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4/14/09 - The Spring 2009 (PDF) edition of the CAC newsletter is available.

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The University of Florida , the University of Arizona and Rutgers, the State University of New Jersey , have established a national research center for autonomic computing (CAC).

This center is funded by the Industry/University Cooperative Research Center program of the National Science Foundation, CAC members from industry and government, and university matching funds.

Programming Sensor-driven Autonomic Applications

Principal researchers: Nanyan Jiang, Manish Parashar, Dario Pompili (Rutgers)

Technical advances are leading to a pervasive computational ecosystem that integrates computing infrastructures with embedded sensors and actuators, and giving rise to a new paradigm for monitoring, understanding, and managing natural and engineered systems ¨C one that is information/data-driven and autonomic. The overarching goal of this research is to develop sensor system middleware and programming support that will enable distributed networks of sensors to function, not only as passive measurement devices, but as intelligent data processing instruments, capable of data quality assurance, statistical synthesis and hypotheses testing as they stream data from the physical environment to the computational world.

This research investigates a programming system to support the development of in-network data processing mechanisms, and enable applications to discover, query, interact with, and control instrumented physical systems using semantically meaningful abstractions [1]. This includes abstractions and runtime mechanisms for integrating sensor systems with applications processes, as well as for in-network data processing such as aggregation, adaptive interpolation and assimilation. The proposed programming systems enable sensor-driven applications at two levels. First, it provides programming abstractions for integrating sensor systems with computational models for applications processes and with other application components in an end-to-end experiment. Second, it provides programming abstractions and system software support [2] for developing in-network data processing mechanisms. Specifically for the latter, we explore the temporal and spatial correlation 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. Experimental results show that the proposed in-network mechanisms can facilitate the efficient usage of constraint resources and satisfy data requirement in the presence of dynamics and uncertainty.

Current efforts are focused on the implementations of the programming abstractions and optimizations across multiple overlapped in-network processing units. Other issues being addressed include comparing the proposed in-network mechanisms with other in-network data processing strategies, and providing a software infrastructure that will enable experts to experiments with different aspects of end-to-end dynamic data-driven autonomic application and systems, including data acquisition, data assimilation and uncertainty management, data transport, and dynamic data injection.

Reference

1. N. Jiang, C. Schmidt, M. Parashar, "A Decentralized Content-based Aggregation Service for Pervasive Environments," International Conference of Pervasive Services (ICPS), June, 2006.

2. N. Jiang and M. Parashar, "Programming Support for Sensor-based Scientific Applications," Proceedings of the Next Generation Software (NGS) Workshop, In conjunction with the 22nd IEEE International Parallel and Distributed Processing Symposium (IPDPS 2008), Miami, FL, USA, IEEE Computer Society Press, April, 2008.