区块链模拟的小群优化lyapunov智能合约深度加强的安全任务在智能家居下载
Preethi1, Mohammed Mujeer Ulla2, Sapna R1
1Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.
MethodsX
|April 21, 2025
概括
本研究介绍了一种新的智能家居方法,可以安全地卸载计算任务,通过最小化延迟和提高吞吐量来提高服务质量 (QoS). 该系统使用一种新的方法来实现可靠的访问控制,保护物联网 (IoT) 设备.
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:
- 智能家居技术在计算任务卸载,数据安全,隐私和设备身份验证方面面临着挑战.
- 现有的解决方案往往只解决特定问题,突出显示了需要综合系统来提高服务质量 (QoS) 和安全性.
研究的目的:
- 为智能家居自动化提出一种新的QoS改进方法,该方法解决了计算任务卸载,安全性和可靠性.
- 加强决策,以改善延迟和吞吐量来卸载计算任务.
主要方法:
- 开发了一种粒子集群优化的利亚普诺夫模型,以最大限度地减少延迟并最大限度地提高计算任务卸载的吞吐量.
- 提出了一个时间差距深度增强安全卸载模型,用于可靠的访问控制和防止非法卸载.
- 应用时间差函数用于计算管理和智能合约在任务卸载中的准确性.
主要成果:
- 拟议的区块链建模的Swarm Optimized Lyapunov智能合约深度强化任务卸载 (BSOLSC-DRTO) 方法通过减少延迟和增加吞吐量,显著改善了QoS.
- 实验和模拟结果表明,BSOLSC-DRTO方法在智能家居自动化中优于现有的方法.
- 该系统通过可靠的访问控制提供增强的安全性,保护物联网 (IoT) 设备.
结论:
- BSOLSC-DRTO方法为智能家居自动化提供了全面的解决方案,有效地管理计算任务卸载,同时确保高质量服务和强大的安全性.
- 集群优化,利亚普诺夫模型和深度强化学习的整合为未来的智能家居系统提供了可靠的框架.
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