Value-of-information in spatio-temporal systems: Sensor placement and scheduling
Value-of-information in spatio-temporal systems: Sensor placement and scheduling
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DOI：
10.1016/j.ress.2017.11.019
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发表时间：
2018-04-01
影响因子：
8.1
通讯作者：
Pozzi, M.
中科院分区：
文献类型：
--
作者：
Malings, C.;Pozzi, M.
基础设施的管理涉及到考虑系统域上空间变化和系统变化时时间变化的因素。有效的系统管理应该以模型为指导，这些模型考虑了这些影响因素的不确定性以及为减少这种不确定性而收集的信息。在本文中，我们解决了空间分布的动态基础设施系统的最佳信息收集问题。基于先验信息，可以设计监测方案，包括传感器的布置和调度。在管理过程中，随着更多信息的出现，可以调整这一方案。最优性可以根据信息的价值（VoI）来定义，它为量化数据收集工作的好处提供了一个合理的度量，以支持系统管理决策。然而，在空间和时间上广泛的系统中计算这个度量可能会对其实施造成实际障碍。我们描述了这种复杂性，并调查了一个特殊的情况下，系统拓扑结构，被称为一个时间上可分解的系统与不受控制的演变，在其中的复杂性评估VoI增长在一个可管理的速度相对于系统管理的持续时间。我们展示了这样一个系统的一个例子中的VoI的评估和优化。(C)2017爱思唯尔有限公司版权所有
The management of infrastructure involves accounting for factors which vary in space over the system domain and in time as the system changes. Effective system management should be guided by models which account for uncertainty in these influencing factors as well as for information gathered to reduce this uncertainty. In this paper, we address the problem of optimal information collection for spatially distributed dynamic infrastructure systems. Based on prior information, a monitoring scheme can be designed, including placement and scheduling of sensors. This scheme can be adapted during the management process, as more information becomes available. Optimality can be defined in terms of the value of information (VoI), which provides a rational metric for quantifying the benefits of data gathering efforts to support system management decision-making. However, the computation of this metric in spatially and temporally extensive systems can present a practical impediment to its implementation. We describe this complexity, and investigate a special case of system topology, termed as a temporally decomposable system with uncontrolled evolution, in which the complexity of assessing VoI grows at a manageable rate with respect to the system management time duration. We demonstrate the evaluation and optimization of the VoI in an example of such a system. (C) 2017 Elsevier Ltd. All rights reserved.