In recent years, the notion of resilience has been developed and applied in many technical areas, becoming exceptionally pertinent to disaster risk science. During a disaster situation, accurate sensing information is the key to efficient recovery efforts. In general, resilience aims to minimize the impact of disruptions to systems through the fast recovery of critical functionality, but resilient design may require redundancy and could increase costs. In this article, we describe a method based on binary linear programming for sensor network design balancing efficiency with resilience. The application of the developed framework is demonstrated for the case of interior building surveillance utilizing infrared sensors in both two- and three-dimensional spaces. The method provides optimal sensor placement, taking into account critical functionality and a desired level of resilience and considering sensor type and availability. The problem formulation, resilience requirements, and application of the optimization algorithm are described in detail. Analysis of sensor locations with and without resilience requirements shows that resilient configuration requires redundancy in number of sensors and their intelligent placement. Both tasks are successfully solved by the described method, which can be applied to strengthen the resilience of sensor networks by design. The proposed methodology is suitable for large-scale optimization problems with many sensors and extensive coverage areas.

近年来，复原力的概念已在许多技术领域得到发展和应用，与灾害风险科学变得异常相关。在灾难情况下，准确的传感信息是高效恢复工作的关键。一般来说，弹性旨在通过关键功能的快速恢复来最大限度地减少系统中断的影响，但弹性设计可能需要冗余并可能增加成本。在本文中，我们描述了一种基于二元线性规划的方法，用于平衡效率与弹性的传感器网络设计。所开发的框架的应用在二维和三维空间中利用红外传感器的内部建筑监控案例中得到了证明。该方法提供了最佳的传感器放置，考虑了关键功能和所需的弹性水平并考虑了传感器类型和可用性。详细描述了优化算法的问题表述、弹性要求和应用。对具有和不具有弹性要求的传感器位置的分析表明，弹性配置需要传感器数量及其智能放置的冗余。上述方法成功解决了这两个任务，可用于通过设计增强传感器网络的弹性。所提出的方法适用于具有许多传感器和广泛覆盖区域的大规模优化问题。