基于区块链建模的边缘计算的智能家居监控系统,具有能源使用预测
Faiza Iqbal1, Ayesha Altaf1, Zeest Waris1
1Department of Computer Science, University of Engineering & Technology (UET), Lahore 54890, Pakistan.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了一种使用区块链和机器学习进行能源管理的安全智能家居系统. 长短期内存 (LSTM) 模型准确预测能源使用情况,增强智能家居安全性和用户配置.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 能源系统 能源系统
背景情况:
- 能源成本和碳足迹的增加需要智能家居能源管理.
- 物联网 (IoT) 系统面临着安全,隐私和数据真实性的挑战.
- 家庭中的智能电表容易受到网络攻击,需要安全的数据访问.
研究的目的:
- 设计基于区块链的边缘计算方法,以确保智能家居系统的安全.
- 整合机器学习用于能源使用预测和用户配置文件.
- 为了增强支持物联网的智能家居设备的安全性和隐私性.
主要方法:
- 开发了一个基于区块链的系统,用于对物联网智能家电设备的持续监控.
- 利用机器学习来训练自动回归集成移动平均数 (ARIMA) 模型进行能源预测.
- 在不同的天气条件下对智能家居能源使用数据进行了LSTM,ARIMA和移动平均模型的评估.
主要成果:
- 长短期内存 (LSTM) 模型在预测智能家居能源消耗方面表现出高准确性.
- 拟议的系统有效监控物联网设备并维护用户配置文件.
- 区块链集成增强了智能家居能源管理系统的安全性.
结论:
- 区块链和机器学习为安全高效的智能家居能源管理提供了强大的解决方案.
- 使用LSTM精确的能源使用预测可以改善用户配置和节能.
- 开发的系统解决了基于物联网的智能家居中关键的安全和隐私问题.
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