Lecture 1: Introduction to Self- Organization · => Definition of Self-organization • How does it work in practice? => Examples for Self-organization in Communication ... GGSN Internet

Introduction to self-organization

Dr.-Ing. Abdalkarim Awad

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Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated Communication Systems Group

www.tu-ilmenau.de/ics

Lecture 1: Introduction to Self-

Organization

Self-Organizing


13.10.2011



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Introduction to Self-Organization

• Why is it important?

=> Motivation for Self-organization

• What does it mean?

=> Definition of Self-organization

• How does it work in practice?

=> Examples for Self-organization in Communication

Systems



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Why a course on self-organizing systems?



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Motivation- Internet

• Traditional

– Client-Server

• Current and future

– Internet of things

– Virtualization (i.e. Peer-to-Peer (P2P) and overlay)



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• Example: data storage/retrieve problem!

Where to store and how to find certain data element (file)?



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Client/Server (Server Stores the Data)

• Well known approach

• Server is a data

source

• Clients request

data from server

• Sever is a powerful

computer

• Clients have usually

lower capabilities

I need data “titanic.avi”



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Unstructured P2P (Server knows info.)

I have data “titanic.avi”

I need data “titanic.avi”

• A way to share files with

others

• Users upload their list of files

to server

• You send queries to for files

of interest (e.g. Song)

• server replies with IP address

of users with matching files

• You connect directly to user A

to download file

• Example Napster , Skype



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Structured P2P (DHT)

Distributed Hash Table -DHT

I have data “titanic.avi” Hash(“titanic.avi”)=7 Put(7,titanic.avi)

I need data “titanic.avi” Hash(“titanic.avi”)=7 Get(7)

1

3 5

8

11 12

0

5 .

8 .

0 .

1 .

3 .

5 .

8 .

11 .

• Decentralized

• Autonomous

components

• Mapping

E.g. Map

Filename to

Node ID

• Nodes have partial

view about the

network



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• Think about! (Which system can be self-organizing?)

– Which system has a central entity?

– Where do we have single node of failure?

– What about the capabilities of the computers (server)?

– …



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Comparison

Client Server Unstructured´P2P Structured P2P

Scalability Bad Middle Good

Cost Bad Middle Good

Reliability Good Bad Bad

Single node of failure

Bad Middle Good



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Motivation- Next Generation Mobile

Communication Systems

• Mobile communication systems in the past

– Manual configuration

– Manual adaptation (optimization)

– Manual repair

– Central controller (e.g. RNC)

• Mobile communication systems in future

– Large, complex and dynamic

– No central controller (several functions are moved to BS)



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Motivation- Next Generation Mobile

Communication Systems femtocell networks

• Massive deployment of user-operated home-base stations (femto-access points) for cellular communications demands for decentralized radio resource allocation

• Lacking any central authority having complete control of the network topology, the system must incorporate self-organization, self-healing capabilities



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Transition to LTE/SAE: Architecture

GPRS Core

(Packet Switched)

SGSN

GGSN

Internet

GSM

RAN

Base station

Base station controller

Base station

Base station

UTRAN

Radio network controller

node B node B

node B

MSC

PSTN

GSM Core

(Circuit

switched)

HLR AuC

EIR

GMSC

E- e-

e- e- S-GW

P-GW

IMS

EPC



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Motivation- Ad hoc Networks

• Example: Wireless Sensor Networks – Massive deployment of sensors in difficult to reach areas

(e.g. deployment by aircraft)

– Self-organization capabilities in terms of sensing and inter-node communications

– All nodes initially have the same configuration (No IP or

MAC address)

– Robustness to node failure and self-healing mechanisms are important

– Limited resources

– Energy efficient

http://www.btnode.ethz.ch/pub/pics/btnode_rev3.20.jpg


















































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Problem of today´s networks – Heterogeneity

– Dynamics

– Scalability

Method: Self-organization – Fast, autonoms reaction to problems

– Automation of control

– Distributed control

Some application scenarios: – Severe network impact due to disasters

– Energy savings

– Privately operated femto cells

Motivation for Self-Organization



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Self-organized Service Recovery S Reconfigurable Radio

Interfaces R

Cognitive Management of Transport Resources T

Our Application Interest in Self-Organization

Decentralized Information Management I



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Research Topics – Secure Network Operation How can access to adhoc-deployed communication infrastructure be restricted?

