Bio Inspired

7/27/2019 Bio Inspired

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[SelfOrg] 5.1

Self-Organization in AutonomousSensor/Actuator Networks

[SelfOrg]

Dr.-Ing. Falko Dressler

Computer Networks and Communication Systems

Department of Computer Sciences

University of Erlangen-Nrnberg

http://www7.informatik.uni-erlangen.de/~dressler/

[email protected]


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[SelfOrg] 5.2

Overview

Self-OrganizationIntroduction; system management and control; principles and

characteristics; natural self-organization; methods and techniques

Networking Aspects: Ad Hoc and Sensor NetworksAd hoc and sensor networks; self-organization in sensor networks;evaluation criteria; medium access control; ad hoc routing; data-centricnetworking; clustering

Coordination and Control: Sensor and Actor NetworksSensor and actor networks; communication and coordination;collaboration and task allocation

Self-Organization in Sensor and Actor Networks

Basic methods of self-organization revisited; evaluation criteria

Bio-inspired Networking

Swarm intelligence; artificial immune system; cellular signaling pathways


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[SelfOrg] 5.3

Bio-inspired Networking

Introduction

Swarm intelligence

Artificial immune system Cellular signaling pathways


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[SelfOrg] 5.4

The term bio-inspired

The term bio- inspiredhas been introduced to demonstrate the strong

relation between a particular system or algorithm, which has beenproposed to solve a specific problem, and a biological system, which

follows a similar procedure or has similar capabilities.

Bio-inspired computing represents a class of algorithms focusing on

efficient computing, e.g. for optimization processes and pattern recognition

Bio-inspired systems rely on system architectures for massively distributed

and collaborative systems, e.g. for distributed sensing and exploration

Bio-inspired networking is a class of strategies for efficient and scalable

networking under uncertain conditions, e.g. for delay tolerant networking


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[SelfOrg] 5.5

The design of bio-inspired solutions

Identification of analogies In swarm or molecular biology and IT systems

Understanding Computer modeling of realistic biological behavior

Engineering

Model simplification and tuning for IT applications

Identification of

analogies between

biology and ICT

Modeling of realistic

biological behavior

Model simplification

and tuning for ICT

applications

Understanding Engineering


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[SelfOrg] 5.7

Bio-inspired research ANNs

Artificial neural networks (ANNs)

Primary objective of an ANN is to acquire knowledge from the environment self-learning property

Input: x1

Input:x2

Input:xn

w1

w2

wn

b

f(u)u Output:y

Activation

function

Summing

junction


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[SelfOrg] 5.8

Bio-inspired research others

Swarm intelligence (SI)

Artificial immune system (AIS)

Cellular signaling pathways


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[SelfOrg] 5.10

Swarm intelligence

Stigmergy: stigma (sting) + ergon (work)

stimulation by work

Characteristics of stigmergy

Indirect agent interaction modification of the

environment

Environmental modification serves as externalmemory

Work can be continued by any individual

The same, simple, behavioral rules can create

different designs according to the environmental

state


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[SelfOrg] 5.11

Swarm intelligence Collective foraging by ants

(a) Starting from the nest, a random search for the food is performed by

foraging ants(b) Pheromone trails are used to identify the path for returning to the nest

(c) The significant pheromone concentration produced by returning ants

marks the shorted path

Nest Food Nest Food

Nest Food

(a)

(c)

(b)


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[SelfOrg] 5.12

Ant Colony Optimization (ACO)

Working on a connected graph G = (V,E), the ACO algorithm is able to

find a shortest path between any two nodes

Capabilities

A colony of ants is employed to build a solution in the graph

A probabilistic transition rule is used for determining the next edge of the

graph on which an ant will move; this moving probability is furtherinfluenced by a heuristic desirability

The routing table is represented by a pheromone level of each edge

indicating the quality of the path

The most important aspect in this algorithm is the t ransi t ionprobabi l i typij for an ant kto move from i toj


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[SelfOrg] 5.13

Ant Colony Optimization (ACO)

