Robust sensor placement for pipeline monitoring: Mixed integer and greedy optimization

Abstract

Intelligent water systems – aided by sensing technologies – have been identified as an important mechanism towards ensuring the resilience of urban systems. In this work, we study the problem of sensor placement that is robust to intermittent failures of sensors, i.e. sensor interruptions. We propose robust mixed integer optimization (RMIO) and robust greedy approximation (RGA) solution approaches. The underlying idea of both approaches is to promote solutions that achieve multiple detectability of events, such that these events remain detectable even when some sensors are interrupted. Additionally, we apply a previously proposed greedy approximation approach for solving the robust submodular function optimization (RSFO) problem. We compare scalability of these approaches and the quality of the solutions using a set of real water-networks. Our results demonstrate that considering sensor interruptions in the design stage improves sensor network performance. Importantly, we find that although the detection performances of RMIO and RGA approaches are comparable, RMIO generally has better performance than RGA, and is scalable to large-scale networks. Furthermore, the results demonstrate that RMIO and RGA approaches tend to outperform the RSFO approach.