  Most distributed sensing methods assume that the expected value of sensed
information is same for all agents ignoring differences in sensor capabilities
due to, for example, environmental factors and sensors quality and condition.
In this paper, we present a distributed sensing framework to exploit
heterogeneity in information provided about a dynamic environment using an
active-passive networked multiagent systems approach. Specifically, this
approach consists of agents subject to exogenous inputs (active agents) and
agents without any inputs (passive agents). In addition, if an active agent
senses a quantity accurately (resp., not accurately), then it is weighted high
(resp., low) in the network such that these weights can be a function of time
due to varying environmental factors. The key feature of our approach is that
the states of all agents converge to an adjustable neighborhood of the weighted
average of the sensed exogenous inputs by the active agents.