Jik is the tabu list of not yet visited nodes, i.e. by exploitingJi

k, an ant kcan

avoid visiting a node i more than once

ij is the visibility ofj when standing at i, i.e. the inverse of the distance

ij is the pheromone level of edge (i,j), i.e. the learned desirability of choosing

nodej and currently at node i

andare adjustable parameters that control the relative weight of the trail

intensity ij and the visibility ij, respectively

The pheromone decay is implemented as a coefficient with 0 < 1

ij(t) (1 ) ij(t) + ij(t)

otherwise0

if)(

)(k

i

Jl

ilil

ijij

kij

Jjt

t

pki


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[SelfOrg] 5.14

AntNet and AntHocNet

Application of ACO for routing

The routing table Tkdefines the probabilistic routing policy currentlyadopted for node k

For each destination dand for each neighborn, Tk stores a probabilisticvaluePndexpressing the quality (desirability) of choosing n as a next hoptowards destination d

Forward ants randomly search for food

After locating the destination, the agents travel backwards (now calledbackward ants) on the same path used for exploration

Reinforcement

Positive Pfd Pfd+ r(1 Pfd)

Negative PndPnd rPnd n Nk, n f

)}({ 1kneighbornnd

P


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[SelfOrg] 5.15

AntHocNet Performance


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[SelfOrg] 5.16

Ant-based task allocation

Combined task allocation and routing

ACO used for selection of appropriate nodes to accomplish a task AND forselecting appropriate routes (similar to AntNet)

Task allocation Routing


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[SelfOrg] 5.17

Artificial Immune System (AIS)

Artificial immune systems are computational systems inspired by

theoretical immunology and observed immune functions, principles

and models, which are applied to complex problem domains

(de Castro & Timmis)

Why the immune system?

Recogni t ion Ability to recognize pattern that are (slightly) different frompreviously known or trained samples, i.e. capability of anomaly detection

Robustness Tolerance against interference and noise

Diversity Applicability in various domains

Reinforc ement learning Inherent self-learning capability that is

accelerated if needed through reinforcement techniques Memory System-inherent memorization of trained pattern

Distr ibuted Autonomous and distributed processing


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[SelfOrg] 5.18

Self/Non-Self Recognition

Immune system needs to be able to differentiate between self and

non-self cells

Antigenic encounters may result in cell death, therefore

Some kind ofpositive selection

Some element ofnegative selection

Primary immune response Launch a response to invading pathogens

unspecific response (Leucoytes)

Secondary immune response

Remember past encounters (immunologic memory)

Faster response the second time around

specific response (B-cells, T-cells)


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[SelfOrg] 5.20

Reinforcement Learning and Immune Memory

Repeated exposure to an antigen throughout a lifetime

Primary and secondary immune responses

Remembers encounters

No need to start from scratch

Memory cells

Associative memory

Antigen Ag1 AntigensAg1, Ag2

Primary Response Secondary Response

Lag

Responseto Ag1

A

ntibodyConcentration

Time

Lag

Responseto Ag2

Responseto Ag1

...

...

Cross-Reactive

Response

...

...

AntigenAg1 + Ag3

Response toAg1+Ag3

Lag


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[SelfOrg] 5.22

Affinity measure

Representation shape-space

Describe the general shape of a molecule Describe interactions between molecules

Degree of binding between molecules


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[SelfOrg] 5.24

Affinity measure

Aff in i tyis related to distance

Euclidian

Manhatten

Hamming

L

i

ii AgAbD1

2)(

L

i

ii AgAbD1

otherwise0

if1,

1

ii

i

L

i

i

AgAbD


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[SelfOrg] 5.25

AIS Application Examples

Fault and anomaly detection

Data mining (machine learning, pattern recognition) Agent based systems

Autonomous control and robotics

Scheduling and other optimization problems

Security of information systems


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[SelfOrg] 5.26

Virus Detection orA Computer Immune System

Protect the computer from unwanted viruses

Initial work by Kephart 1994

Detect Anomaly

Scan for known viruses

Capture samples using decoys

Extract Signature(s)

