  The association of weights in a distributed consensus protocol quantify the
trust that an agent has on its neighbors in a network. An important problem in
such networked systems is the uncertainty in the estimation of trust between
neighboring agents, coupled with the losses arising from mistakenly associating
wrong amounts of trust with different neighboring agents. We introduce a
probabilistic approach which uses the historical data collected in the network,
to determine the level of trust between each agent. Specifically, using the
finite history of the shared data between neighbors, we obtain a configuration
which represents the confidence estimate of every neighboring agent's
trustworthiness. Finally, we propose a History-Data-Driven (HDD) distributed
consensus protocol which translates the computed configuration data into
weights to be used in the consensus update. The approach using the historical
data in the context of a distributed consensus setting marks the novel
contribution of our paper.
