论文标题

可互操作机系统中的定时属性的规范和验证

Specification and Verification of Timing Properties in Interoperable Medical Systems

论文作者

Zarneshan, Mahsa, Ghassemi, Fatemeh, Khamespanah, Ehsan, Sirjani, Marjan, Hatcliff, John

论文摘要

为了支持各种设备/应用程序的动态组成在护理点上的医疗系统中,已经提出了一组来描述设备通信需求的通信模式。为了满足时序要求,每个模式都会将通用的时序属性打破到可以由组件本地执行的较细的定时属性中。基础通信基板的常见时序要求来自这些本地属性。供应商在开发时间确保了设备的局部特性。尽管组织采购以其本地属性和中间件兼容的设备,但可能无法根据需要运行。组织网络的延迟与设备的本地属性进行交互。为了验证组件和网络的定时属性之间的相互作用,我们在定时rebeca中正式指定了此类系统。我们使用模型检查来验证网络和设备模型方面通信基板的派生时间要求。我们提供一组模板作为指南,以根据形式的模式指定医疗系统。使用多个设备的复合医疗系统会遭受状态空间爆炸。我们根据模式的静态特性扩展了定时rebeca的还原技术。我们证明我们的减少是合理的,并通过对几种模式实例制成的两个临床场景进行建模,从而证明了我们方法在减少状态空间中的适用性。

To support the dynamic composition of various devices/apps into a medical system at point-of-care, a set of communication patterns to describe the communication needs of devices has been proposed. To address timing requirements, each pattern breaks common timing properties into finer ones that can be enforced locally by the components. Common timing requirements for the underlying communication substrate are derived from these local properties. The local properties of devices are assured by the vendors at the development time. Although organizations procure devices that are compatible in terms of their local properties and middleware, they may not operate as desired. The latency of the organization network interacts with the local properties of devices. To validate the interaction among the timing properties of components and the network, we formally specify such systems in Timed Rebeca. We use model checking to verify the derived timing requirements of the communication substrate in terms of the network and device models. We provide a set of templates as a guideline to specify medical systems in terms of the formal model of patterns. A composite medical system using several devices is subject to state-space explosion. We extend the reduction technique of Timed Rebeca based on the static properties of patterns. We prove that our reduction is sound and show the applicability of our approach in reducing the state space by modeling two clinical scenarios made of several instances of patterns.

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