Cargando…

Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction

Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply–demand disparities, and rising carbon footprints, the need for smart homes for monitoring, managing, and conserving energy has increased. In IoT-based...

Descripción completa

Detalles Bibliográficos
Autores principales: Iqbal, Faiza, Altaf, Ayesha, Waris, Zeest, Aray, Daniel Gavilanes, Flores, Miguel Angel López, Díez, Isabel de la Torre, Ashraf, Imran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256092/
https://www.ncbi.nlm.nih.gov/pubmed/37299993
http://dx.doi.org/10.3390/s23115263
_version_ 1785057030620315648
author Iqbal, Faiza
Altaf, Ayesha
Waris, Zeest
Aray, Daniel Gavilanes
Flores, Miguel Angel López
Díez, Isabel de la Torre
Ashraf, Imran
author_facet Iqbal, Faiza
Altaf, Ayesha
Waris, Zeest
Aray, Daniel Gavilanes
Flores, Miguel Angel López
Díez, Isabel de la Torre
Ashraf, Imran
author_sort Iqbal, Faiza
collection PubMed
description Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply–demand disparities, and rising carbon footprints, the need for smart homes for monitoring, managing, and conserving energy has increased. In IoT-based systems, device data are delivered to the network edge before being stored in the fog or cloud for further transactions. This raises worries about the data’s security, privacy, and veracity. It is vital to monitor who accesses and updates this information to protect IoT end-users linked to IoT devices. Smart meters are installed in smart homes and are susceptible to numerous cyber attacks. Access to IoT devices and related data must be secured to prevent misuse and protect IoT users’ privacy. The purpose of this research was to design a blockchain-based edge computing method for securing the smart home system, in conjunction with machine learning techniques, in order to construct a secure smart home system with energy usage prediction and user profiling. The research proposes a blockchain-based smart home system that can continuously monitor IoT-enabled smart home appliances such as smart microwaves, dishwashers, furnaces, and refrigerators, among others. An approach based on machine learning was utilized to train the auto-regressive integrated moving average (ARIMA) model for energy usage prediction, which is provided in the user’s wallet, to estimate energy consumption and maintain user profiles. The model was tested using the moving average statistical model, the ARIMA model, and the deep-learning-based long short-term memory (LSTM) model on a dataset of smart-home-based energy usage under changing weather conditions. The findings of the analysis reveal that the LSTM model accurately forecasts the energy usage of smart homes.
format Online
Article
Text
id pubmed-10256092
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-102560922023-06-10 Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction Iqbal, Faiza Altaf, Ayesha Waris, Zeest Aray, Daniel Gavilanes Flores, Miguel Angel López Díez, Isabel de la Torre Ashraf, Imran Sensors (Basel) Article Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply–demand disparities, and rising carbon footprints, the need for smart homes for monitoring, managing, and conserving energy has increased. In IoT-based systems, device data are delivered to the network edge before being stored in the fog or cloud for further transactions. This raises worries about the data’s security, privacy, and veracity. It is vital to monitor who accesses and updates this information to protect IoT end-users linked to IoT devices. Smart meters are installed in smart homes and are susceptible to numerous cyber attacks. Access to IoT devices and related data must be secured to prevent misuse and protect IoT users’ privacy. The purpose of this research was to design a blockchain-based edge computing method for securing the smart home system, in conjunction with machine learning techniques, in order to construct a secure smart home system with energy usage prediction and user profiling. The research proposes a blockchain-based smart home system that can continuously monitor IoT-enabled smart home appliances such as smart microwaves, dishwashers, furnaces, and refrigerators, among others. An approach based on machine learning was utilized to train the auto-regressive integrated moving average (ARIMA) model for energy usage prediction, which is provided in the user’s wallet, to estimate energy consumption and maintain user profiles. The model was tested using the moving average statistical model, the ARIMA model, and the deep-learning-based long short-term memory (LSTM) model on a dataset of smart-home-based energy usage under changing weather conditions. The findings of the analysis reveal that the LSTM model accurately forecasts the energy usage of smart homes. MDPI 2023-06-01 /pmc/articles/PMC10256092/ /pubmed/37299993 http://dx.doi.org/10.3390/s23115263 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Iqbal, Faiza
Altaf, Ayesha
Waris, Zeest
Aray, Daniel Gavilanes
Flores, Miguel Angel López
Díez, Isabel de la Torre
Ashraf, Imran
Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction
title Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction
title_full Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction
title_fullStr Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction
title_full_unstemmed Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction
title_short Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction
title_sort blockchain-modeled edge-computing-based smart home monitoring system with energy usage prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256092/
https://www.ncbi.nlm.nih.gov/pubmed/37299993
http://dx.doi.org/10.3390/s23115263
work_keys_str_mv AT iqbalfaiza blockchainmodelededgecomputingbasedsmarthomemonitoringsystemwithenergyusageprediction
AT altafayesha blockchainmodelededgecomputingbasedsmarthomemonitoringsystemwithenergyusageprediction
AT wariszeest blockchainmodelededgecomputingbasedsmarthomemonitoringsystemwithenergyusageprediction
AT araydanielgavilanes blockchainmodelededgecomputingbasedsmarthomemonitoringsystemwithenergyusageprediction
AT floresmiguelangellopez blockchainmodelededgecomputingbasedsmarthomemonitoringsystemwithenergyusageprediction
AT diezisabeldelatorre blockchainmodelededgecomputingbasedsmarthomemonitoringsystemwithenergyusageprediction
AT ashrafimran blockchainmodelededgecomputingbasedsmarthomemonitoringsystemwithenergyusageprediction