基于物联网的智能家居自动化使用区块链和深度学习模型
Muhammad Umer1, Saima Sadiq2, Reemah M Alhebshi3
1Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
PeerJ. Computer science
|June 22, 2023
概括
本研究介绍了使用卷积神经网络 (CNN) 和区块链进行安全设备身份验证和自动决策的深度学习智能家居系统. 该系统为现代家庭自动化挑战提供了可靠,廉价和可扩展的解决方案.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 智能家居在数据安全,隐私和设备身份验证方面面临着挑战.
- 现有的系统往往只解决特定问题,缺乏集成的自动决策.
- 一个全面的,安全的,可靠的家庭自动化解决方案是必不可少的.
研究的目的:
- 提出一个深度学习驱动的智能家居系统.
- 集成卷积神经网络 (CNN) 进行自动设备状态分类 (ON/OFF).
- 为安全的物联网 (IoT) 设备身份验证和识别利用区块链技术.
主要方法:
- 开发了一个使用传感器,Raspberry Pi作为服务器和5V中继电路的系统.
- 集成了一个CNN,用于基于设备使用情况的自动决策.
- 实现区块链以实现分散和安全的物联网设备身份验证.
- 通过Apache服务器和HTTP接口创建了一个用于系统控制的Android应用程序.
主要成果:
- 拟议的系统有效地使用CNN对设备状态 (ON/OFF) 进行分类.
- 区块链集成确保了物联网设备的安全可靠的身份验证和识别.
- 该系统通过实验室和实时测试证明了它的有效性.
- 使用的硬件和技术是廉价的,可访问的和可扩展的.
结论:
- 深度学习和区块链集成的智能家居系统提供了一个安全,可靠和自动化的解决方案.
- 该研究强调了在智能家居设计中需要强大的安全和隐私模型.
- 实验结果验证了系统的现实世界可用性和意义.
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