EurekaMag.com logo
+ Site Statistics
References:
53,869,633
Abstracts:
29,686,251
+ Search Articles
+ Subscribe to Site Feeds
EurekaMag Most Shared ContentMost Shared
EurekaMag PDF Full Text ContentPDF Full Text
+ PDF Full Text
Request PDF Full TextRequest PDF Full Text
+ Follow Us
Follow on FacebookFollow on Facebook
Follow on TwitterFollow on Twitter
Follow on LinkedInFollow on LinkedIn

+ Translate

RAZOR: a compression and classification solution for the Internet of Things



RAZOR: a compression and classification solution for the Internet of Things



Sensors 14(1): 68-94



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.

(PDF emailed within 0-6 h: $19.90)

Accession: 055333529

Download citation: RISBibTeXText

PMID: 24451454

DOI: 10.3390/s140100068



Related references

Data management for the internet of things: design primitives and solution. Sensors 13(11): 15582-15612, 2013

Classification based on pruning and double covered rule sets for the internet of things applications. Thescientificworldjournal 2014: 984375-984375, 2014

Multivariable Sensors for Ubiquitous Monitoring of Gases in the Era of Internet of Things and Industrial Internet. Chemical Reviews 116(19): 11877-11923, 2016

The Internet of things. Scientific American 291(4): 76-81, 2004

The Internet of Things (IoT). Nursing Education Perspectives 34(1): 63-64, 2013

Research, the Internet, and the way things are. Health Education & Behavior 27(6): 695-7; Discussion 698, 2000

The Internet Of Caring Things. Provider 42(3): 20-2, 24-6, 29, 2016

Classification of adults with problematic internet experiences: linking internet and conventional problems from a clinical perspective. Cyberpsychology & Behavior 10(3): 381-392, 2007

Internet of things for an age-friendly healthcare. Studies in Health Technology and Informatics 210: 587-591, 2016

Ethical Design in the Internet of Things. Science and Engineering Ethics (): -, 2016

Sensing in the collaborative Internet of Things. Sensors 15(3): 6607-6632, 2015

How to search the Internet and ... find things. L'infirmiere du Quebec 4(5): 12-13, 1997

Smart Medications & the Internet of Things. Studies in Health Technology and Informatics 221: 116-116, 2016

The "Internet of Things": What It Is and What It Means for Libraries. Medical Reference Services Quarterly 34(3): 353-358, 2015

Internet of Things & Personalized Healthcare. Studies in Health Technology and Informatics 221: 129-129, 2016