A programming system for sensor-based scientific applications

Technical advances are leading to a pervasive computational ecosystem that integrates computing infrastructures with embedded sensors and actuators, and are giving rise to a new paradigm for monitoring, understanding, and managing natural and engineered systems - one that is information/data-driven. In this paper, we present a programming system that can support such end-to-end sensor-based dynamic data-driven applications. Specifically, the programming system enables these applications at two levels. First, it provides programming abstractions for integrating sensor systems with computational models for scientific and engineering processes and with other application components in an end-to-end experiment. Second, it provides programming abstractions and system software support for developing in-network data processing mechanisms. The former supports complex querying of the sensor system, while the latter enables development of in-network data processing mechanisms such as aggregation, adaptive interpolation and assimilation. Furthermore, for the latter, we also explore the use of temporal and spatial correlations of sensor measurements in the targeted application domains to tradeoff between the complexity of coordination among sensor clusters and the savings that result from having fewer sensors for in-network processing, while maintaining an acceptable error threshold. The research is evaluated using two application scenarios: the management and optimization of an instrumented oil field and the management and optimization of an instrumented data center. Experimental results show that the provided programming system reduces overheads while achieving near optimal and timely management and control in both application scenarios.