1. Speaker: Ao Weng Chon Advisor: Kwang-Cheng Chen
Complex network
Speaker: Ao Weng 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 TC satisfies

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 This view provides
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 This view provides
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, (2009)
[2] Joel C. Miller, Percolation in clustered networks, Arxiv preprint arXiv: v2, 2009.
[3] W. Ren, Q. Zhao, and A.Swami, “Connectivity of Heterogeneous Wireless Networks”, Arxiv preprint arXiv: v5, 2009.
[4] Vince Poor, Lecture presented in First School of Information Theory, State Collega, PA, June 5, 2008.