– Innovative authentication techniques based on localization?

– How to ensure secure localization?

– How to configure/manage access rights, cryptographic keys etc.?



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Research Topics – Integration of UAVs

image source: http://gallery.mikrokopter.de

Self-organized integration of mobile platforms (land-based and airborne)



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Self-org is a vital need for complex systems

• The term complex system formally refers to a system of many

parts which are coupled in a nonlinear fashion. A linear

system is subject to the principle of superposition, and hence

is literally the sum of its parts, while a nonlinear system is not.

• When there are many nonlinearities in a system (many

components), its behavior can be as unpredictable as it is

interesting.

• Local rules, which describe the

behavior of each entity in the

system, lead to complex

global states.



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Are Self-Organizing Techniques Unique Solutions?



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Are Self-Organizing Techniques Unique Solutions? NOT Necessary

What are they good for?

Remember Client-Server and P2P!



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Drivers for Self Organizing Networks

• Technological drivers

– The complexity of systems

• Market drivers

– Reduce Capital EXpenditures (CAPEX)

• For example applying self-configuration algorithms reduces

human effort in the installation

– Reduce Operational EXpenditure (OPEX)

• For example applying self-optimization algorithms reduces

the power consumption



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Natural Systems

• Non-living systems

– Sand dune ripples



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Natural Systems

• Living systems

– Zebra stripes



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Natural Systems

• Living systems (social)

• Ants



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Natural Systems

• Living systems (social)

• School of fish



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Self-Organization and Emergence

• Emergent phenomena – pattern / function / property /structure

• Examples: – Sand dune ripples

– Zebra stripes

– Emergence of consciousness in human • Emerges from neurons interactions

– Shortest path to food found by foraging ants

– Engineered systems: • TSP: shortest path to visit all cities



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• A self-organizing system produces complex organization

from randomness based on local interaction and without

external intervention



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• Emergent Phenomena do not appear on the individual

component level but on the global level

• Emergent Phenomena is far beyond the capabilities of

individual components



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Self-Organization-definition

• Self-organization in Engineered systems “Self-organization is the process enabling a system to change its organization in case of environmental changes without explicit external command .”

• “Strong self-organizing systems are those systems where there is reorganization with no explicit central control, either internal or external.”

• “Weak self-organizing systems are those systems where, from an internal point of view, there is re organization under an internal central control or planning.”



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Properties of self-organizing systems

• No central entity

• No external controller

• Global behavior results from local interaction

• No global pattern / recipe / rule to refer to



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Capabilities of Self-Organizing systems

• Self-configuring

– The process where newly deployed components are configured

by automatic installation procedures to get the necessary basic

configuration for system operation

• Self-optimization

– Ability of the system to optimize the local operation parameters

according to global objectives

• Self-healing

– Methods for changing configurations and operational parameters

of the overall system to compensate failures

• Self-protection

– System automatically defends against malicious attacks or

cascading failures. It uses early warning to anticipate and

prevent system wide failures [Kephart 03]”



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Limitations of self-organizing approaches

• Controllability

• Cross-mechanism interference

• Difficult assessment of the

mechanisms

– Large scale

– Dynamic

– Complex

– Unpredictable environment

• New software engineering

approaches are needed



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Natural Self-Organization and “Bio-Inspired”

• Echolocation mechanisms in many animals have always

been a source of inspiration for radar waveform design

• The chirp acoustic signals emitted by bats/dolphins are

very similar to the radar waveform



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Natural Self-Organization and “Bio-Inspired”

Principles of echolocation (bio-sonar)

The 18th century Italian scientist Lazzaro Spallanzani had, by means of a series of elaborate experiments, concluded that bats navigate by hearing and not by vision



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Bio-Inspired

• Routing based on Ants behavior



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Design Steps of Bio-inspired Solutions

• Analogy

– Study the analogy between the biological system and our

system (ICT problem)

• Modeling and simulation

– Obtain a realistic model

• Testing

– Explore the performance

• Implementation

– Implement the system



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Engineering of Self-Organizing systems

