A System-Level Design Approach For Dynamic Resource Coordination And Energy Optimization In Sensor Network Platforms

Strict run-time and resource constraints in wireless sensor networks (WSNs) introduce complex design problems that need to be addressed systematically. Recent processor platforms for WSNs have groups of peripheral devices that are used for data sensing and processing. Hardware interrupts are commonly used as an efficient method for handling data acquisition from such peripherals. Dynamic control for multiple interrupts and efficient handling of power consumption on embedded processors are important issues when implementing dynamic, data-driven signal processing applications, where the structure of processing subsystems may need to be adapted at run-time based characteristics of input data and associated operating conditions.To address these issues, we introduce a dataflow-based design approach based on integrating interrupt-based signal acquisition in the context of parameterized synchronous dataflow (PSDF) modeling. This application of PSDF provides a useful foundation for structured development of power-and energy-efficient wireless sensor network systems for dynamic, data-driven applications systems (DDDAS), including DDDAS that employ intensive acquisition and processing of signals from heterogeneous sensors. To demonstrate our proposed new signal-processing-oriented, dataflow-based design approach - which we refer to as DDPSDF (data-driven PSDF) - we have implemented an embedded speech recognition system using the proposed DDPSDF techniques. We demonstrate that by applying our DDPSDF approach, energy-and resource-efficient embedded software can be derived systematically from high level models of DDDAS functional structure.