Dense sensor deployments impose significant constraints on aggregate network data rate and resource utilization. Effective protocols for such data transfers rely on spatio-temporal correlations in sensor data for in-network data compression. The message complexity of these schemes is generally lower bounded by n, for a network with n sensors, since correlation is not collocated with sensing. Consequently, as the number of nodes and network density increase, these protocols become increasingly inefficient. We present here a novel protocol, called SNP, for fine-grained data collection, which requires approximately O(n∈-∈R) messages, where R, a measure of redundancy in sensed data generally increases with density. SNP uses spatio-temporal correlations to near-optimally compress data at the source, reducing network traffic and power consumption. We present a comprehensive information theoretic basis for SNP and establish its superior performance in comparison to existing approaches. We support our results with a comprehensive experimental evaluation of the performance of SNP in a real-world sensor network testbed. © 2008 Springer Berlin Heidelberg.
CITATION STYLE
Awan, A., Jagannathan, S., & Grama, A. (2008). Scalable data collection in sensor networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5374 LNCS, pp. 415–426). Springer Verlag. https://doi.org/10.1007/978-3-540-89894-8_37
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