5

6

7

8

9

10

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

3billon

4billon

5billon

6billon

7billon

8billon

9billon

Popula

tion(B

illio

ns)

Year

Developed countries

Less-developed countries

Source : Statistics Bureau , Ministry of Internal Affairs Communications , JAPAN

World Population:1950-2050


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ARPA NETWORK (’69)

UCLA

UC Santa Barbara

University of Utah

Stanford Research Institute(SRI)

adapted from “Casting the Net”

Birth of the Internet


6

Nobody dreamed of the Internet society.

81

2US$

100M

0

0

192

120US$

1300M

2,500M

542M

UN Member States

Oil Prices(per barrel)

Telephone Subscribers

Mobile Phone Subscribers

Hosts registered to DNS

Will the Internet in the next 50 years

meet our demands?

Year 1958 2008

(1980s～)

(1969～)

50 years ago...


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How can we overcome global challenges

to accomplish sustainable development ?

Environmental changes

Population Explosion

Other problems and crisis facing the future

ICT can contribute to overcoming

global challenges as a key technology

for sustainable development.C

Next 50 years : Big Challenge


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1800 billion GB2011

281 billion GB2007

32 billion GB2003

6.2 billion GB2000

20001990

1980

1970

40000

BCE

billion GB

Source : International Data Corporation ,“The Diverse and Exploding Digital Universe ” ,2008

Source : Horison Information Strategies “cited from Storage New Game New Rules ”

40,0

00

BC

Eca

ve

pa

inting

105

paper

1450

printin

g p

ress

1870

tele

ph

on

e

1950

co

mp

ute

r

1970

inte

rnet D

AR

PA

1993

Wo

rld

wid

e W

eb

Information Explosion

1800

1600

1400

1200

1000

800

600

400

200

0


9

0 10 20 30 40 50 60

Relationship between Gross Income Per Capita and Internet Utilization Rate

High Income Countries10,726US$~

Upper-middle Income Countries3,466~10,725US$

Lower-middle Income Countries1,876~3,465US$

Low Income Countries~1,875US$

Source : WHITE PAPER Information and Communications in Japan,2007

%

Internet Utilization Rate


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cisco data seat

Power Consumption

=

one nuclear power plant generation

Peta bit ClassIf We made “Peta bit Class Router” with existing technologies

Required Switching Ability

of Core Router in 2020

10,000KWx100

1,000,000KW

Increased power consumption -Huge Router-


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Support sustainable

development of society

Realize safety and

security of society

Support the lives of the

handicapped

Support future

knowledge society

Social Infrastructural Point

Compatible to the future

service and application

Compatible during

disasters and

emergencies

Capable to become the

intellectual foundation

Meet the Green ICT’s

requirements

Technological Point

Requirements for the Ideal New Generation Network


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Is Current Internet Really Simple?

KISS principle “Keep It Simple, Stupid”

Everything on IP, IP on Everything

Today’s optimization is tomorrow’s bottleneck

Overlay Network

email WWW VOIP...

SMTP HTTP RTP...

TCP UDP…

IP

Ethernet PPP…

SONET WDM...

copper fiber radio...

MPLS／GMPLS

Mobile IP

IPSecRSVP(-TE)

HierarchicalAddressing

NAT

DHCP

Multicast/Anycast


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NICT’s Vision for NWGN

Human wisdom

Knowledage society

Quality of life

Productivity

Max imize

The Po tential

New diverse and inclusive collaborations

Energy issues

Medical issues

Inequality

Aging society withFewer children

Food issues

Mini mizeThe Ne gatives

Solving Emerging Social

Issues:

Establish new societal systems

Establish a sustainable society

Minimize environmental impact

Improve disaster management

…

Creating New Values:

Promote the wisdom of

human beings

Improve quality of life

Promote innovation

Inclusion:

Respect diversity in civiliz-

ation, culture, and people

Involve people in ICT on a

global scaleEnergy

Natural disaster

Medical

Food shortage

Accident

Anticrime

City- country gap

Intl economic gap

Aging society

Education

Cyber Security

Culture & life diversity

Knowledge society

Media fusionnew-value distribution

Better productivity

e-democracy

Entertainment

Frontiers


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IP NGN

NWGN?Future Internet?

New Direction


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Bio-inspired Network Control

Expect to learn robustness, adaptability and self-

organizing properties of biological systems

Biologists point out the adaptability to the

changing environments, and as a result

robustness in the biological systems is excellent.

• while, it is rather slow

Incorporate the self-organized and autonomous

mechanism in biology into the communication

network

Ref. The behavior of natural systems may appear unpredictable and

imprecise, but at the same time living organisms and the

ecosystems in which they live show a substantial degree of

resilience. (“Toward Self-Organizing, Self-Repairing and Resilient

Large-Scale Distributed Systems,” A. Montresor et al. Technical

Report UBLCS-2002-10, Sept. 2002.15


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Bio-inspired Examples

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Overlay Network Symbiosissymbiosis of different cells, organisms,

groups, and species

Reaction-Diffusion based Control Scheme for Sensor Networks

pattern formation on the surface of the body of an emperor angelfish

Waveform Synchronized Data Gatheringsynchronized flashes in a group of fireflies

Scalable Ant-based Routing Schemeforaging behavior of ants

Scalable Congestion Control for Transport Layer Protocol

Lotka-Voltera model based on ecosystems

For detailed information, visit at http://www.anarg.jp/

http://www.koze950.com/wp-content/uploads/2007/08/wolves.jpg


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Sensor Networks

Sensor nodes are equipped with sensor (heat, temperature), wireless transmitter, battery unit

Applications：Health and welfare (vital signs, safety)Crime prevention and securityDisaster prevention (fire, landslide, flood, earthquake)Environment (weather, water/air pollution)

Requirements:large number of nodes requireddeployed in an uncontrolled and unorganized waynot be halted due to depletion of the battery or failure

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MOTE2Crossbow Technology, Inc.


