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IoT based smart home automation using blockchain and deep learning models.

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Summary
This summary is machine-generated.

This study introduces a deep learning smart home system using Convolutional Neural Networks (CNN) and blockchain for secure device authentication and automated decision-making. The system offers a reliable, inexpensive, and scalable solution for modern home automation challenges.

Keywords:
Cyber attacksCyber threatsHardware securityHome automationInternet of thingsSensors

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Smart homes face challenges in data security, privacy, and device authentication.
  • Existing systems often address only specific issues, lacking integrated automated decision-making.
  • A comprehensive, secure, and reliable home automation solution is essential.

Purpose of the Study:

  • To propose a deep learning-driven smart home system.
  • To integrate Convolutional Neural Networks (CNN) for automated device state classification (ON/OFF).
  • To leverage blockchain technology for secure Internet of Things (IoT) device authentication and identification.

Main Methods:

  • Developed a system using sensors, Raspberry Pi as a server, and a 5V relay circuit.
  • Integrated a CNN for automated decision-making based on device utilization.
  • Implemented blockchain for decentralized and secure IoT device authentication.
  • Created an Android application for system control via an Apache server and HTTP interface.

Main Results:

  • The proposed system effectively classifies device states (ON/OFF) using CNN.
  • Blockchain integration ensures secure and reliable authentication and identification of IoT devices.
  • The system demonstrated efficacy through lab and real-time testing.
  • The utilized hardware and technology are inexpensive, accessible, and scalable.

Conclusions:

  • The deep learning and blockchain-integrated smart home system provides a secure, reliable, and automated solution.
  • The study highlights the need for robust security and privacy models in smart home design.
  • Experimental results validate the system's real-world usability and significance.