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Complex network

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.

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Complex network

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  1. Complex network Speaker: AoWeng Chon Advisor: Kwang-Cheng Chen

  2. Outline • Interference control • Epidemics • Bio-inspired networking • Particle Swarm Optimization • Ant Colony Optimization • Further directions • Reference

  3. Interference control • Coexistence of primary users and secondary users

  4. Interference control • SUs should defer their transmission activities when located in the inference ranges of PUs.

  5. 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.

  6. Interference control • Cooperative relays: • Energy efficient

  7. Interference control • A way to capture interference range

  8. Interference control • Interference range reduces after cooperation

  9. 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

  10. Epidemics

  11. Epidemics

  12. Epidemics • The one hop BT motif can be replaced by a complete graph with 4 or more vertices

  13. Epidemics • Epidemic is possible when TCsatisfies

  14. 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

  15. Particle Swarm Optimization

  16. Particle Swarm Optimization

  17. 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

  18. Others • Network resilience • Search in social network • Evolutionary game

  19. 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

  20. 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

  21. 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.

  22. 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.

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