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Sensors 2014, 14(1), 68-94; doi:10.3390/s140100068
Article

RAZOR: A Compression and Classification Solution for the Internet of Things

1,* , 2
 and 1,3
Received: 30 October 2013; in revised form: 29 November 2013 / Accepted: 2 December 2013 / Published: 19 December 2013
(This article belongs to the Section Sensor Networks)
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Abstract: The Internet of Things is expected to increase the amount of data produced and exchanged in the network, due to the huge number of smart objects that will interact with one another. The related information management and transmission costs are increasing and becoming an almost unbearable burden, due to the unprecedented number of data sources and the intrinsic vastness and variety of the datasets. In this paper, we propose RAZOR, a novel lightweight algorithm for data compression and classification, which is expected to alleviate both aspects by leveraging the advantages offered by data mining methods for optimizing communications and by enhancing information transmission to simplify data classification. In particular, RAZOR leverages the concept of motifs, recurrent features used for signal categorization, in order to compress data streams: in such a way, it is possible to achieve compression levels of up to an order of magnitude, while maintaining the signal distortion within acceptable bounds and allowing for simple lightweight distributed classification. In addition, RAZOR is designed to keep the computational complexity low, in order to allow its implementation in the most constrained devices. The paper provides results about the algorithm configuration and a performance comparison against state-of-the-art signal processing techniques.
Keywords: signal processing; motif; compression; classification; computational complexity; Internet of Things signal processing; motif; compression; classification; computational complexity; Internet of Things
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Danieletto, M.; Bui, N.; Zorzi, M. RAZOR: A Compression and Classification Solution for the Internet of Things. Sensors 2014, 14, 68-94.

AMA Style

Danieletto M, Bui N, Zorzi M. RAZOR: A Compression and Classification Solution for the Internet of Things. Sensors. 2014; 14(1):68-94.

Chicago/Turabian Style

Danieletto, Matteo; Bui, Nicola; Zorzi, Michele. 2014. "RAZOR: A Compression and Classification Solution for the Internet of Things." Sensors 14, no. 1: 68-94.


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