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Contents
ƒ Historical Bio-Inspiration
Bio-Inspired Networking
ƒ Why Bio-Inspired Networking?
ƒ Bio-Inspired Research Fields
The Road to Efficient and Sustainable Mobile Computing ƒ Evolutionary Algorithms (EAs)
ƒ Artificial Neural Networks (ANNs)
Abbas Jamalipour,
Jamalipour, PhD; Fellow IEEE, Fellow IEAust
ƒ Swarm Intelligence (SI)
Editor-in-Chief, IEEE Wireless Communications
IEEE Distinguished Lecturer
[email protected]
ƒ Artificial Immune System (AIS)
ƒ Cellular Signaling Pathways
“The Wireless Networking Group (WiNG
(WiNG))”
ƒ Bio-Inspired Applications in Telecommunications
ƒ Closing Remarks
A. Jamalipour, 4 Feb 2009
BioBio-Inspired: A Very Old Story …
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Why Seek Inspiration from Biology?
ƒ Living organisms are complex adaptive systems
ƒ Artificial systems are going in that direction too
ƒ Look for new solutions to difficult problems
Icarus & Daedalus
Da Vinci
ƒ Life has many self-* features which are also desirable in
artificial systems:
Vaucanson’s Duck
ƒ
ƒ
ƒ
ƒ
ƒ
Mercedes Bionic Car
Velcro
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Wright Brothers
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Self-organization
Self-adaptation
Self-healing ability
Self-optimization
Self-robustness
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The Term “BioBio-Inspired”
Inspired”
Biologically Inspired Problem Solving
ƒ Bio-inspired demonstrates the strong relation between
ƒ Typical problems that can be tackled with bio-inspired
solutions are characterized by the:
ƒ A particular system or algorithm
ƒ which has been proposed to solve a specific problem
ƒ And a biological system
ƒ which follows a similar procedure or has similar capabilities
ƒ Absence of a complete mathematical model
ƒ Bio-inspired computing represents a class of algorithms focusing on
efficient computing
ƒ Large number of (inter-dependent) variables
ƒ Non-linearity
ƒ e.g. in optimization processes and pattern recognition
ƒ Combinatorial or extremely vast solution space
ƒ Bio-inspired systems rely on system architectures for massively
distributed and collaborative systems
ƒ e.g. in distributed sensing and exploration
ƒ Bio-inspired networking is a class of strategies for efficient and
scalable networking under uncertain conditions
ƒ e.g. in delay tolerant networking
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Why BioBio-Inspired Networking?
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Design of BioBio-Inspired Solutions
ƒ Identification of analogies
ƒ Structural view: communication
is an intrinsic part of an
organization
ƒ In swarm or molecular biology and ICT systems
ƒ Understanding
ƒ Computer modeling of realistic biological behavior
ƒ Example organizations:
ƒ Engineering
ƒ Brain (organization of neurons)
ƒ Animal “super organisms”
(ant/bee colonies)
ƒ Human society
ƒ Model simplification and tuning for ICT applications
ƒ Those natural and living
organizations seem better
organized than the current
Internet!
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Identification of
analogies
between biology
and ICT
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2009 Sendai International Workshop
Understanding
Modeling of
realistic biological
behavior
Engineering
Model simplification
and tuning for ICT
applications
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Example of BioBio-Inspired Research Fields
Evolutionary Algorithms (EAs
(EAs))
ƒ Evolutionary Algorithms (EAs)
ƒ Mainly rooted on the Darwinian theory of evolution
ƒ An EA uses some mechanisms inspired by biological
evolution
ƒ Artificial Neural Networks (ANNs)
ƒ Swarm Intelligence (SI)
ƒ Reproduction, Mutation, Recombination, Selection
ƒ EAs represent a set of search techniques used in
computing to find the solutions to optimization problems
ƒ Artificial Immune System (AIS)
ƒ Cellular Signaling Pathways
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Evolutionary Algorithms (EAs
(EAs))
Artificial Neural Networks (ANNs
(ANNs))
ƒ EAs can be categorized into the following Classes
ƒ A Neural Network is a network of biological neurons
ƒ
ƒ
ƒ
ƒ
ƒ
ƒ ANNs are non-linear statistical data modelling tools
Genetic Algorithms (GAs)
Evolution strategies
Evolutionary programming
Genetic programming
Classifier systems
ƒ Used to acquire knowledge from the environment
(known as self-learning property)
ƒ The weights of the neurons are determined in a learning
process
ƒ Examples
ƒ Simulated annealing
ƒ They can be used to model complex relationships
between inputs and outputs or to find patterns in data
ƒ Generic probabilistic meta-algorithm for the global optimization
problem
ƒ Simulated hill-climbing
ƒ A mathematical optimization technique which belongs to the family
of local search
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Artificial Neural Networks (ANNs
(ANNs))
Swarm Intelligence (SI)
ƒ A neuron k that connects n inputs can be described as:
ƒ An Artificial Intelligence (AI) technique based on the
observations of the collective behavior in decentralized
and self-organized systems
Neuron's bias (b)
Input: x1
w2
yk
u
…
Input: xn
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Activation/Squashing
function
Wt(w1)
Input: x2
f(u)
ƒ Typically made up of a population of simple agents
interacting locally with one another and with their
environment (no centralized control)
Output: yk
wn Summing
ƒ Local interactions between autonomously acting agents
often lead to the emergence of global behavior
junction
f (uk )
§ n
·
f ¨¨ ¦ wkj x j bk ¸¸
©j1
¹
ƒ Examples: Ant/bee/termite colonies, bird flocking, animal
herding, bacteria growth, and fish schooling
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SI – Example of Ant Colonies
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SI – Example of Ant Colonies
ƒ Ants solve complex tasks by simple local means
ƒ Ant productivity is better than the sum of their single
activities
ƒ Ants are “grand masters” in search and exploration
“The emergent collective
intelligence of groups of simple
agents.” (Bonabeau)
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SI Properties
What is Stigmergy?
