Abstract
Sensor networks employed by scientific applications often need to support localized collaboration of sensor nodes to perform in-network data processing. This includes new quantitative synthesis and hypothesis testing in near real time, as data streaming from distributed instruments, to transform raw data into high level domain-dependent information. This paper investigates in-network data processing mechanisms with dynamic data requirements in resource constrained heterogeneous sensor networks. Particularly, we explore how the temporal and spatial correlation of sensor measurements can be used to trade off between the complexity of coordination among sensor clusters and the savings that result from having fewer sensors involved in in-network processing, while maintaining an acceptable error threshold. Experimental results show that the proposed in-network mechanisms can facilitate the efficient usage of resources and satisfy data requirement in the presence of dynamics and uncertainty.
The research presented in this paper is supported in part by National Science Foundation via grants numbers CNS 0723594, IIP 0758566, IIP 0733988, CNS 0305495, CNS 0426354, IIS 0430826 and ANI 0335244,and by Department of Energy via the grant number DE-FG02-06ER54857, and was conducted as part of the NSF Center for Autonomic Computing at Rutgers University.
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Jiang, N., Parashar, M. (2008). In-Network Data Estimation for Sensor-Driven Scientific Applications. In: Sadayappan, P., Parashar, M., Badrinath, R., Prasanna, V.K. (eds) High Performance Computing - HiPC 2008. HiPC 2008. Lecture Notes in Computer Science, vol 5374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89894-8_27
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DOI: https://doi.org/10.1007/978-3-540-89894-8_27
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