Modeling and optimization of dynamic signal processing in resource-aware sensor networks

Sensor node processing in resource-aware sensor networks is often critically dependent on dynamic signal processing functionality - i.e., signal processing functionality in which computational structure must be dynamically assessed and adapted based on time-varying environmental conditions, operating constraints or application requirements. In dynamic signal processing systems, it is important to provide flexibility for run-time adaptation of application behavior and execution characteristics, but in the domain of resource-aware sensor networks, such flexibility cannot come with significant costs in terms of power consumption overhead or reduced predictability. In this paper, we review a variety of complementary models of computation that are being developed as part of the dataflow interchange format (DIF) project to facilitate efficient and reliable implementation of dynamic signal processing systems. We demonstrate these methods in the context of resource-aware sensor networks.