Stigmergy?
ƒ Properties of Swarm Intelligence
ƒ Stigmergy is a mechanism of spontaneous, indirect
coordination between agents or actions, where the trace
left in the environment by an action stimulates the
performance of a subsequent action, by the same or a
different agent
ƒ Agents are assumed to be simple
ƒ Indirect agent communication
ƒ Global behavior may be emergent
ƒ Produces complex, apparently intelligent structures, without
need for any planning, control, or even communication between
the agents
ƒ supports efficient collaboration between extremely simple
agents, who lack any memory, intelligence or even awareness of
each other
ƒ Specific local programming not necessary
ƒ Behaviors are robust
ƒ Required in unpredictable environments
ƒ Individuals are not important
ƒ Stigmergy is a form of self-organization first observed in
social insects
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SI Example: Collective Foraging by Ants
Principles of Swarm Intelligence
ƒ Starting from the nest, a random search for the food is
performed by foraging ants
ƒ Pheromone trails are used to identify the path for
returning to the nest
ƒ The significant pheromone concentration produced by
returning ants marks the shorted path
ƒ What makes a Swarm Intelligent system work?
x Positive Feedback
x Negative Feedback
x Randomness
x Multiple Interactions
ƒ Positive Feedback reinforces good solutions
ƒ Ants are able to attract more help when a food source is found
ƒ More ants on a trail increases pheromone and attracts even
more ants
ƒ Negative Feedback removes bad or old solutions from
the collective memory
Nest
Food
ƒ Pheromone decay
ƒ Distant food sources are exploited last
ƒ Pheromone has less time to decay on closer solutions
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Principles of Swarm Intelligence
SI Routing Overview
ƒ Randomness allows new solutions to arise and directs
current ones
ƒ Ant-Based Control
ƒ Ant Based Control (ABC) is introduced to route calls on a circuitswitched telephone network. ABC is the first SI routing algorithm
for telecommunications networks
ƒ Ant decisions are random
ƒ Exploration probability
ƒ AntNet
ƒ Food sources are found randomly
ƒ AntNet is introduced to route information in a packet switched
network
ƒ AntNet is related to the Ant Colony Optimization (ACO) algorithm
for solving Traveling Salesman type problems
ƒ Multiple Interactions: No individual can solve a given
problem. Only through the interaction of many can a
solution be found
ƒ AntHocNet
ƒ A MANET routing algorithm based on AntNet which follows a
reactive routing approach
ƒ One ant cannot forage for food; pheromone would decay too fast
ƒ Many ants are needed to sustain the pheromone trail
ƒ More food can be found faster
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BioBio-Inspired Networking
ƒ Termite
ƒ Also a MANET routing algorithm
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SI Application in MANET Routing
AntAnt-based Task Allocation
ƒ Routing in MANETs is an extension of Ant Foraging!
ƒ Combined task allocation according to ACO paradigm
has been investigated for MANETs
ƒ Ants looking for food…
ƒ Packets looking for destinations…
ƒ Can routing be solved with SI?
ƒ The proposed architecture for MANETs is completely
dependant on probabilistic decisions
ƒ Can routing be an emergent behavior from the
interaction of packets?