• Positive and Negative Feedback

• Direct and indirect interaction

• Decentralized Control



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Positive Feedback

• Initial change in a system is reinforced in the same

direction as initial change

– Towards amplification

– Implies changes in the system

• Ex: Fish nesting

– Initial change = some fishes nest close to each other

– Positive feedback rule = “I nest where other similar

individuals nest”

– Increased aggregation of fishes at the same place



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Negative Feedback

• Perturbation applied to system triggers response that counteracts the perturbation

– Towards stabilisation

– Avoids fluctuations

• Ex: maintain a constant distance to your neighbors

– Negative feedback = A bird is close to flock (swarm)

– Response = Decrease speed

– Result = Avoid collisions with neighbors



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Positive Feedback coupled with Negative Feedback

• Positive feedback pushes system towards its limits

• Negative feedback or physical constrains provide inhibition and maintain system under control

• Ex: Fish nesting – Rule = “I nest where other similar individuals nest unless

there are too much fishes”



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Direct Communication

• Schools of fishes – Information acquired from neighbour



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Indirect Communication

• Information acquired from shared local environment and work-in-progress : Stigmergy

• Social Insects

– Ants deposit pheromone along traveled path which is

used by other ants to follow the trail.

– This kind of indirect communication via the local

environment is called stigmergy



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Decentralized Control

• Coordination of work without central decisions

• Self-organization mechanisms are (mostly) based on

decentralized architectures of information flow

– No instruction issued from leaders

– Individuals gather information (directly or not) and decides

what to do



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Decentralized Control

• Two kinds of engineered systems with decentralized

control

– Case 1: Large set of autonomous components, pertaining

to the same system and providing as a whole expected

properties, or functions.

• Engineering with emergent functionality in mind

– Case 2: Large set of autonomous components,

spontaneously interacting with each other, for possibly

independent or competing reasons.

• No expected emergent global function or properties

• In both cases, autonomous components may be

• Heterogeneous

• dynamically joining and leaving the system.



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Dynamic Change

• Continual interactions among components

• Components join and leave system at any time

• Dynamic systems

– Ants foraging

– Skype system of users



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Research Directions

• Self-Organizing methods to solve problems in next

generation networks

• Evaluation of self-organizing methods

• Not discovered bio-Inspired approaches



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Summary (what do I need to know)

• Why self-organizing systems?

• What is emergence phenomena?

• What are the characteristics of self-org systems?

• What are the advantages and disadvantages?

• What do we mean by bio-inspired approaches?

• What is Positive and Negative Feedback?

• What is direct and indirect communication?



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References

• Slides “Evaluation of self-organizing systems using

quantitative measures ” by Hermann de Meer, Richard Holzer,

Patrick Wüchner

• Slides “Adaptive Systems” by Giovanna Di Marzo Serugendo

• Slides“ Bio-Inspired Signal Processing” by Sergio Barbarossa

• Slides “Self-Organization in Sensor and Actor Networks” by

Falko Dressler

• F. Dressler, Self-Organization in Sensor and Actor Networks.

John Wiley & Sons, December 2007

• E. Bonabeau, M. Dorigo, and G. Théraulaz. Swarm

Intelligence: From Natural to Artificial Systems Santa Fe

Institute Studies on the Sciences of Complexity. Oxford

University Press, UK, 1999.



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References

• E. Bonabeau, M. Dorigo, and G. Théraulaz. Swarm Intelligence: From Natural to Artificial Systems Santa Fe Institute Studies on the Sciences of Complexity. Oxford University Press, UK, 1999.

• S. Camazine, J.-L. Deneubourg, Nigel R. F., J. Sneyd, G. Téraulaz, and E.Bonabeau. Self-Organisation in Biological Systems. Princeton Studies in Complexity. Princeton University Press, 2001.

• J.H. Holland, Emergence – from Chaos to Order. Oxford University Press, 1998

• M. Wooldridge: An Introduction to Multi-Agent Systems. Wiley, 2003

• L. M. de Castro: Fundamentals of Natural Computing – Basic Concepts, Algorithms, and Applications. Chapman & Hall/CRC. 2006.

Documents

Lecture 1: Introduction to Self- Organization · => Definition of Self-organization • How does it work in practice? => Examples for Self-organization in Communication ... GGSN Internet