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

Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction
type: AcademicArticle
type: Article
abstract: 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.
date: 2023-06
volume: 23
issue: 11
is part of: Sensors
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction (Texto)
has document: Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction (Otro)
subject: Engineering

Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction (Texto)
type: Document
has file: sensors-23-05263.pdf
has part: sensors-23-05263.pdf

Sensors
type: Collection
issn: 14248220

Imran Ashraf
type: Person
given name: Imran
family name: Ashraf

Isabel de la Torre Díez
type: Person
given name: Isabel de la Torre
family name: Díez

Ayesha Altaf
type: Person
given name: Ayesha
family name: Altaf

Daniel Gavilanes Aray
type: Person
given name: Daniel
family name: Gavilanes Aray

Faiza Iqbal
type: Person
given name: Faiza
family name: Iqbal

Zeest Waris
type: Person
given name: Zeest
family name: Waris

Miguel Ángel López Flores
type: Person
given name: Miguel Ángel
family name: López Flores

Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction (Otro)
type: Document
is version of: Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction (Texto)
is volatile version of: Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction (Texto)
is index codes version of: Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

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

Engineering
type: Concept
preferred label: Ingeniería @es