Distributed Sensor Networks have evolved from the early networks of sensors coupled with processing elements to wireless networks of resource-constrained embedded devices. Such networks usher in new paradigms for computation, control and communication. Data Fusion is an important application for Distributed Sensor Networks as it facilitates the synthesis of new information by integrating data from multiple sensors in a deterministic, time-critical and reliable manner. In general, sensors are used either in complementary, competitive, or collaborative modes. The mode of the sensors forces a consideration of architectural issues. In this paper, we explore the landscape of architectures for Distributed Sensor Networks and identify the critical elements that are essential for Data Fusion.