Speaker: Ao Weng Chon Advisor: Kwang-Cheng Chen 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 Coexistence of primary users and secondary users

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

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.

Interference control Cooperative relays: Energy efficient

Interference control A way to capture interference range

Interference control Interference range reduces after cooperation

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

Epidemics

Epidemics

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

Epidemics Epidemic is possible when TC satisfies

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

Particle Swarm Optimization

Particle Swarm Optimization

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

Others Network resilience Search in social network Evolutionary game

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

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

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.

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.