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End-to-end anytime solution for appliance recognition based on high-resolution current sensing with few-shot learning
Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
2020 (Engelska)Ingår i: Internet of Things, ISSN 2543-1536, Vol. 11, artikel-id 100263Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

With the steady rise of home and building automation management system, it is becoming paramount to gain access to information that reflects consumption patterns with devicelevel granularity. Various application-level services can then makes use of this data for monitoring and controlling purposes in an efficient manner. In this paper we report on the design and development of an Internet of Things (IoT) end-to-end solution for electric appliance recognition that can operate in real-time and entails low hardware cost. For the task of identifying various appliance signatures we also provide a comparative analysis, where on the one hand, we investigate the suitability of several machine learning approaches given publicly available datasets, that generally provide months worth of data with a relatively low sampling frequency. On the other hand, we proceed to evaluate their discriminative effectiveness for our particular scenario, where the goal is to provide rapid identification of the appliance signature in real-time based on a reduced training dataset (few-shot learning). This is particularly important in the context of appliance recognition, where due to the high variance in consumption patterns within each class, in order to achieve high accuracy, data points often need to be collected for each individual appliance or device that would need to be later identified. Clearly, this data collection process is often expensive and difficult to perform, especially in large-scale settings, hence few-shot learning is key. Besides presenting our end-to-end IoT solution that meets the abovementioned desiderata, the paper also provides an analysis of the computational demand of such an approach with regard to cost and real-time performance, which is often critical to low-powered IoT solutions. (C) 2020 The Authors. Published by Elsevier B.V.

Ort, förlag, år, upplaga, sidor
Elsevier, 2020. Vol. 11, artikel-id 100263
Nyckelord [en]
Internet of things, Machine learning
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:mau:diva-48232DOI: 10.1016/j.iot.2020.100263ISI: 000695695100060Scopus ID: 2-s2.0-85102696167OAI: oai:DiVA.org:mau-48232DiVA, id: diva2:1621265
Tillgänglig från: 2021-12-17 Skapad: 2021-12-17 Senast uppdaterad: 2024-02-05Bibliografiskt granskad

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Mihailescu, Radu-Casian

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