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Region-Based Compressive Networked Storage with Lazy Encoding
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DOI：10.1109/TPDS.2018.2883550.png)
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Abstract
En 中文
Existing work on distributed networked storage, although extensive, has generally focused on the recovery of global data field covering the entire network. This, while demanded by a broad range of applications, has ignored cases where only a subset of the data are needed, for example, from a local region of the network. Based on this observation and the fact that the sensor readings are correlated, this paper proposes a compressive networked storage solution. Specifically, by employing the compressive sensing ( CS) theory, we present a lazy-encoding algorithm with local dissemination and a region-based reconstruction algorithm. Utilizing our local dissemination strategy, sensor readings only have to be disseminated and stored in their respective regions, which makes the dissemination cost decrease significantly. With the lazy-encoding algorithm, the readings in specified local regions are capable of being encoded individually, dramatically reducing the decoding ratio. The region-based reconstruction algorithm is introduced to explore the inter-region correlation, aiming at offering improved data accuracy. We further provide the mathematical foundation that our reconstruction algorithm could ensure efficient CS recovery. Experimental results using real sensor readings show that the proposed scheme is especially beneficial to the recovery of local data. At the same time, our scheme can recover the global data field as well without increasing reconstruction error.
Keywords:
Compressive sensing
data storage
sensor network
encoding
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