采用高精度室内定位系统的高精度室内定位系统采用了基于Galois现场的加密技术和混合深度学习
Mohammad Mazyad Hazzazi1, Prashant Kumar Shukla2, Piyush Kumar Shukla3
1Department of Mathematics, College of Science, King Khalid University, 61413, Abha, Saudi Arabia.
Scientific reports
|April 29, 2025
概括
本研究介绍了一种使用深度学习和高级安全功能的新型室内定位系统 (IPS). 该系统实现了高精度和弹性,可在复杂环境中实现可靠的室内位置跟踪.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络安全 网络安全
背景情况:
- 全球定位系统 (GPS) 在室内环境中无效.
- 传统的室内定位系统 (IPS) 由于环境复杂性和信号噪声而面临准确性,弹性和安全性的挑战.
- 智能制造和物流需要强大的室内定位解决方案.
研究的目的:
- 开发一个准确,弹性和安全的室内定位系统.
- 利用深度学习和先进的加密技术来提高IPS性能.
- 在复杂的室内环境中解决传统IPS的局限性.
主要方法:
- 使用了两阶段的方法:线下数据收集和在线位置分类.
- 采用信号处理来降低噪音和增强数据,然后进行DBSCAN集群.
- 开发了深度时空注意力网络 (Deep-STAN),这是一个混合模型,将CNN,ViT和LSTM与注意力机制相结合.
- 集成圆曲线加密 (ECC) 用于数据加密,QR码用于位置标记,以及区块链用于不可变的数据存储.
主要成果:
- 实现了高性能指标:准确度为0.9937,精度为0.987,灵敏度为0.9898,特异性为0.9878.
- 即使使用80%的数据,也证明了持续的准确性 (0.9804),表明模型的稳定性.
- 集成的ECC,QR码和区块链显著提高了数据完整性,机密性和系统弹性.
结论:
- 拟议的基于深度学习的IPS带有先进的安全功能,为室内定位提供了稳定灵活的解决方案.
- 与传统方法相比,混合型深层STAN模型和加密增强提供了更高的准确性和安全性.
- 该系统非常适合用于需要低延迟,安全和可靠的室内定位服务的现实应用.
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