Impact of complex network's topology in distributed adaptive estimation based on diffusion cooperation

In this paper, the topology of complex adaptive networks based on distributed estimation least mean square (LMS) algorithm for the diffusion mode of cooperation (ATC) is studied . The study c overs different network models, including the regular, small - world and random assuming temporal and spatial dependence of data. The parameters used for implementation of complex networks are average path length, cluster coefficient and algebraic connectivi ty in addition to the performance of the network. The simulation results indicate that in all complex networks with ideal and noisy links, the small - world networks (for adding random links) are better candidates for practical implementations due to high al gebraic connectivity (robust to node failure problem) , average path length and cluster coefficients (strong locality). Simulation results are also included for the Metropolis coefficient combination.