Technical advances are leading to a pervasive computational infrastructure that integrates computational processes with embedded sensors and actuators, and giving rise to a new paradigm for monitoring, understanding, and managing natural and engineered systems – one that is information/data-driven. However, developing and deploying these applications remains a challenge, primarily due to the lack of programming and runtime support. This paper addresses these challenges and presents a programming system for end-to-end sensor/actuator-based scientific and engineering applications. Specifically, the programming system provides semantically meaningful abstractions and runtime mechanisms for integrating sensor systems with computational models for scientific processes, and for in-network data processing such as aggregation, adaptive interpolation and assimilations. The overall architecture of the programming system and the design of its key components, as well as its prototype implementation are described. An end-to-end dynamic data-driven oil reservoir application that combines reservoir simulation models with sensors/actuators in an instrumented oilfield is used as a case study to demonstrate the operation of the programming system, as well as to experimentally demonstrate its effectiveness and performance.
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- Enabling End-to-End Data-Driven Sensor-Based Scientific and Engineering Applications
- Springer Berlin Heidelberg