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Iqbal F, Altaf A, Waris Z, Aray DG, Flores MAL, Díez IDLT, Ashraf I. Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction. Sensors (Basel) 2023; 23:s23115263. [PMID: 37299993 DOI: 10.3390/s23115263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 05/25/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
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.
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Affiliation(s)
- Faiza Iqbal
- Department of Computer Science, University of Engineering & Technology (UET), Lahore 54890, Pakistan
| | - Ayesha Altaf
- Department of Computer Science, University of Engineering & Technology (UET), Lahore 54890, Pakistan
| | - Zeest Waris
- Department of Computer Science, University of Engineering & Technology (UET), Lahore 54890, Pakistan
| | - Daniel Gavilanes Aray
- Research Group on Foods, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Research Group on Foods, Universidad Internacional Iberoamericana Arecibo, Arecibo, PR 00613, USA
- Universidade Internacional do Cuanza, Cuito EN250, Bié, Angola
| | - Miguel Angel López Flores
- Research Group on Foods, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Instituto Politécnico Nacional, UPIICSA, Ciudad de México 04510, Mexico
| | - Isabel de la Torre Díez
- Department of Signal Theory, Communications and Telematics Engineering, Unviersity of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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