ƒ During the lifetime of the MANETs, nodes adapt the
probability to execute one task out of a given set
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Bee Colony Optimization (BCO)
Bee Colony Optimization (BCO)
ƒ The BCO algorithm is inspired by the behavior of a
honey bee colony in nectar collection
ƒ Artificial bees fly around in a multidimensional search space and
some (employed and onlooker bees) choose food sources
depending on their experience of and their nest mates, and adjust
their positions
ƒ Some (scouts) fly and choose the food sources randomly without
using experience
ƒ If the nectar amount of a new source is higher than that of the
previous one in their memory, they memorize the new position and
forget the previous one
ƒ Thus, ABC system combines local search methods, carried out by
employed and onlooker bees, with global search methods, managed
by onlookers and scouts, attempting to balance exploration and
exploitation process
ƒ This biologically inspired approach is currently being employed
to solve continuous optimization problems
ƒ training neural networks, job shop scheduling, server optimization
BCO provides a population-based search
procedure in which individuals called
foods positions are modified by the
artificial bees with time and the bee’s aim
is to discover the places of food sources
with high nectar amount and finally the
one with the highest nectar
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Artificial Immune System (AIS)
Why the Immune System?
ƒ Artificial immune systems are computational systems
inspired by theoretical immunology and observed
immune functions, principles and models, which are
applied to complex problem domains
ƒ Recognition
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ƒ Ability to recognize pattern that are different from previously
known or trained samples, i.e. capability of anomaly detection
ƒ Robustness
ƒ Tolerance against interference and noise
ƒ Diversity
ƒ Applicability in various domains
ƒ The primary goal of an AIS is to efficiently detect
changes in the environment from the normal system
behavior in complex problem domains
ƒ Reinforcement learning
ƒ Inherent self-learning capability that is accelerated if needed
through reinforcement techniques
ƒ Memory
ƒ System-inherent memorization of trained pattern
ƒ Distributed
ƒ Autonomous and distributed processing
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AIS – Application Examples
Molecular and Cell Biology
ƒ Fault and anomaly detection
ƒ Properties
ƒ Basis of all biological systems
ƒ Specificity of information transfer
ƒ Similar structures in biology and in technology
Æ Especially in computer networking
ƒ Data mining (machine learning, pattern recognition)
ƒ Agent based systems
ƒ Autonomous control and robotics
ƒ Scheduling and other optimization problems
ƒ Lessons to learn from biology
ƒ Security of information systems
ƒ Efficient response to a request
ƒ Shortening of information pathways
ƒ Directing of messages to an applicable destination
ƒ Misbehavior detection for MANETs based on the DSR
protocol (Boudec and Sarafijanovic, 2004)
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Info Exchange in Cellular Environments
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Intracellular Signaling Pathways
Reception of signaling molecules (ligands such as hormones, ions, small molecules)
ƒ Signaling in biological systems occurs at multiple levels
and in many shapes
(1-a)
ƒ Signaling describes interactions between individual molecules
Different
cellular answer
ƒ The information processing capabilities of a single cell
ƒ Received information particles initiate complex signaling cascades
that finally lead to the cellular response
Nucleus
DNA
ƒ Intercellular signaling
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mRNA translation
into proteins
ƒ
Reorganization of
intracellular
structure
ƒ
After processing
the information, a
specific cellular
answer is initiated
Gene
transcription
ƒ
The effect could be
the submission of a
molecule
Nucleus
DNA
Secretion of
hormones
etc.
(3-a)
ƒ Communication among multiple cells is performed by intercellular
signaling pathways
ƒ Objective is to reach appropriate destinations and to induce a
specific effect at this place
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Communication with other
cells via cell junctions
(2)
ƒ Intracellular signaling
Transfer via
receptors on cell
surface
(3-b)
Intracellular
signaling
molecules
ƒ Main cellular signaling techniques
ƒ
Neighboring cell
(1-b)
Submission of signaling molecules
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Intracellular Information Exchange
Intercellular Information Exchange
ƒ Local: A signal reaches only cells in the neighborhood.
