Cognitive Networks: Towards Self-Aware Networks

1.5. Conclusion

In this chapter we discussed some basic principles and examples of biologically inspired networking. Methods from biology find increasing interest among the research community due to their attractive features like scalability and resilience to changes in the environment. By getting inspiration from biological systems, we can establish fully distributed and self-organizing networks. Especially networks which operate under no coordinating unit with highly autonomous nodes can profit from such methods. Furthermore, by utilizing the inherent fluctuations and noise in biologically inspired networks, they become more tolerant to perturbations, resulting in a greater stability and resilience.

However, we should also keep the following limitations in mind. Biologically inspired methods are often slower in reaction than conventional control algorithms. The reason for this lies in the way the adaptation is performed in nature. Often many thousand generations are needed for a species to evolve and adapt to a changing environment. Furthermore, adaptation is often done without knowing the target function, but simply by negative feedback. Individuals which have mutated to bear unfavorable features with disadvantages compared to other individuals will die out (survival of the fittest). This trial-and-error development is often driven by fluctuations and is thus not a directed development to a certain genetic feature. When biological methods are applied, it should be considered ...