Cargando…
IoT based smart home automation using blockchain and deep learning models
For the past few years, the concept of the smart house has gained popularity. The major challenges concerning a smart home include data security, privacy issues, authentication, secure identification, and automated decision-making of Internet of Things (IoT) devices. Currently, existing home automat...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280418/ https://www.ncbi.nlm.nih.gov/pubmed/37346725 http://dx.doi.org/10.7717/peerj-cs.1332 |
_version_ | 1785060790049439744 |
---|---|
author | Umer, Muhammad Sadiq, Saima Alhebshi, Reemah M. Sabir, Maha Farouk Alsubai, Shtwai Al Hejaili, Abdullah Khayyat, Mashael M. Eshmawi, Ala’ Abdulmajid Mohamed, Abdullah |
author_facet | Umer, Muhammad Sadiq, Saima Alhebshi, Reemah M. Sabir, Maha Farouk Alsubai, Shtwai Al Hejaili, Abdullah Khayyat, Mashael M. Eshmawi, Ala’ Abdulmajid Mohamed, Abdullah |
author_sort | Umer, Muhammad |
collection | PubMed |
description | For the past few years, the concept of the smart house has gained popularity. The major challenges concerning a smart home include data security, privacy issues, authentication, secure identification, and automated decision-making of Internet of Things (IoT) devices. Currently, existing home automation systems address either of these challenges, however, home automation that also involves automated decision-making systems and systematic features apart from being reliable and safe is an absolute necessity. The current study proposes a deep learning-driven smart home system that integrates a Convolutional neural network (CNN) for automated decision-making such as classifying the device as “ON” and “OFF” based on its utilization at home. Additionally, to provide a decentralized, secure, and reliable mechanism to assure the authentication and identification of the IoT devices we integrated the emerging blockchain technology into this study. The proposed system is fundamentally comprised of a variety of sensors, a 5 V relay circuit, and Raspberry Pi which operates as a server and maintains the database of each device being used. Moreover, an android application is developed which communicates with the Raspberry Pi interface using the Apache server and HTTP web interface. The practicality of the proposed system for home automation is tested and evaluated in the lab and in real-time to ensure its efficacy. The current study also assures that the technology and hardware utilized in the proposed smart house system are inexpensive, widely available, and scalable. Furthermore, the need for a more comprehensive security and privacy model to be incorporated into the design phase of smart homes is highlighted by a discussion of the risks analysis’ implications including cyber threats, hardware security, and cyber attacks. The experimental results emphasize the significance of the proposed system and validate its usability in the real world. |
format | Online Article Text |
id | pubmed-10280418 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102804182023-06-21 IoT based smart home automation using blockchain and deep learning models Umer, Muhammad Sadiq, Saima Alhebshi, Reemah M. Sabir, Maha Farouk Alsubai, Shtwai Al Hejaili, Abdullah Khayyat, Mashael M. Eshmawi, Ala’ Abdulmajid Mohamed, Abdullah PeerJ Comput Sci Artificial Intelligence For the past few years, the concept of the smart house has gained popularity. The major challenges concerning a smart home include data security, privacy issues, authentication, secure identification, and automated decision-making of Internet of Things (IoT) devices. Currently, existing home automation systems address either of these challenges, however, home automation that also involves automated decision-making systems and systematic features apart from being reliable and safe is an absolute necessity. The current study proposes a deep learning-driven smart home system that integrates a Convolutional neural network (CNN) for automated decision-making such as classifying the device as “ON” and “OFF” based on its utilization at home. Additionally, to provide a decentralized, secure, and reliable mechanism to assure the authentication and identification of the IoT devices we integrated the emerging blockchain technology into this study. The proposed system is fundamentally comprised of a variety of sensors, a 5 V relay circuit, and Raspberry Pi which operates as a server and maintains the database of each device being used. Moreover, an android application is developed which communicates with the Raspberry Pi interface using the Apache server and HTTP web interface. The practicality of the proposed system for home automation is tested and evaluated in the lab and in real-time to ensure its efficacy. The current study also assures that the technology and hardware utilized in the proposed smart house system are inexpensive, widely available, and scalable. Furthermore, the need for a more comprehensive security and privacy model to be incorporated into the design phase of smart homes is highlighted by a discussion of the risks analysis’ implications including cyber threats, hardware security, and cyber attacks. The experimental results emphasize the significance of the proposed system and validate its usability in the real world. PeerJ Inc. 2023-05-22 /pmc/articles/PMC10280418/ /pubmed/37346725 http://dx.doi.org/10.7717/peerj-cs.1332 Text en © 2023 Umer et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Umer, Muhammad Sadiq, Saima Alhebshi, Reemah M. Sabir, Maha Farouk Alsubai, Shtwai Al Hejaili, Abdullah Khayyat, Mashael M. Eshmawi, Ala’ Abdulmajid Mohamed, Abdullah IoT based smart home automation using blockchain and deep learning models |
title | IoT based smart home automation using blockchain and deep learning models |
title_full | IoT based smart home automation using blockchain and deep learning models |
title_fullStr | IoT based smart home automation using blockchain and deep learning models |
title_full_unstemmed | IoT based smart home automation using blockchain and deep learning models |
title_short | IoT based smart home automation using blockchain and deep learning models |
title_sort | iot based smart home automation using blockchain and deep learning models |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280418/ https://www.ncbi.nlm.nih.gov/pubmed/37346725 http://dx.doi.org/10.7717/peerj-cs.1332 |
work_keys_str_mv | AT umermuhammad iotbasedsmarthomeautomationusingblockchainanddeeplearningmodels AT sadiqsaima iotbasedsmarthomeautomationusingblockchainanddeeplearningmodels AT alhebshireemahm iotbasedsmarthomeautomationusingblockchainanddeeplearningmodels AT sabirmahafarouk iotbasedsmarthomeautomationusingblockchainanddeeplearningmodels AT alsubaishtwai iotbasedsmarthomeautomationusingblockchainanddeeplearningmodels AT alhejailiabdullah iotbasedsmarthomeautomationusingblockchainanddeeplearningmodels AT khayyatmashaelm iotbasedsmarthomeautomationusingblockchainanddeeplearningmodels AT eshmawialaabdulmajid iotbasedsmarthomeautomationusingblockchainanddeeplearningmodels AT mohamedabdullah iotbasedsmarthomeautomationusingblockchainanddeeplearningmodels |