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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27740/24:10255067

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

IEEE Internet of Things Journal

ISSN

2327-4662

e-ISSN

2327-4662

Svazek periodika

11

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

13961-13979

Kód UT WoS článku

001203466500057

EID výsledku v databázi Scopus

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