实时传感器网络中事件偏差估计的仿真模型

L. Donatiello, G. Marfia, Armir Bujari, C. Palazzi
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引用次数: 3

摘要

在本文中,我们提出事件goodput,即系统可以成功管理的事件的比例,作为描述电池供电的实时传感器网络性能的相关度量。与响应、完成、最大延迟时间等其他性能指标不同,这些指标都代表了描述实时系统行为的基本但不同的价值指标,事件good - put提供了与实时应用程序可能满意地管理(或不满意地管理)的事件之间的直接关系。我们将展示这样的度量很好地服务于描述电池供电、随机事件驱动网络的性能的目的,例如用于监视和入侵检测应用的传感器网络,在时间关键场景下运行。本质上,这种实时系统可以根据它们在感兴趣的时间间隔内成功/不成功检测和报告的事件的比例来评估。这种度量的重要性在这里被证明,提供了一个模拟模型和结果,其中事件good - put度量的使用与传统上用于实时传感器网络评估的那些度量一起被讨论。
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A simulation model for event goodput estimation in real-time sensor networks
In this paper we propose event goodput, i.e., the fraction of events which may be successfully managed by a system, as a relevant metric to describe the performance of battery powered real-time sensor networks. Unlike other performance metrics as response, completion, maximum lateness times, all representing fundamental, but different, figures of merit for the description of the behavior of real-time systems, event goodput provides an immediate and a direct relation with the events which may be satisfactorily managed (or not) by a real-time application. We will show such metric well serves the purpose of describing the performance of battery powered, random event-driven networks, such as sensor networks deployed for surveillance and intrusion detection applications, operating in time critical scenarios. In essence, such real-time systems may be assessed in terms of the fraction of events which they successfully/unsuccessfully detect and report within a time interval of interest. The importance of such metric is here demonstrated providing a simulation model and results where the use of the event goodput metric is discussed in conjunction with those metrics which are traditionally utilized for the assessment of a real-time sensor networks.
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