Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-29611Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Gkikopoulos, Panagiotis | - |
| dc.contributor.author | Kropf, Peter | - |
| dc.contributor.author | Schiavoni, Valerio | - |
| dc.contributor.author | Spillner, Josef | - |
| dc.date.accessioned | 2024-01-19T07:41:01Z | - |
| dc.date.available | 2024-01-19T07:41:01Z | - |
| dc.date.issued | 2022-11-22 | - |
| dc.identifier.isbn | 9781450399173 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/29611 | - |
| dc.description.abstract | IoT systems rely on collected data to operate autonomously and generate insights. Such systems commonly produce redundant measurements, which can be insufficient to mitigate complex data disagreements. We believe a well-defined process to achieve internal ground truth through fusion is needed. Leveraging two case studies, we show how sensor data fusion with variants of history-aware voting can help to reconcile observations. We contribute a specification scheme with unified format to define the parameters and characteristics of a particular voting scenario, supporting reliable decision-making. Finally, we deploy and evaluate a novel method of bootstrapping historical records of sensor modules using a clustering algorithm. This method boosts the convergence of the measurements by 4x. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | ACM | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject | Voting algorithm | de_CH |
| dc.subject | Data quality | de_CH |
| dc.subject | Data fusion | de_CH |
| dc.subject | IoT | de_CH |
| dc.subject.ddc | 005: Computerprogrammierung, Programme und Daten | de_CH |
| dc.title | AVOC : history-aware data fusion for reliable IoT analytics | de_CH |
| dc.type | Konferenz: Paper | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institut für Informatik (InIT) | de_CH |
| dc.identifier.doi | 10.1145/3564695.3564772 | de_CH |
| dc.identifier.doi | 10.21256/zhaw-29611 | - |
| zhaw.conference.details | 23rd ACM/IFIP International Middleware Conference (Middleware), Québec City, Canada, 7-11 November 2022 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.publication.status | acceptedVersion | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.title.proceedings | Proceedings of the 23rd International Middleware Conference Industrial Track | de_CH |
| zhaw.webfeed | Service Engineering | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2022_Gkikopoulos-etal_AVOC-history-aware-data-fusion-reliable-IoT-analytics.pdf | Accepted Version | 623.95 kB | Adobe PDF |  View/Open |
Show simple item record
Gkikopoulos, P., Kropf, P., Schiavoni, V., & Spillner, J. (2022, November 22). AVOC : history-aware data fusion for reliable IoT analytics. Proceedings of the 23rd International Middleware Conference Industrial Track. https://doi.org/10.1145/3564695.3564772
Gkikopoulos, P. et al. (2022) ‘AVOC : history-aware data fusion for reliable IoT analytics’, in Proceedings of the 23rd International Middleware Conference Industrial Track. ACM. Available at: https://doi.org/10.1145/3564695.3564772.
P. Gkikopoulos, P. Kropf, V. Schiavoni, and J. Spillner, “AVOC : history-aware data fusion for reliable IoT analytics,” in Proceedings of the 23rd International Middleware Conference Industrial Track, Nov. 2022. doi: 10.1145/3564695.3564772.
GKIKOPOULOS, Panagiotis, Peter KROPF, Valerio SCHIAVONI und Josef SPILLNER, 2022. AVOC : history-aware data fusion for reliable IoT analytics. In: Proceedings of the 23rd International Middleware Conference Industrial Track. Conference paper. ACM. 22 November 2022. ISBN 9781450399173
Gkikopoulos, Panagiotis, Peter Kropf, Valerio Schiavoni, and Josef Spillner. 2022. “AVOC : History-Aware Data Fusion for Reliable IoT Analytics.” Conference paper. In Proceedings of the 23rd International Middleware Conference Industrial Track. ACM. https://doi.org/10.1145/3564695.3564772.
Gkikopoulos, Panagiotis, et al. “AVOC : History-Aware Data Fusion for Reliable IoT Analytics.” Proceedings of the 23rd International Middleware Conference Industrial Track, ACM, 2022, https://doi.org/10.1145/3564695.3564772.
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