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Due to their high fault-tolerance and scalability to large networks, consensus-based distributed algorithms have recently gained immense popularity in the sensor networks community. Large-scale camera networks are a special case. In a consensus-based state estimation framework, multiple neighboring nodes iteratively communicate with each other, exchanging their own local information about each target's state with the goal of converging to a single state estimate over the entire network. However, the state estimation problem becomes challenging when some nodes have limited observability of the state. In addition, the consensus estimate is suboptimal when the cross-covariances between the individual state estimates across different nodes are not incorporated in the distributed estimation framework. The cross-covariance is usually neglected because the computational and bandwidth requirements for its computation become unscalable for a large network. These limitations can be overcome by noting that, as the state estimates at different nodes converge, the information at each node becomes correlated. This fact can be utilized to compute the optimal estimate by proper weighting of the prior state and measurement information. Motivated by this idea, we propose information-weighted consensus algorithms for distributed maximum a posteriori parameter estimation, and their extension to the information-weighted consensus filter (ICF) for state estimation. We compare the performance of the ICF with existing consensus algorithms analytically, as well as experimentally by considering the scenario of a distributed camera network under various operating conditions.