  Distributed estimation and processing in networks modeled by graphs have
received a great deal of interest recently, due to the benefits of
decentralised processing in terms of performance and robustness to
communications link failure between nodes of the network. Diffusion-based
algorithms have been demonstrated to be among the most effective for
distributed signal processing problems, through the combination of local node
estimate updates and sharing of information with neighbour nodes through
diffusion. In this work, we develop a serial-inspired approach based on
message-passing strategies that provides a significant improvement in
performance over prior art. The concept of serial processing in the graph has
been successfully applied in sum-product based algorithms and here provides
inspiration for an algorithm which makes use of the most up-to-date information
in the graph in combination with the diffusion approach to offer improved
performance.
