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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/
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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
(brain): vasopressin
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[SelfOrg] 5.38
Shifting the Paradigm: Feedback Loop Mechanism
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
(brain): vasopressin
Event
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Shifting the Paradigm: Feedback Loop Mechanism
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
Shifting the Paradigm: Feedback Loop Mechanism
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.