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Bio-inspired Networking and Complex Networks: A Survey

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Bio-inspired Networking and Complex Networks: A Survey

Sheng-Yuan Tu

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Outline

Challenges in future wireless networks

Bio-inspired networking

Example 1: ant colony

Example 2: immune system

Complex networks

Network measures

Network models

Phenomena in complex networks

Dynamical processes on complex networks

Further research topics

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Challenges in Future Wireless Networks

Scalability

By 2020, there will be trillion wireless devices [1] (e.g. cell phone, laptop, health/safety care se nsors, …)

Adaptation

Dynamic network condition and diverse user demand

Resilience

Robust to failure/malfunction of nodes and to intru ders

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Bio-inspired Networking

Biomimicry: studies designs and processes in n ature and then mimics them in order to solve h uman problems [3]

A number of principles and mechanisms in large scale biological systems [2]

Self-organization: Patterns emerge, regulated by fe edback loops, without existence of leader

Autonomous actions based on local information/inter action: Distributed computing with simple rule of t humb

Birth and death as expected events: Systems equip w ith self-regulation

Natural selection and evolution

Optimal solution in some sense

A special issue on bio-inspired networking wil l be published in IEEE JSAC in 2

nd

quarter 2010 .

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Bio-inspired Networking

Observatio n, verbal description Observatio n, verbal description

Math. Model (Diff. eq.,

prob.

methods, fuzzy logic,

…)

Math. Model (Diff. eq.,

prob.

methods, fuzzy logic,

…) Verification

,

hypothesis testing Verification

,

hypothesis testing Parameter

evaluation, prediction Parameter evaluation,

prediction

Entities mapping Entities mapping

Algorithm establishm

ent

Algorithm establishm

ent

Performan ce evaluation Performan

ce evaluation

Biological Modeling Engineering Applying

Parameter tuning Parameter

tuning

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Example 1: Foraging of Ant Colony

Stigmergy: interaction between ants is built o n trail pheromone [6]

Behaviors [6]:

Lay pheromone in both directions between food sourc e and nest

Amount of pheromone when go back to nest is accordi ng to richness of food source (explore richest reso urce)

Pheromone intensity decreases over time due to evap oration

Stochastic model (no trail-laying in backward) :

i

i i i

dC q P fC dt   

1

( )

( )

n i

i m

n i j

P k C

k C

C1

C2

Cm

P1

P2

Pm

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Example 1: Foraging of Ant Colony

Parameter evaluat ion:

Ω: flux of ants

q: amount of pher omone laying

f: rate of pherom one evaporation

k: attractiveness of an unmarked pa th

n: degree of nonl inearity of the c hoice

Shortest path sea rch

1 2

/ 2 ( m)

R q  fk q q q m

[5]

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Example 1: Foraging of Ant Colony

Application in ad-hoc network routing [4]

Modified behaviors

Probabilistic solution construction without forward pheromone updating

Deterministic backward path with loop elimination a nd pheromone updating

Pheromone updates based on solution quality

Pheromone evaporation (balance between exploration and exploitation)

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Example 1: Foraging of Ant Colony

Algorithm

Initiation

Path selection

Pheromone update

More other applications can be found in swarm intelligence [7].

0 /

ij m Cnn

[ ] [ ] [ ] [ ]

ik

ij ij

k ij

il il

l N

p

ij 1/ dij

(1 )

ij ij

   

1

m k

ij ij ij

k

1/ if arc( , ) belongs to 0 otherwise

k k

k ij

C i j T

 

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Example 2: Immune System

Functional architecture of the IS [8]

Physical barriers: skin, mucous membranes of digest ive, respiratory, and reproductive tracts

Innate immune system: macrophages cells, complement proteins, and natural killer cells against common p athogen

Adaptive immune system: B cells and T cells

B cells and T cell are created from stem cells in the bo ne marrow ( 骨髓 ) and the thymus ( 胸腺 ) respectively by rearrangement of genes in immature B/T cells.

Negative selection: if the antibodies of a B cell match any self antigen in the bone marrow, the cell dies.

Self tolerance: almost all self antigens are presented i n the thymus.

Clonal selection: a B cell divides into a number of clon es with similar but not strictly identical antibodies.

Danger signal: generated when a cell dies before be gin old

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Example 2: Immune System

Procedure

Antibodies of B cell match antigens (signal 1b) Antibodies of B cell match

antigens (signal 1b)

Antibodies of T cell binds the antigens (signal 1t) Antibodies of T cell binds

the antigens (signal 1t) Matching

>

Threshol d?

Clonal selection Clonal selection

Receive signal

2t?

Match antigens

? Antigen Presenting

Cell Antigen Presenting

Cell No

Yes

Yes

Yes

Danger Signal

Signal 2t

T cell sent signal 2b to B cell

T cell sent signal 2b to B cell

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Example 2: Immune System

Application in misbehavior detection in mobile ad-hoc networks with dynamic source routing (D SR) protocol [8]

Entity mapping:

Body: the entire mobile ad-hoc network

Self-cells: well behaving nodes

Non-self cells: misbehaving nodes

Antigen: sequence of observed DSR protocol events i n the packet headers

Antibody: A pattern with the same format of antigen

Chemical binding: matching function

Bone marrow: a network with only certified nodes

Negative selection: antibodies are created during a n offline learning phase

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Complex Networks

The above approach is more or less heuristic a nd is based on trial and error. What is theor etical framework to understanding network beha viors?

