TY  - JOUR
AV  - public
JF  - Sensors
VL  - 23
SN  - 1424-8220
UR  - http://doi.org/10.3390/s23115263
ID  - uninimx7455
N2  - 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.
TI  - Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction
A1  - Iqbal, Faiza
A1  - Altaf, Ayesha
A1  - Waris, Zeest
A1  - Gavilanes Aray, Daniel
A1  - López Flores, Miguel Ángel
A1  - Díez, Isabel de la Torre
A1  - Ashraf, Imran
Y1  - 2023///
IS  - 11
KW  - Internet of Things; blockchain; edge computing; privacy; machine learning; net-metering
ER  -