complex network
DESCRIPTION
Complex network. Speaker: Ao Weng Chon Advisor: Kwang -Cheng Chen. Outline. Interference control Epidemics Bio-inspired networking Particle Swarm Optimization Ant Colony Optimization Further directions Reference. Interference control. - PowerPoint PPT PresentationTRANSCRIPT
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Complex network
Speaker: Ao Weng ChonAdvisor: Kwang-Cheng Chen
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Outline Interference control Epidemics Bio-inspired networking
Particle Swarm Optimization Ant Colony Optimization
Further directions Reference
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Interference control
Coexistence of primary users and secondary users
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Interference control
SUs should defer their transmission activities when located in the inference ranges of PUs.
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Interference control
When the deferred SUs acted as cooperative relays, they facilitate PUs transmissions, reduce the interference ranges of PUs and expose extra spectrum opportunities.
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Interference control
Cooperative relays: Energy efficient 2 2 2
1 2d d d
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Interference control
A way to capture interference range
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Interference control
Interference range reduces after cooperation
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Interference control
The necessary condition of existence of an infinite connected component in the SUs is the interference balls of PUs (wall width is rp) do not form an infinite connected component
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1
I
PS r
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Epidemics
Internet
Suspect UE
Suspect UE
Suspect UE
BT virus
BT virus
SMS virus
Infected UE
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Epidemics
I
S
S
S
S
S
S
S
S
S
S
SS
S
S
S
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Random Geometric Graph
E-R Model
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Epidemics
The one hop BT motif can be replaced by a complete graph with 4 or more vertices
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Epidemics
Epidemic is possible when TC satisfies
2 2
2
( 1) ( )( 1) ( )
( )( )
2 (1 ) 0
SMS SMS BT SMSSMS SMS
SMS BT BT BTBT BT
SMS SMS SMS SMSSMS
SMS SMSBT
BT
C Cn t n tC Cn t n t
k k k kT T n tk k
n tT T T P
1det 0
1SMS SMS BT SMS
SMS BT BT BT
C CC C
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Bio-inspired networking Biomimicry: studies designs and processes in
nature and then mimics them in order to solve human problems
A number of principles and mechanisms in large scale biological systems Self-organization: Patterns emerge, regulated by
feedback loops, without existence of leader Autonomous actions based on local
information/interaction: Distributed computing with simple rule of thumb
Birth and death as expected events: Systems equip with self-regulation
Natural selection and evolution Optimal solution in some sense
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Particle Swarm Optimization
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Particle Swarm Optimization
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Ant Colony Optimization Interaction between ants is built on trail
pheromone Behaviors:
Lay pheromone in both directions between food source and nest
Amount of pheromone when go back to nest is according to richness of food source (explore richest resource)
Pheromone intensity decreases over time due to evaporation
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Others Network resilience Search in social network Evolutionary game
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Further directions Economorphic Networking
Competition in Communication Networks Nodes can be viewed as economic agents, each
seeking to maximize its own utility (e.g., energy/spectral efficiency): Non-cooperative games: nodes compete for radio
resources Auctions: nodes bid for network resources Coalition games: incentives to nodes for good behavior
This view provides new understanding of network behavior, new design tools, and is based on individualized node behavior
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Further directions Sociomorphic Networking
Collaboration in Networks Network nodes work together
Collaboration: nodes work together for a common goal Cooperation: nodes help each other to achieve
individual goals This view provides
new algorithms, new protocols, and is based on collective behavior of nodes
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Further directions Bio-inspired networking
Devices are mobile and autonomous, and must adapt to the surrounding environment in a distributed way.
To discover and adapt biological methods to technical solutions that are showing similarly high stability, adaptability, and scalability as biological entities often have.
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References [1] Newman, M. E. J., Random graphs with Clustering, Phys. Rev. L 103, 058701 (2009) [2] Joel C. Miller, Percolation in clustered networks, Arxiv preprint arXiv:0904.3253v2, 2009. [3] W. Ren, Q. Zhao, and A.Swami, “Connectivity of Heterogeneous Wireless Networks”, Arxiv preprint
arXiv:0903.1684v5, 2009. [4] Vince Poor, Lecture presented in First School of Information Theory, State Collega, PA, June 5, 2008.