We study the distributed estimation and detection problem in a complex network,
where each networked node converges to the same intelligence just based on local
observations and information exchanges with its neighbors. For the estimation
problem, we estimate the state of a potentially unstable linear dynamical system
in the framework of distributed Kalman filtering. In particular, it is shown
that, in a weakly connected communication network, there exist (randomized)
gossip based information dissemination schemes leading to a stochastically
bounded estimation error at each node for any non-zero rate of inter-node
communication. A gossip-based information exchange protocol, the M-GIKF, is
presented. Under the assumption of global detectability of the
signal/observation model, it is shown