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Periodic Data Gathering

Collect sensor information from all sensor nodes at regular intervals

Save energy consumption by multi-hop communication

sensor information propagates from the edge to the base station

Each node receives information from more distant nodes, aggregates it with its own information, and sends it to the next node

Information is propagated in concentric circles

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base station


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Synchronized Data Gathering

A group of fireflies flashes synchronouslyEach firefly decides its timing of flashing by observing its surroundings (flashing of neighboring fireflies) fully-distributed and self-organizingBy adopting the mechanism, sensor nodes come to synchronization without any centralized control

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pulse-coupled oscillator model

a set of oscillators O = {O1, ..., ON}phase-state function

xi = fi(i ) with fi:[0,1] → [0,1] and i = 1, ..., N

stimulationxi → min(xi +e,1)


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Stimulus

Pulse-Coupled Oscillator Model

A set of oscillators O = {O1, ..., ON}Oscillator Oi has phase i [0,1] and state xi [0,1]

xi = fi(i ) with fi:[0,1] → [0,1] and i = 1, ..., NWhen state xi reaches 1, the oscillator firesA coupled oscillator Oj is stimulated and raises its stateWhen oscillator Oj also fires from stimulus, both are synchronized

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e

e

time time

state

StimulusO1O2


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Sensor Network

The proposed method can collect sensor information from a large number of randomly distributed sensors at regular intervals in an energy-efficient way

simple and easy to implementfully-distributed and self-organizinglonger lifetime of a sensor networkno initial setting of sensor nodes and no careful planningadapts to addition, removal, and movement of sensor nodesadapts to changes in frequency of data gathering

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Reaction diffusion-based self-organization

Pattern and structure are observedand organized in communication network

Autonomous and distributedstructure & pattern organization

is required

Structure and pattern in network Autonomous pattern generation

Periodic pattern is self-organized onsurface of biological systems

Reaction-Diffusion Model

mathematical formulation

Self-organization of structure & pattern

through local interaction

routingspatial channel reuse

grouping, clustering

vDvuGt

vuDvuF

t

uvu

22 , ,,

reaction term diffusion term

temporal dynamics of chemical reactionof morphogen substrates


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Attractor selection-based adaptive communication

Network should keep providing servicesunder dynamically changing and even unpredictable operational environment

Adaptive and robustnetworking technologies

are required

Adaptive communication Biological adaptation

Bacteria choose nutrient to synthesizein adaptation to environmental condition

Attractor Selection Model

mathematical formulation

xfdt

dx

Self-adaptation of communication and networking

to changing & unknown environment

Dynamic changes in communication• Unstable wireless link• User churn• Mobility of nodes• Node and link failure• Interference among competing networks• Unexpected & unpredictable events...

envA envB

x

pote

ntial

current controlnew control

activity decreases for environmental changeand system starts random walkstate

potential

activity

noise


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Adaptation to Environment by Thermal Fluctuation in

Gene Expression

Attactor1 Attrator2 Attractor2

ActivityEnviornemnt1is comfortable

ActivityEnvironmental2 is comfortable

Environment1

← Environmental change ← Original environment → Environmental change →

Environment2

Attractor1

Glutamine deficiency Tetrahydrofolate deficiency

Po

ten

tia

l

Generalized Yuragi formula

dx

dUxf )( activityxfx

dt

d)( , where

It selects the best situation (attractor) by maximizing activity (Yuragi search) in the gene expression

Colon Bacillus


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Interpretation of the Formula

activityxfxdt

d)(

Thermal fluctuation, Spontaneousfluctuation

Structure of Yuragi

Condition of the systemDegree of

comfortable feeling

The control structurethat has attractors

Structure that acceptsuse of Yuragif(x)=－dU/dx

Activity

State variable xPotentialU(x)

Attractor

Yuragi Search

Selection


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Realize adaptive and responsive routing by monitoring communication conditions that fluctuate always.

2 2

( )( )

1 max

dx s activityd activity x η

dt x x

activityxfx Bdtd )(

Suppose there are 10000 routers and each router has5 routing patterns. The traditional routing methodneeds to compute 510000 patterns. On the other hand,the new system based on the biological mechanismselect just 5 patterns in parallel.

Quick recovery in accidents and quickadaptation to environ-mental changessuch as load variations

Each router individually and adaptively selects routes

according to the communication conditions

Router

Probability forselecting routs Noise


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１９００

２０００

HighLow

New technologies to adapt complicated systems

to humansand the environments

Co

mp

lex

ity

Environmental load

Previous principlescannot satisfy both

development of complicated systems

and relaxation of the environmental load

２０１５

Low

High

Change Directions of Science and Engineering

Innovation with

biological fluctuation

Yuragi


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National Institute of Information andCommunications Technology

Thank you for your kind attention

bio-inspired research for a new generation...

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