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Collaborative storage management in sensor networks

Collaborative storage management in sensor networks In this paper, we consider a class of sensor networks where the data is not required in real-time by an observer; for example: a sensor network monitoring a scientific phenomenon for later play back and analysis. In such networks, the data must be stored in the network. Thus, in addition to battery power, storage is a primary resource; the useful lifetime of the network is constrained by its ability to store the generated data samples. We explore the use of collaborative storage techniques to efficiently manage data in storage constrained sensor networks. The proposed collaborative storage technique takes advantage of spatial correlation among the data collected by nearby sensors to significantly reduce the size of the data near the data sources. In addition, local coordination can be used to adjust the sampling rate to match the required application fidelity. We show that the proposed approach provides significant savings in the size of the stored data vs. local buffering. These savings allow the network to operate for a longer time without exhausting storage space. Furthermore, the savings reduce the amount of data that will eventually be relayed in response to queries or upon eventual collection of the data. In addition, collaborative storage performs load balancing of the available storage space if data generation rates are not uniform across sensors (as would be the case in an event driven sensor network), or if the available storage varies across the network. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Ad Hoc and Ubiquitous Computing Inderscience Publishers

Collaborative storage management in sensor networks

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1743-8225
eISSN
1743-8233
Publisher site
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Abstract

In this paper, we consider a class of sensor networks where the data is not required in real-time by an observer; for example: a sensor network monitoring a scientific phenomenon for later play back and analysis. In such networks, the data must be stored in the network. Thus, in addition to battery power, storage is a primary resource; the useful lifetime of the network is constrained by its ability to store the generated data samples. We explore the use of collaborative storage techniques to efficiently manage data in storage constrained sensor networks. The proposed collaborative storage technique takes advantage of spatial correlation among the data collected by nearby sensors to significantly reduce the size of the data near the data sources. In addition, local coordination can be used to adjust the sampling rate to match the required application fidelity. We show that the proposed approach provides significant savings in the size of the stored data vs. local buffering. These savings allow the network to operate for a longer time without exhausting storage space. Furthermore, the savings reduce the amount of data that will eventually be relayed in response to queries or upon eventual collection of the data. In addition, collaborative storage performs load balancing of the available storage space if data generation rates are not uniform across sensors (as would be the case in an event driven sensor network), or if the available storage varies across the network.

Journal

International Journal of Ad Hoc and Ubiquitous Computing – Inderscience Publishers

Published: Jan 1, 2005

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