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Over the past few years, a huge number of distributed camera calibration strategies have been proposed for video surveillance and monitoring systems involving mobile terminals. Many of the proposed solutions rely on consensus-based algorithms, which aim at estimating the configuration of the network via a message passing protocol. In this paper we propose an improved consensus-based distributed camera calibration strategy that exploits a robust initialization, together with a pruning protocol to remove faulty links which could propagate excessively-noisy information through the network reducing the convergence time. The proposed solution seems to improve the state-of-the-art strategies in terms of accuracy, convergence speed, and computational complexity.

In recent years complex networks have gained in-
creasing attention in different fields of science and engineering.
The problem of controlling these networks is an interesting and
challenging problem to investigate. In this paper we look at the
controllability problem focusing on the energy needed for the
control. Precisely not only we want to analyze whether a network
can be controlled, but we also want to establish whether the
control can be performed using a limited amount of energy.
We restrict our study to irreducible and (marginally) stable
networks and we find that the leading right and left eigenvectors
of the network matrix play a crucial role in this analysis.
Interestingly, our results suggest the existence of a connection
between controllability and network centrality, a well-known
concept in network science. In case the network is reversible, the
latter connection involves the PageRank, an extensively studied
type of centrality measure. Finally, the proposed results are
applied to examples concerning random graphs.