A Network Emergent Computing Model Based on Cellular Automaton

Emergent computation is a relatively new approach for understanding the behaviors of complex systems. Central to this approach is the idea that system-level behavior emerges from interaction among individual elements. This paper proposes a network emergent computation model based on cellular automaton, which introduces network operating mechanism, such as store-and-forward, adjacency interaction, rate adjustment, resource competition and delayed feedback as the interactions among individual cells. The simulation results show that the system-level behaviors, such as power-law, self-similarity and 1/f´ noise, can be generated spontaneously by local nonlinear interaction among cells, which have led to a better understanding of the macro-behavior of the system from the perspective of microscopic mechanism.