Reliable IoT analytics at scale

Gkikopoulos, Panagiotis; Kropf, Peter; Schiavoni, Valerio; Spillner, Josef

doi:10.1016/j.jpdc.2024.104840

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29645

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Reliable IoT analytics at scale
Authors:	Gkikopoulos, Panagiotis Kropf, Peter Schiavoni, Valerio Spillner, Josef
et. al:	No
DOI:	10.1016/j.jpdc.2024.104840 10.21256/zhaw-29645
Published in:	Journal of Parallel and Distributed Computing
Volume(Issue):	187
Issue:	104840
Issue Date:	1-May-2024
Publisher / Ed. Institution:	Elsevier
ISSN:	0743-7315 1096-0848
Language:	English
Subjects:	Consensus voting; Data reliability; Data fusion; Sensor redundancy
Subject (DDC):	005: Computer programming, programs and data
Abstract:	Societies and legislations are moving towards automated decision-making based on measured data in safety-critical environments. Over the next years, density and frequency of measurements will increase to generate more insights and get a more solid basis for decisions, including through redundant low-cost sensor deployments. The resulting data characteristics lead to large-scale system design in which small input data errors may lead to severe cascading problems including ultimately wrong decisions. To ensure internal data consistency to mitigate this risk in such IoT environments, fast-paced data fusion and consensus among redundant measurements need to be achieved. In this context, we introduce history-aware sensor fusion powered by accurate voting with clustering as a promising approach to achieve fast and informed consensus, which can converge to the output up to 4X faster than the state of the art history-based voting. Leveraging three case studies, we investigate different voting schemes and show how this approach can improve data accuracy by up to 30% and performance by up to 12% compared to state-of-the-art sensor fusion approaches. We furthermore contribute a specification format for easily deploying our methods in practice and use it to develop a pilot implementation.
URI:	https://digitalcollection.zhaw.ch/handle/11475/29645
Related research data:	https://doi.org/10.5281/zenodo.8069916
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Engineering
Organisational Unit:	Institute of Computer Science (InIT)
Published as part of the ZHAW project:	Innenraumnavigation für personalisiertes Einkaufen
Appears in collections:	Publikationen School of Engineering

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Gkikopoulos, P., Kropf, P., Schiavoni, V., & Spillner, J. (2024). Reliable IoT analytics at scale. Journal of Parallel and Distributed Computing, 187(104840). https://doi.org/10.1016/j.jpdc.2024.104840

Gkikopoulos, P. et al. (2024) ‘Reliable IoT analytics at scale’, Journal of Parallel and Distributed Computing, 187(104840). Available at: https://doi.org/10.1016/j.jpdc.2024.104840.

P. Gkikopoulos, P. Kropf, V. Schiavoni, and J. Spillner, “Reliable IoT analytics at scale,” Journal of Parallel and Distributed Computing, vol. 187, no. 104840, May 2024, doi: 10.1016/j.jpdc.2024.104840.

GKIKOPOULOS, Panagiotis, Peter KROPF, Valerio SCHIAVONI und Josef SPILLNER, 2024. Reliable IoT analytics at scale. Journal of Parallel and Distributed Computing. 1 Mai 2024. Bd. 187, Nr. 104840. DOI 10.1016/j.jpdc.2024.104840

Gkikopoulos, Panagiotis, Peter Kropf, Valerio Schiavoni, and Josef Spillner. 2024. “Reliable IoT Analytics at Scale.” Journal of Parallel and Distributed Computing 187 (104840). https://doi.org/10.1016/j.jpdc.2024.104840.

Gkikopoulos, Panagiotis, et al. “Reliable IoT Analytics at Scale.” Journal of Parallel and Distributed Computing, vol. 187, no. 104840, May 2024, https://doi.org/10.1016/j.jpdc.2024.104840.