{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T18:32:27Z","timestamp":1775932347569,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European University of Atlantics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply\u2013demand 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\u2019s 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\u2019 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s23115263","type":"journal-article","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T02:16:48Z","timestamp":1685672208000},"page":"5263","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5536-8764","authenticated-orcid":false,"given":"Faiza","family":"Iqbal","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Engineering & Technology (UET), Lahore 54890, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6446-4945","authenticated-orcid":false,"given":"Ayesha","family":"Altaf","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Engineering & Technology (UET), Lahore 54890, Pakistan"}]},{"given":"Zeest","family":"Waris","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Engineering & Technology (UET), Lahore 54890, Pakistan"}]},{"given":"Daniel Gavilanes","family":"Aray","sequence":"additional","affiliation":[{"name":"Research Group on Foods, Universidad Europea del Atl\u00e1ntico, Isabel Torres 21, 39011 Santander, Spain"},{"name":"Research Group on Foods, Universidad Internacional Iberoamericana Arecibo, Arecibo, PR 00613, USA"},{"name":"Universidade Internacional do Cuanza, Cuito EN250, Bi\u00e9, Angola"}]},{"given":"Miguel Angel L\u00f3pez","family":"Flores","sequence":"additional","affiliation":[{"name":"Research Group on Foods, Universidad Europea del Atl\u00e1ntico, Isabel Torres 21, 39011 Santander, Spain"},{"name":"Universidad Internacional Iberoamericana, Campeche 24560, Mexico"},{"name":"Instituto Polit\u00e9cnico Nacional, UPIICSA, Ciudad de M\u00e9xico 04510, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3134-7720","authenticated-orcid":false,"given":"Isabel de la Torre","family":"D\u00edez","sequence":"additional","affiliation":[{"name":"Department of Signal Theory, Communications and Telematics Engineering, Unviersity of Valladolid, Paseo de Bel\u00e9n, 15, 47011 Valladolid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8271-6496","authenticated-orcid":false,"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106425","DOI":"10.1016\/j.cie.2020.106425","article-title":"Smart home energy management using hybrid robust-stochastic optimization","volume":"143","author":"Nojavan","year":"2020","journal-title":"Comput. 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