IoT based smart home automation using blockchain and deep learning models

Muhammad Umer; Saima Sadiq; Reemah M Alhebshi; Maha Farouk Sabir; Shtwai Alsubai; Abdullah Al Hejaili; Mashael M Khayyat; Ala' Abdulmajid Eshmawi; Abdullah Mohamed

doi:10.7717/peerj-cs.1332

IoT based smart home automation using blockchain and deep learning models

PeerJ Comput Sci. 2023 May 22:9:e1332. doi: 10.7717/peerj-cs.1332. eCollection 2023.

Authors

Muhammad Umer¹, Saima Sadiq², Reemah M Alhebshi³, Maha Farouk Sabir³, Shtwai Alsubai⁴, Abdullah Al Hejaili⁵, Mashael M Khayyat⁶, Ala' Abdulmajid Eshmawi⁷, Abdullah Mohamed⁸

Affiliations

¹ Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
² Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.
³ Department of Computer Science, Faculty of Computing and Information Technology, King Abdul Aziz University, Jeddah, Saudi Arabia.
⁴ Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.
⁵ Faculty of Computers & Information Technology, Computer Science Department, University of Tabuk, Tabuk, Saudi Arabia.
⁶ Department of Information Systems and Technology, Faculty of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.
⁷ Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudia Arabia.
⁸ University Research Centre, Future University in Egypt, New Cairo, Egypt.

Abstract

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.

Keywords: Cyber attacks; Cyber threats; Hardware security; Home automation; Internet of things; Sensors.

Grants and funding

The authors received no funding for this work.