Abstract
Ubiquitous Sensor Networks (USNs) have the potential to generate large amounts of time sequenced raw data anytime and anywhere. In addition, the USN itself forms a dynamic system. As such, information about the system including its state variables and state transitions increases the data requirements inherent in USNs. Moreover, large-scale USNs including clustered organization, routing algorithms and power constraints increase the complexity of the system, further adding to the information requirements. In many cases, it is important for humans to understand the implications of the raw data or of the underlying system that the raw data is associated with, or, the operation of the USN itself. This is especially the case when supporting policy-based decision-making. Visualization is well-known to facilitate human understanding of data and its underlying systems. This talk proposes several visualization models including the Parameterized Iconic Glyph (PIG) and the Orthogonal Organized Finite State Machine (OOFSM) models as potential candidates for visualization models capable of addressing the large data size issues inherent in USNs. Example applications of these models to sensor acquired information are also described.