Design of Resilient Sensor Networks Balancing Resilience and Efficiency

Sergey N. Vecherin; Kiril D. Ratmanski; Luke Hogewood; Igor Linkov

doi:10.1007/s13753-024-00546-w

Volume 15 Issue 1

Feb. 2024

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Sergey N. Vecherin, Kiril D. Ratmanski, Luke Hogewood, Igor Linkov. Design of Resilient Sensor Networks Balancing Resilience and Efficiency[J]. International Journal of Disaster Risk Science, 2024, 15(1): 107-115. doi: 10.1007/s13753-024-00546-w

Citation:

Sergey N. Vecherin, Kiril D. Ratmanski, Luke Hogewood, Igor Linkov. Design of Resilient Sensor Networks Balancing Resilience and Efficiency[J]. International Journal of Disaster Risk Science, 2024, 15(1): 107-115. doi: 10.1007/s13753-024-00546-w

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Design of Resilient Sensor Networks Balancing Resilience and Efficiency

doi: 10.1007/s13753-024-00546-w

1. Cold Regions Research and Engineering Laboratory, Engineering Research and Development Center, Hanover, NH, 03755, USA;
2. Environmental Laboratory, Engineering Research and Development Center, Vicksburg, MS, 39180, USA

Accepted Date: 2024-02-01

Available Online: 2024-03-14

Publish Date: 2024-03-01

Abstract

Abstract

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.
- Binary linear programming,
- Optimal sensor placement,
- Redundant networks,
- Resilience and efficiency,
- Resilient sensor networks

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References(31)

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