Show simple item record

dc.contributor.authorSudharsan, Bharath
dc.contributor.authorPatel, Pankesh
dc.contributor.authorWahid, Abdul
dc.contributor.authorYahya, Muhammad
dc.contributor.authorBreslin, John G.
dc.contributor.authorAli, Muhammad Intizar
dc.date.accessioned2021-05-21T10:26:16Z
dc.date.available2021-05-21T10:26:16Z
dc.date.issued2021-05-18
dc.identifier.citationSudharsan, Bharath, Patel, Pankesh, Wahid, Abdul, Yahya, Muhammad, Breslin, John G., & Ali, Muhammad Intizar. (2021). Porting and Execution of Anomalies Detection Models on Embedded Systems in IoT: Demo abstract. Paper presented at the Proceedings of the International Conference on Internet-of-Things Design and Implementation, Charlottesvle, VA, USA, 18-21 May, https://doi.org/10.1145/3450268.3453513en_IE
dc.identifier.urihttp://hdl.handle.net/10379/16776
dc.description.abstractIn the Industry 4.0 era, Microcontrollers (MCUs) based tiny embedded sensor systems have become the sensing paradigm to interact with the physical world. In 2020, 25.6 billion MCUs were shipped, and over 250 billion MCUs are already operating in the wild. Such low-power, low-cost MCUs are being used as the brain to control diverse applications and soon will become the global digital nervous system. In an Industrial IoT setup, such tiny MCU-based embedded systems are equipped with anomaly detection models and mounted on production plant machines for monitoring the machine’s health/condition. These models process the machine’s health data (from temperature, RPM, vibration sensors) and raise timely alerts when it predicts/detects data patterns that show deviations from the normal operation state. In this demo, we train One Class Support Vector Machines (OCSVM) based anomaly detection models and port the trained models to their MCU executable versions. We then deploy and execute the ported models on 4 popular MCUs and report their on-board inference performance along with their memory (Flash and SRAM) consumption. The steps/procedure that we show in the demo is generic, and the viewers can use it to efficiently port a wide variety of datasets-trained classifiers and execute them on different resource-constrained MCU and small CPU-based devices.en_IE
dc.description.sponsorshipThis publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/16/RC/3918 (Confirm) and also by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289_P2 (Insight), with both grants co-funded by the European Regional Development Fund.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherAssociation for Computing Machinery (ACM)en_IE
dc.relation.ispartofIoTDI '21: Proceedings of the International Conference on Internet-of-Things Design and Implementationen
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectOffline Inferenceen_IE
dc.subjectIntelligent Embedded Systemsen_IE
dc.subjectEdge AIen_IE
dc.titlePorting and execution of anomalies detection models on embedded systems in IoT: Demo abstracten_IE
dc.typeDemonstration paperen_IE
dc.date.updated2021-05-21T09:37:17Z
dc.identifier.doi10.1145/3450268.3453513
dc.local.publishedsourcehttps://doi.org/10.1145/3450268.3453513en_IE
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funderScience Foundation Irelanden_IE
dc.contributor.funderEuropean Regional Development Funden_IE
dc.internal.rssid25999428
dc.local.contactBharath Sudharsan, Insight Centre For Data Analytics, Nui Galway. Email: b.sudharsan1@nuigalway.ie
dc.local.copyrightcheckedYes
dcterms.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en_IE
nui.item.downloads46


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)