Add signature(s) to databases

Add removal infoto database

Segregatecode/data

AlgorithmicVirus Analysis

Send signals toneighbor machines

Remove Virus


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[SelfOrg] 5.27

Forrests Model

Hofmeyr & Forrest (1999, 2000) developed an artificial immune system that is

distributed, robust, dynamic, diverse and adaptive, with applications to

computer network security

Datapath triple

(20.20.15.7, 31.14.22.87, ftp)

Broadcast LAN

ip: 31.14.22.87

port: 2000

Internalhost

External

host

ip: 20.20.15.7

port: 22

Host

Activation

threshold

Cytokinelevel

Permutation

mask

Detector

set

immature memory activated matches

0100111010101000110......101010010

Detector


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[SelfOrg] 5.28

Properties Basis of all biological systems

Specificity of information transfer Similar structures in biology and in technology especially in computer networking

Concepts Intracellular signaling Intracellular signaling refers to the information processing

capabilities of a single cell. Received information particles initiate complex signalingcascades that finally lead to the cellular response.

Intercellular signaling Communication among multiple cells is performed byintercellular signaling pathways. Essentially, the objective is to reach appropriatedestinations and to induce a specific effect at this place.

Lessons to learn from biology Efficient response to a request

Shortening of information pathways

Directing of messages to an applicable destination

Molecular and Cell Biology

Bio-inspired

Networking

Bio-inspired

Networking


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[SelfOrg] 5.29

Intracellular Information Exchange

Local: a signal reaches only cells in the neighborhood. The signal induces asignaling cascade in each target cell resulting in a very specific answer which

vice versa affects neighboring cells

DNA

Signal

(information)

Gene transcription

results in the

formation of a

specific cellular

response to thesignal

Receptor


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[SelfOrg] 5.31

Signaling pathways

Communication with other

cells via cell junctions

Nucleus

Neighboring cell

DNAGene

transcription

mRNA translation

into proteins

Intracellular

signaling

molecules

Reception of signaling molecules

Secretion of

hormones etc.

Nucleus

DNA

Nucleus

DNA

Reception of signaling molecules (ligands such as hormones, ions, small molecules)

Different cellularanswer

(1-a)

(1-b)

(2)

(3-a)

(3-b)

Submission of signaling molecules

Neighboring cell


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[SelfOrg] 5.33

Signaling pathways

Communication with other

cells via cell junctions

Nucleus

Neighboring cell

DNAGene

transcription

mRNA translation

into proteins

Intracellular

signaling

molecules

Reception of signaling molecules

Secretion of

hormones etc.

Nucleus

DNA

Nucleus

DNA

Reception of signaling molecules (ligands such as hormones, ions, small molecules)

Different cellularanswer

(1-a)

(1-b)

(2)

(3-a)

(3-b)

Submission of signaling molecules

Neighboring cell

(2) Indirect stim ulat ion of c ellular processes

A signaling molecule can directly enter the cell and is

processed in a biochemical reaction. The resulting

product changes the behavior or state of the cell. For

example, nitric oxide leads to smooth muscle

contraction.


