Outline
     Challenges in future wireless networks
     Bio-inspired networking
       Example 1: ant colony
       Example 2: immune system
     Complex networks
       Network measures
       Network models
       Phenomena in complex networks
       Dynamical processes on complex networks
     Further research topics
     2
     Challenges in Future Wireless Networks
      Scalability
        By 2020, there will be trillion wireless devices [1] 
         (e.g. cell phone, laptop, health/safety care sensors
         , …)
      Adaptation
        Dynamic network condition and diverse user dema
         nd
      Resilience
        Robust to failure/malfunction of nodes and to intru
         ders
      3
    Bio-inspired Networking
     Biomimicry: studies designs and processes in 
      nature and then mimics them in order to solve 
      human problems [3]
     A number of principles and mechanisms in lar
      ge scale biological systems [2]
       Self-organization: Patterns emerge, regulated by f
        eedback loops, without existence of leader
       Autonomous actions based on local information/int
        eraction: Distributed computing with simple rule of 
        thumb
       Birth and death as expected events: Systems equi
        p with self-regulation
       Natural selection and evolution
      4 Optimal solution in some sense
     A special issue on bio-inspired networking will 
                                          nd
      be published in IEEE JSAC in 2  quarter 2010.
    Bio-inspired Networking
                     Math. Model 
                     Math. Model 
                      (Diff. eq., 
       Observatio     (Diff. eq., 
       Observatio                                    Algorithm 
                       prob.                         Algorithm 
                       prob.             Entities 
       n, verbal                         Entities 
       n, verbal                                    establishm
                      methods,                      establishm
                      methods,          mapping
       description                      mapping
       description                                     ent
                     fuzzy logic,                      ent
                     fuzzy logic,
                        …)
                        …)
                    Verification
       Parameter    Verification
       Parameter                        Performan
                         ,              Performan
                         ,                          Parameter
       evaluation,                                  Parameter
       evaluation,                         ce 
                     hypothesis            ce 
                     hypothesis                       tuning
       prediction                                     tuning
       prediction                       evaluation
                      testing           evaluation
                      testing
    Biological Modeling                     Engineering Applying
     5
              Example 1: Foraging of Ant Colony
               Stigmergy: interaction between ants is built on 
                     trail pheromone [6]
               Behaviors [6]:
                      Lay pheromone in both directions between food so
                           urce and nest
                      Amount of pheromone when go back to nest is acc
                           ording to richness of food source (explore richest r
                           esource)
                                                                                                                                                                                C
                           Pheromone intensity decreases over time due to e
                                                                                                                                                                                     1               C
                           vaporation                                                                                                                                                                     2
                                                                                                                       n                                             P
                              dC                                                                    (k C )                                                            1
                                    i  qP fC                                     P                            i                                                          P
                     Stochastic model (no trail-laying in backward):
                               dt               i        i             i                i         m                                                                              2
                                                                                                (kC)n
                                                                                                                      i
                                                                                                 j1                                                                      P
                                                                                                                                                                             m
                  6                                                                                                                                                                                  Cm