Sensor placement for fault location identification in water networks: A minimum test cover approach

Abstract

This paper focuses on the optimal sensor placement problem for the identification of pipe failure locations in large-scale urban water systems. The problem involves selecting the minimum number of sensors such that every pipe failure can be uniquely localized. This problem can be viewed as a minimum test cover (MTC) problem, which is NP-hard. We consider two approaches to obtain approximate solutions to this problem. In the first approach, we transform the MTC problem to a minimum set cover (MSC) problem and use the greedy algorithm that exploits the submodularity property of the MSC problem to compute the solution to the MTC problem. In the second approach, we develop a new augmented greedy algorithm for solving the MTC problem. This approach does not require the transformation of the MTC to MSC. Our augmented greedy algorithm provides in a significant computational improvement while guaranteeing the same approximation ratio as the first approach. We propose several metrics to evaluate the performance of the sensor placement designs. Finally, we present detailed computational experiments for a number of real water distribution networks.

Synergistic Security for the Industrial Internet of Things: Integrating Redundancy, Diversity, and Hardening

“…An intelligent urban water-supply management system, which consists of IoT gateways connecting the water assets (for instance, water pumps, valves, and tanks) to the cloud service platform for advanced analytics, significantly improves the operational efficiency, safety, and service availability of the overall system [28], [29]. There are ongoing efforts to develop efficient remote monitoring systems for pipeline monitoring (such as PIPENET deployed at Boston Water and Sewer Commission [30], [31]), water quality monitoring [32], [33], [34], leak and burst detection [35], [36], and other applications, for instance [37], [38], [39]. The adoption of new technologies (such as IoT, CPS) and networking devices enhances the monitoring capability, service reliability, and operational efficiency of water distribution systems, but also exposes them to malicious intrusions in the form of cyberand cyber-physical attacks [3], [40], [41].…”

Sensor selection for fault diagnosis in uncertain systems

“…A greedy search strategy for finding a set of sensors fulfilling the diagnosability requirements is proposed in Raghuraj, Bhushan, and Rengaswamy (1999). Another greedy approach is proposed in Perelman, Abbas, Koutsoukos, and Amin (2016) which utilises the submodularity property of the sensor selection problem to significantly reduce computational time. In Rosich, Sarrate, and Nejjari (2009), the sensor placement problem is formulated as a binary integer linear programming problem.…”

“…This property is utilised in, for example, Perelman et al (2016) and Shamaiah, Banerjee, and Vikalo (2010), and if satisfied, a heuristic function would also be easy to compute. However, the amount of distinguishability that is increased for each fault pair D S∪{y l } i,j (θ ), when adding a sensor y l , depends on the previous selected set of sensors S. If S 1 , S 2 ⊆ S \ {y l } are two sets of sensors such that S 1 ⊆ S 2 , then…”

Scheduling Resource-Bounded Monitoring Devices for Event Detection and Isolation in Networks

“…However, complete coverage is a very strict requirement, which severely limits the sets of devices that may be asleep at the same time. In fact, coverage (i.e., ratio of monitored targets to the total number of targets) is a submodular function of the set of active devices in most models (e.g., [4], [5]), which roughly means that attaining complete coverage is disproportionately expensive as compared to achieving reasonably good coverage. Managing energy resources of monitoring devices via their scheduling to achieve an appropriate coverage of targets is a significant issue in networks where extended network lifetime is a critical requirement.…”