The signal induces a signaling cascade in each target
cell resulting in a very specific answer which vice versa
affects neighboring cells
Tissue 1
DNA
DNA
Tissue 2
DNA
Signal
(information)
DNA
Receptor
DNA
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Remote: A signal is released in the
blood stream, a medium which carries it
to distant cells and induces an answer
in these cells which then passes on the
information or can activate helper cells
(e.g. the immune system)
Gene transcription
results in the
formation of a
specific cellular
response to the
signal
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o
Blo
DNA
Tissue 3
DNA
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Lessons to be Learnt
Adaptation to Networking
ƒ The adaptation of mechanisms known from molecular
and cell biology promises to enable more efficient
information exchange
ƒ New concepts for behavior patterns of network nodes
ƒ Local mechanisms
ƒ
ƒ
ƒ
ƒ
ƒ
ƒ Improved efficiency and reliability of the entire communication
system
ƒ Flexible self-organizing infrastructures
d
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ƒ Remote mechanisms
ƒ Localization of significant relays,
helpers, or cooperation partners
ƒ Semantics of transmitted
messages
ƒ Cooperation across domains
ƒ Internetworking of different
technologies
ƒ Authentication and authorization
Adaptive group formation
Optimized task allocation
Efficient group communication
Data aggregation and filtering
Reliability and redundancy
ƒ Main concepts to be exploited in the context of
communication networks
ƒ Signaling pathways based on specific signal cascades with
stimulating and inhibitory functionality used for intracellular
communication
ƒ Diffuse (probabilistic) communication with specific encoding of
the destination receptors for intercellular communication
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Heterogeneous Mobile Networks
NGMN will be an
integrated platform
interconnecting multiple
networks for seamless
user connectivity for
multimedia applications
anytime and anywhere
server farm,
gateways, proxies
broadcast
PSTN, CS core
gateways
MSC
firewall, GGSN,
gateway
SGSN
BSC
GSM
router
Internet
access
points
It is the ultimate
solution to the problem
of ubiquitous mobile
communications!
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Networking Issues in NGMN
SS7 signalling
IP-based
core
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private
WPAN
private
WLAN
RNC
UMTS
public
WLAN
ƒ Internetworking routing
and address allocation
ƒ Resource management
ƒ Traffic and congestion
control
ƒ Mobility among many
networks
ƒ Network address
translation
ƒ Network protocol
translation
CN
HSS
I-CSCF
MIP HA
P-CSCF
P-CSCF
P-CSCF
GGSN
MIP-FA
Visitor UMTS
N/W
PDSN
MIP-FA
Visitor
CDMA
2000
N/W
Destination
N/W
S-CSCF
IMS
Home
N/W
WiMAX
Core
WiMAX
ASN G/W
MIP-FA
SGSN
Visitor UMTS CN
Visitor UMTS CN
WiMAX
WiMAX
Data Flow
Signaling Flow via UMTS Interface
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Complex NGMN Design
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NGMN Requirements
ƒ Heterogeneous networking technologies, devices, and
services keep changing in NGMN over the time
Third party applications
and value added services
ƒ A centralized control/management tends to be an infeasible
approach for NGMN
Application
Access independent network
functionalities in a transparent
manner hiding access specific
signaling requirements
Access network interfaces,
connecting reconfigurable
SDR-based end terminals
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Mobility Mgmt.
APIs
Resource Mgmt.
Cross-layer
coordination
Service control and mechanism
essential for the smooth
operation of the network
architecture
ƒ The dynamic NGMN architecture and protocols need to
have
Security
Network
Services
&
OAM&P
ƒ scalability, self-organization, self-adaptation, and sustainability
ƒ By appropriately mapping key biological principles to
NGMN architectures it is possible to create a scalable,
self-organizable, self-adaptable, and sustainable network
QoS Mgmt.
Convergence sublayer
2G
3G
WLAN
ƒ Such network is motivated by the inspirations from various
biological systems’ abilities to naturally adapt to the changing
environment
Emerging
networks
Physical
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Hierarchical Wireless Sensor Networks
Vehicular Ad Hoc Networks
ƒ Sensor clustering for efficient routing
ITS and
infotainment
applications of
V2V and V2I
ƒ Layered topology design for better
data aggregation
ƒ Secure sensor networking
CH3
Sink
CH 1
CH4
CH5
CH2
Destination
ds1
FL2
n1
d1
n5
n1
n4
n1
source
Cluster
Node
n3
n3
FL1
n2
n2
Source
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MultiMulti-layered Wireless Mesh Networks
ƒ Developing a
new backbone
network for the
Internet
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Closing Remarks
ƒ Very little number of studies on biologically
inspired network models exist in the literature
Internet
ƒ Available models mainly imitate some biological
coordination aspects
IEEE 802.20 Tower
IEEE 802.20 Tower
Router
Router
ƒ Emergency
ƒ Fault tolerance
ƒ Increased
throughput
ƒ Reliability
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Router
ƒ As for the nature, however, they could have
great potential to assist with better and more
efficient network management in telecomm
networks, particularly for the future dynamic
non-centralized heterogeneous environment
IEEE 802.20 Tower
ƒ Applications:
Router
Router
Router
IEEE 802.20 Tower
Router
Router
Fixed Links
802.20 Cover
area
ƒ for scalability, self-organization, self adaptation, and
sustainability
Wi-Fi Cell
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Thank You
AJ
Abbas Jamalipour
[email protected]
Acknowledgment:
A. Jamalipour, 4 Parts
Feb 2009
of materials presented here are collected
Bioby Kumudu
Networking
S. Munasinghe from multiple sources.
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