In this paper, we focus on a new distributed input and state estimation architecture, where nodes of a given sensor network are allowed to have heterogeneous information roles in the sense that a subset of nodes can be active (that is, subject to observations of a process of interest) and the rest can be passive (that is, subject to no observations). Moreover, these nodes are allowed to have nonidentical sensor modalities under the common underlying assumption that they have complimentary properties distributed over the sensor network to achieve collective observability. The key feature of our framework is that it utilizes local information not only during the execution of the proposed distributed input and state estimation architecture but also in its design; that is, global stability is guaranteed once each node satisfies given local stability conditions (independent from the graph topology and neighboring information of these nodes). Several examples are provided to demonstrate the efficacy of the proposed approach.