Network measures

Degree/connectivity (k)

Degree distribution

Scale-free networks

Shortest path

Six degrees of separation (S. Milgram 1960s)

Small-world effect

Clustering coefficient (C)

Average clustering coefficient of all nodes with k links C(k)

[12]

( ) (2< 3) P k k

3 # of triangles

# of connected triples of vertices

C

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Complex Networks

Network models

Random graphs (ER model)

Start with N nodes and connect each pair of nodes with p rob. p

Node degrees follow a Poisson distribution

Generalized random graphs (with arbitrary degree di stribution)

Assign ki stubs to every vertex i=1,2,…,N

Iteratively choose pairs of stubs at random and join the m together

Scale-free networks (evolution of networks)

Start with m0 unconnected vertices

Growth: add a new vertex with m stubs at every time step

Preference attachment:

Hierarchical networks

Coexistence of modularity, local clustering, scale-free tology

Generalized random graphs [11]

( )i i / j

j

k k k

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Complex Networks

15 [12]

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Phenomena in Complex Networks: Phase Trans ition

Phase transition: as an external parameter is varied, a change occurs in the macroscopic beh avior of the system under study [10].

Example: Emergence of giant component in gener alized random graphs [13]

Degree distribution : pk

Outgoing degree distribution of neighbors:

With the aid of generating function, [13] derived d istribution of component sizes. Specially, the aver age component size is

Diverges if , and a giant componen t emerges.

For random graphs, a giant component emerges if

( 1) 1 /

k k j

j

q k p

jp

2

1 2

2 s k

k k

 

2 2

k k

( 1) 1 k p N  

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Phenomena in Complex Networks: Synchroniza tion

Synchronization: many natural systems can be d escribed as a collection of oscillators couple d to each other via an interaction matrix and display synchronized behavior [10].

Application: distributed decision through sel f-synchronization [14]

xi(t): state of the system yi: measurement (e.

g. temperature)

gi(yi): local processing unit K: global control loop gain

Ci: local positive coefficient aij: coupling among nodes

h: coupling function w(t): coupling no ise

: propagation delay

1 1

( ) ( ) [ ( ) ( )] ( )

N

i i i i ij j ij i i

j

x t g y KC a h x t x t w t

ij

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Phenomena in Complex Networks: Synchroniza tion

Form of consensus: when h(x)=x, system achieves synchronize if and only if the directional gra ph is quasi strongly connected (QSC) and

1

1 1

( ) lim ( )

N

i i i i

i

q N N

t

i i i ij ij

i i

c g y x t

c K a

 



 

Example of QSC graph [14]

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Dynamical Processes on Complex Networks

Epidemic spreading

SIR model

S: susceptible, I: infective, R: recovered

Fully mixed model

SIS model

Application in routing/data forwarding in mobile ad hoc networks [15]

Search in networks

Search in power-law random graphs [16]

Random walk

Utilizing high degree nodes

, ,

ds di dr

is is i i

dt   dt dt

( )

P k k

3(1 2/ )

s N

s N 2 4/

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Further Research Topics

Cognition and knowledge construction/representation o f humans

Information theoretical approach to local information

In general, we can model the observing/sensing process as a c hannel, what does the channel capacity mean?

What is relationship between channel capacity and statistical inference?

What are conditions that cooperative information helps (or th ey achieves consensus)?

Example: spectrum sensing in cognitive radio networks

Global informati

on Observed

local informatio Equivalent channel model n

Cooperativ e

informatio n

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Reference

[1] K. C. Chen, Cognitive radio networks, lecture note.

[2] M. Wang and T. Suda, “The bio-networking architecture: A biologically inspired approach to the design of s calable, adaptive, and survivable/available network application,”

[3] M. Margaliot, “Biomimicry and fuzzy modeling: A match made in heaven,” IEEE Computational Intelligen ce Magazine, Aug. 2008.

[4] M. Dorigo and T. Stutzle, Ant colony optimization, 2004.

[5] S. C. Nicolis, “Communication networks in insect societies,” BIOWIRE, pp. 155-164, 2008.

[6] S. Camazine, J. L. Deneubourg, N. R. Franks, J. Sneyd, G. Theraulaz, and E. Bonabeau, Self-organization in biological systems, 2003.

[7] E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm intelligence: From natural to artificial systems, 1999.

[8] J. Y. Le Boudec and S. Sarafijanovic, “ An artificial immune system approach to misbehavior detection in m obile ad-hoc networks,” Bio-ADIT, pp. 96-111, Jan. 2004.

[9] M. E. J. Newman, “The structure and function of complex networks,” 2003

[10] A. Barrat, M. Barthelemy, and A. Vespignani, Dynamical processes on complex networks, 2008 [11] C. Gros, Complex and adaptive dynamical systems, 2008.

[12] A-L Barahasi and Z. N. Oltvai, “Network biology: Understanding the cell’s function organization,” Nature Review, Feb. 2004.

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Reference

[13] M. E. J. Newman, S. H. Strogatz, and D. J. Watts, “Random graphs with arbitrary degree distributions and t heir applications,” Physical Review E., 2001.

[14] S. Barbarossa and G. Scutari, “Bio-inspired sensor network design: Distributed decisions through self-sync hronization,” IEEE Signal Processing Magazine, May 2007.

[15] L. Pelusi, A. Passarella, and M. Conti, “Opportunistic networking: Data forwarding in disconnected mobile ad hoc networks,” IEEE Communications Magazine, Nov. 2006.

[16] L. A. Adamic, R. M. Lukose, A. R. Puniyani, and B. A. Huberman, “Search in power-law networks,” Physi cal Review E., 2001.

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