Load Monitoring and Appliance Recognition Using an Inexpensive, Low-Frequency, Data-to-Image, Neural Network, and Network Mobility Approach for Domestic IoT Systems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10255067" target="_blank" >RIV/61989100:27240/24:10255067 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27740/24:10255067

Result on the web

<a href="https://ieeexplore.ieee.org/document/10347444" target="_blank" >https://ieeexplore.ieee.org/document/10347444</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/JIOT.2023.3340423" target="_blank" >10.1109/JIOT.2023.3340423</a>

Result language

angličtina

Original language name

Load Monitoring and Appliance Recognition Using an Inexpensive, Low-Frequency, Data-to-Image, Neural Network, and Network Mobility Approach for Domestic IoT Systems

Original language description

With the low integration costs and quick development cycle of all-IP-based 5G+ technologies, it is not surprising that the proliferation of IP devices for residential or industrial purposes is ubiquitous. Energy scheduling/management and automated device recognition are popular research areas in the engineering community, and much time and work have been invested in producing the systems required for smart city networks. However, most proposed approaches involve expensive and invasive equipment that produces huge volumes of data (high-frequency complexity) for analysis by supervised learning algorithms. In contrast to other studies in the literature, we propose an approach based on encoding consumption data into vehicular mobility and imaging systems to apply a simple convolutional neural network to recognize certain scenarios (devices powered on) in real time and based on the nonintrusive load monitoring paradigm. Our idea is based on a very cheap device and can be adapted at a very low cost for any real scenario. We have also created our own data set, taken from a real domestic environment, contrary to most existing works based on synthetic data. The results of the study&apos;s simulation demonstrate the effectiveness of this innovative and low-cost approach and its scalability in function of the number of considered appliances.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20203 - Telecommunications

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Internet of Things Journal

ISSN

2327-4662

e-ISSN

2327-4662

Volume of the periodical

11

Issue of the periodical within the volume

8

Country of publishing house

US - UNITED STATES

Number of pages

19

Pages from-to

13961-13979

UT code for WoS article

001203466500057

EID of the result in the Scopus database

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