Design of Resilient Sensor Networks Balancing Resilience and Efficiency

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...

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Bibliographic Details
Published in:International journal of disaster risk science 2024-02, Vol.15 (1), p.107-115
Main Authors: Vecherin, Sergey N., Ratmanski, Kiril D., Hogewood, Luke, Linkov, Igor
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Balancing
Binary linear programming
Climate Change
Design
Disaster risk
Disasters
Earth and Environmental Science
Earth Sciences
Environment
Infrared detectors
Landscape/Regional and Urban Planning
Linear programming
Mathematical optimization
Natural Hazards
Network design
Optimal sensor placement
Optimization
Placement
Recovery
Redundancy
Redundant networks
Resilience
Resilience and efficiency
Resilient sensor networks
Sensors
Sustainable Development
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Summary: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.
ISSN:2095-0055
2192-6395
DOI:10.1007/s13753-024-00546-w