TY - GEN
T1 - In-network data estimation for sensor-driven scientific applications
AU - Jiang, Nanyan
AU - Parashar, Manish
N1 - Funding Information:
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.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - In-network data estimation
KW - Sensor system programming
KW - Sensor-driven scientific applications
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U2 - 10.1007/978-3-540-89894-8_27
DO - 10.1007/978-3-540-89894-8_27
M3 - Conference contribution
AN - SCOPUS:58449129764
SN - 354089893X
SN - 9783540898931
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 282
EP - 294
BT - High Performance Computing - HiPC 2008 - 15th International Conference, Proceedings
PB - Springer Verlag
T2 - 15th International Conference on High Performance Computing, HiPC 2008
Y2 - 17 December 2008 through 20 December 2008
ER -