一个过程代数方法来预测和控制智能城市智能物联网系统中的不确定性,基于允许的概率等价值
Junsup Song1, Dimitris Karagiannis2, Moonkun Lee1
1Department of Computer Science and Engineering, Jeonbuk National University, Jeonju 561-756, Republic of Korea.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
本研究引入了一种新的过程代数,dTP-Calculus,用于管理智能城市物联网系统中的不确定性. 它允许使用离散概率逐步执行要求,提高系统可靠性和风险控制.
科学领域:
- 计算机科学 计算机科学
- 正式方法 正式方法
- 网络物理系统 网络物理系统
背景情况:
- 过程代数适用于智能城市物联网系统的建模,但缺乏用于测量或控制要求满足性不确定性的机制.
- 之前的工作引入了dTP-Calculus,具有静态和动态概率,但需要对未满足的概率要求做出强有力的假设.
- 处理概率要求中的不确定性需要在正式方法中进行测量和控制的实际机制.
研究的目的:
- 消除以前研究中关于连续概率域的强有力的假设.
- 引入一种新的方法,通过使用离散的概率级别逐步强制执行概率要求.
- 开发和演示用于验证和控制智能物联网系统中不确定性的实用工具.
主要方法:
- 利用基于允许的过程和系统等价值的离散概率级别.
- 通过允许的流程增强 (流程级) 和允许的系统增强 (系统级) 实现增量执行.
- 在ADOxx元建模平台上开发SAVE工具套件,用于演示和分析.
主要成果:
- 新方法成功地消除了对连续概率领域和强有力的假设的需求.
- 未满足的概率要求在离散的步骤中逐步强制执行,从而导致性能更好的概率.
- SAVE工具套件有效地展示了该方法在指定,分析和验证智能物联网系统中的适用性,使用智能EMS示例.
结论:
- 增强了离散概率和SAVE工具的dTP-Calculus提供了一个强大的方法来管理智能城市物联网系统中的不确定性.
- 这种方法提供了预测和控制来自非确定性行为的风险的实际机制.
- dTP-Calculus和SAVE的结合代表了智能城市应用的正式方法的重大进步.
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