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[SelfOrg] 5.35

Adaptation to Networking

Local mechanisms

Adaptive group formation

Optimized task allocation

Efficient group communication

Data aggregation and filtering

Reliability and redundancy

Remote mechanisms

Localization of significant relays,

helpers, or cooperation partners

Semantics of transmitted messages

Cooperation across domains

Internetworking of different

technologies

Authentication and authorization


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[SelfOrg] 5.36

Example: Regulation of Blood Pressure

Liver

Angiotensin I

Angiotensinogen

Angiotensin II

Renin

ACE

KidneyAterial bloodpressure

Aterial blood

pressure

Increase of

blood volume

Smooth muscle

cells: contraction

Kidney: aldosterone Na+

retention regulation of

blood volume

Adenohypophysis

(brain): vasopressin


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[SelfOrg] 5.37

Shifting the Paradigm: Feedback Loop Mechanism

Liver

Angiotensin I

Angiotensinogen

Angiotensin II

Renin

ACE

Kidney

Aterial blood

pressure

Increase of

blood volume

Event

Smooth muscle

cells: contraction

Kidney: aldosterone Na+

retention regulation of

blood volume

Adenohypophysis



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[SelfOrg] 5.38


S Liver

Angiotensin I

Angiotensinogen

Angiotensin II

Renin

ACE

Aterial blood

pressure

Increase of

blood volume

Smooth muscle

cells: contraction

Kidney: aldosterone Na+retention regulation of

blood volume

Adenohypophysis


Event


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[SelfOrg] 5.40


The smooth muscle cells, the kidney and the brain team up

one meta node

This node knows the answer to the request

reques

t

S

Aterial blood

pressure

Increase of

blood volume

Event

S


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[SelfOrg] 5.41


No confirmation message is needed

The change of the environment indicates the successful initiation of

the task

request

SEvent

S

change of the

environment


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[SelfOrg] 5.43

Conclusions

Self-organization in for communication and coordinationbetween networked embedded systems, i.e. in WSN and

SANET

Many objectives, many directions, similar solutions

Bio-inspired networking is just one but powerful approach

S ( h d I d k )


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[SelfOrg] 5.44

Summary (what do I need to know)

Bio- inspi red n etwork ing

Ideas and objectives

Swarm intel l igence

Principles pheromone trails

Ant colony optimization with application in ad hoc routing

Art i f ic ial immune sy stem

Principles reinforcement learning

Anomaly detection

Cellu lar sign aling pathways

Principles intracellular and intercellular signaling cascades

Specific reaction on environmental changes

R f


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References

E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems. NewYork, Oxford University Press, 1999.

M. Dorigo, V. Maniezzo, and A. Colorni, "The Ant System: Optimization by a colony of cooperating agents,"

IEEE Transactions on Systems, Man, and Cybernetics, vol. 26 (1), pp. 1-13, 1996. G. Di Caro and M. Dorgio, "AntNet: Distributed Stigmergetic Control for Communication Networks," Journal

of Artificial Intelligence Research, vol. 9, pp. 317-365, December 1998.

G. Di Caro, F. Ducatelle, and L. M. Gambardella, "AntHocNet: An adaptive nature-inspired algorithm forrouting in mobile ad hoc networks," European Transactions on Telecommunications, Special Issue on Self-organization in Mobile Networking, vol. 16, pp. 443-455, 2005.

F. Dressler and I. Carreras (Eds.),Advances in Biologically Inspired Information Systems - Models,Methods, and Tools, Studies in Computational Intelligence (SCI), vol. 69. Berlin, Heidelberg, New York,

Springer, 2007. F. Dressler, B. Krger, G. Fuchs, and R. German, "Self-Organization in Sensor Networks using Bio-Inspired

Mechanisms," Proceedings of 18th ACM/GI/ITG International Conference on Architecture of ComputingSystems - System Aspects in Organic and Pervasive Computing (ARCS'05): Workshop Self-Organizationand Emergence, Innsbruck, Austria, March 2005, pp. 139-144.

S. A. Hofmeyr and S. Forrest, "Architecture for an Artificial Immune System," Evolutionary Computation, vol.8 (4), pp. 443-473, 2000.

J. O. Kephart, "A Biologically Inspired Immune System for Computers," Proceedings of 4th InternationalWorkshop on Synthesis and Simulation of Living Systems, Cambridge, Massachusetts, USA, 1994, pp. 130-139.

J. Kim and P. J. Bentley, "Towards an Artificial Immune System for Network Intrusion Detection,"Proceedings of IEEE Congress on Evolutionary Computation (CEC), Honolulu, May 2002, pp. 1015-1020.

T. H. Labella and F. Dressler, "A Bio-Inspired Architecture for Division of Labour in SANETs," Proceedings of1st IEEE/ACM International Conference on Bio-Inspired Models of Network, Information and ComputingSystems (IEEE/ACM BIONETICS 2006), Cavalese, Italy, December 2006.

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