Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 206-220 |
| Number of pages | 15 |
| Journal | Journal of Computational Science |
| Volume | 1 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 1 2010 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
- Modeling and Simulation
Keywords
- End-to-end system
- In-network processing
- Middleware
- Programming model
- Sensor-based scientific applications
Cite this
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A programming system for sensor-based scientific applications. / Jiang, Nanyan; Parashar, Manish.
In: Journal of Computational Science, Vol. 1, No. 4, 01.12.2010, p. 206-220.Research output: Contribution to journal › Article
TY - JOUR
T1 - A programming system for sensor-based scientific applications
AU - Jiang, Nanyan
AU - Parashar, Manish
PY - 2010/12/1
Y1 - 2010/12/1
N2 - 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.
AB - 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.
KW - End-to-end system
KW - In-network processing
KW - Middleware
KW - Programming model
KW - Sensor-based scientific applications
UR - http://www.scopus.com/inward/record.url?scp=78649511377&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78649511377&partnerID=8YFLogxK
U2 - 10.1016/j.jocs.2010.07.006
DO - 10.1016/j.jocs.2010.07.006
M3 - Article
AN - SCOPUS:78649511377
VL - 1
SP - 206
EP - 220
JO - Journal of Computational Science
JF - Journal of Computational Science
SN - 1877-7503
IS - 4
ER -