"Chatty Devices” and edge-based activity classification

Lakoju, Mike, Javed, Amir ORCID: https://orcid.org/0000-0001-9761-0945, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646, Burnap, Peter ORCID: https://orcid.org/0000-0003-0396-633X, Atiba, Samuelson ORCID: https://orcid.org/0000-0001-5913-4503 and Cherkaoui, Soumaya 2021. "Chatty Devices” and edge-based activity classification. Discover Internet of Things 1 , 5. 10.1007/s43926-021-00004-9

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Abstract

With increasing automation of manufacturing processes (focusing on technologies such as robotics and human-robot interaction), there is a realisation that the manufacturing process and the artefacts/products it produces can be better connected post-production. Built on this requirement, a “chatty' factory involves creating products which are able to send data back to the manufacturing/production environment as they are used, whilst still ensuring user privacy. The intended use of a product during design phase may different significantly from actual usage. Understanding how this data can be used to support continuous product refinement, and how the manufacturing process can be dynamically adapted based on the availability of this data provides a number of opportunities. We describe how data collected on product use can be used to: (i) classify product use; (ii) associate a label with product use using unsupervised learning—making use of edge-based analytics; (iii) transmission of this data to a cloud environment where labels can be compared across different products of the same type. Federated learning strategies are used on edge devices to ensure that any data captured from a product can be analysed locally (ensuring data privacy).

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
ISSN:	2730-7239
Funders:	UK Engineering and Physical Sciences Research Council (EPSRC)
Date of First Compliant Deposit:	23 February 2021
Date of Acceptance:	24 December 2020
Last Modified:	13 Jun 2023 18:32
URI:	https://orca.cardiff.ac.uk/id/eprint/138280

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