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Efficient filtering processes for machine-to-machine data based on automation modules and data-agnostic algorithms
NEC Laboratories Europe, NEC Europe Ltd., Heidelberg.
Sejong University, Department of Computer and Information Security, Gwangjin-Gu, Seoul.
NEC Corporation, 1753 Shimonumabe, Nakahara-ku, Kawasaki.
2014 (English)In: International Journal of Business Process Integration and Management, ISSN 1741-8763, E-ISSN 1741-8771, Vol. 7, no 1, p. 73-86Article in journal (Refereed) Published
Abstract [en]

Machine-to-machine (M2M) platforms are evolving as large-scale multi-layer solutions that unify the access and the control of all devices that are being equipped with the capability to perform automated tasks and to report data based on connectivity to a backend system. As the integration of more and more devices in such platforms results in the need to handle big M2M data, M2M platforms need to automate their configuration and include appropriate data filtering frameworks and algorithms. Otherwise, the collected raw data canbecome expensive, unmanageable, and of low quality. This paper presents how data filtering processes can be automated as part of an M2M self-configuration framework and describes a solution that enables the seamless adjustment of domain-specific filtering thresholds in domain-agnostic platforms, based on quality-of-information calculations and M2M-specific data categorisation. An evaluation from the facilities-monitoring domain shows that our approach was the only one to achieve, for example, forwarding less than 25% of the monitored data maintaining at the same time a coverage ratio bigger than 50% for all considered applications. Further, a projection of this evaluation to a Smart City scale indicates that such gains can makedatabase queries up to many seconds faster.

Place, publisher, year, edition, pages
2014. Vol. 7, no 1, p. 73-86
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-10874DOI: 10.1504/IJBPIM.2014.060606Local ID: 9c100f9f-8e97-4ebf-8387-2a2b2d8f94eeOAI: oai:DiVA.org:ltu-10874DiVA, id: diva2:983822
Note
Upprättat; 2014; 20150921 (missch)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-01-10Bibliographically approved

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Schmidt, Mischa

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