Water Contaminants Detection Using Sensor Placement Approach in Smart Water Networks

Incidents of water pollution or contamination have occurred repeatedly in recent years, causing significant disasters and negative health impacts. Water quality sensors need to be installed in the water distribution system (WDS) to allow real-time water contamination detection to reduce the risk of water contamination. Deploying sensors in WDS is essential to monitor and detect any pollution incident at the appropriate time. However, it is impossible to place sensors on all nodes of the network due to the relatively large structure of WDS and the high cost of water quality sensors. For that, it is necessary to reduce the cost of deployment and guarantee the reliability of the sensing, such as detection time and coverage of the whole water network. In this paper, a dynamic approach of sensor placement that uses an Evolutionary Algorithm (EA) is proposed and implemented. The proposed method generates a multiple set of water contamination scenarios in several locations selected randomly in the WDS. Each contamination scenario spreads in the water networks for several hours, and then the proposed approach simulates the various effect of each contamination scenario on the water networks. On the other hand, the multiple objectives of the sensor placement optimization problem, which aim to find the optimal locations of the deployed sensors, have been formulated. The sensor placement optimization solver, which uses the EA, is operated to find the optimal sensor placements. The effectiveness of the proposed method has been evaluated using two different case studies on the example of water networks: Battle of the Water Sensor Network (BWSN) and another real case study from Madrid (Spain). The results have shown the capability of the proposed method to adapt the location of the sensors based on the numbers and the locations of contaminant sources. Moreover, the results also have demonstrated the ability of the proposed approach for maximising the coverage of deployed sensors and reducing the time to detect all the water contaminants using a few numbers of water quality sensor

Optimizing Sensing: From Water to the Web

Where should we place sensors to quickly detect contamination in drinking water distribution networks? Which blogs should we read to learn about the biggest stories on the Web? Such problems are typically NP-hard in theory and extremely challenging in practice. The authors present algorithms that exploit submodularity to efficiently find provably near-optimal solutions to large, complex real-world sensing problems

Contamination source inference in water distribution networks

We study the inference of the origin and the pattern of contamination in water distribution networks. We assume a simplified model for the dyanmics of the contamination spread inside a water distribution network, and assume that at some random location a sensor detects the presence of contaminants. We transform the source location problem into an optimization problem by considering discrete times and a binary contaminated/not contaminated state for the nodes of the network. The resulting problem is solved by Mixed Integer Linear Programming. We test our results on random networks as well as in the Modena city network

ADVANCED MODELING AND EFFICIENT OPTIMIZATION METHODS FOR REAL-TIME RESPONSE IN WATER NETWORKS

In response to a contamination incident in water distribution networks, effective mitigation procedures must be planned. Disinfectant booster stations can be used to neutralize a variety of contaminant and protect the public. In this thesis, two methods are proposed for the optimal placement of booster stations. Since the contaminant species is unknown a priori, these two methods differ in how they model the unknown reaction between the contaminant and the disinfectant. Both methods employ Mixed-Integer Linear Programming to minimize the expected impact over a large set of potential contamination scenarios that consider the uncertainty in the location and time of the incident. To make the optimal booster placement problem tractable for realistic large-scale networks, we exploit the symmetry in the problem structure to drastically reduce the problem size. The results highlight the effectiveness of booster stations in reducing the overall impact on the population, which is measured using two different metrics - mass of contaminant consumed, and population dosed above a cumulative mass threshold. Additionally, we also study the importance of various factors that influence the performance of disinfectant booster stations (e.g., sensor placement, contaminant reactivity and toxicity, etc.)

A Spark-based genetic algorithm for sensor placement in large scale drinking water distribution systems

Water pollution incidents have occurred frequently in recent years, causing severe damages, economic loss and long-lasting society impact. A viable solution is to install water quality monitoring sensors in water supply networks (WSNs) for real-time pollution detection, thereby mitigating the risk of catastrophic contamination incidents. Given the significant cost of placing sensors at all locations in a network, a critical issue is where to deploy sensors within WSNs, while achieving rapid detection of contaminant events. Existing studies have mainly focused on sensor placement in water distribution systems (WDSs). However, the problem is still not adequately addressed, especially for large scale WSNs. In this paper, we investigate the sensor placement problem in large scale WDSs with the objective of minimizing the impact of contamination events. Specifically, we propose a two-phase Spark-based genetic algorithm (SGA). Experimental results show that SGA outperforms other traditional algorithms in both accuracy and efficiency, which validates the feasibility and effectiveness of our proposed approach

Alternative Strategies For Optimal Water Quality Sensor Placement In Drinking Water Distribution Networks

The most commonly applied strategies for optimal water quality sensor placement in drinking water distribution systems are aimed at contamination early warning systems. These strategies aim to minimize the number of people affected in case of a deliberate contamination of drinking water in the distribution system, and provide a valuable tool. A number of factors which are usually not taken into account, including the response strategy to the identification of a contamination event, the fallibility of sensors and changes in network configuration (valve manipulation) and operation, may affect the results of these strategies. Since the quickness and effectiveness of a response is generally also a function of the location of the contamination event (both source and first detection), knowledge on the response strategy should also be part of the sensor placement optimization methodology. Hydraulic models generally play a central role in the optimization of sensor placement. The validity of their computations strongly depends upon accurate and up to date information on the network, which is often not fully available (e.g. unregistered valve status changes). Therefore, a sensor network configuration which is somewhat robust to these issues is desirable. Besides contamination early warning systems, there are several other reasons for placing water quality sensors in distribution network, including process control and monitoring, regulatory monitoring, etc. These require a different approach to optimization of the sensor network in terms of sensor locations. In this paper, we demonstrate the application of different sensor location optimization strategies in drinking water distribution networks, with aims such as minimization of the number of people affected, maximization of distribution network coverage, optimization of sensor network robustness and optimization of contamination source identification. We present and compare results of these different approaches applied to hydraulic models of a real drinking water distribution network in the Netherlands

Optimal Placement of Water Quality Monitoring Stations in Sewer Systems: An Information Theory Approach

A core problem associated with the water quality monitoring in the sewer system is the optimal placement of a limited number of monitoring sites. A methodology is provided for optimally design water quality monitoring stations in sewer networks. The methodology is based on information theory, formulated as a multi-objective optimization problem and solved using NSGA-II. Computer code is written to estimate two entropy quantities, namely Joint Entropy, a measure of information content, and Total Correlation, a measure of redundancy, which are maximized and minimized, respectively. The test on a real sewer network suggests the effectiveness of the proposed methodology

Online Distributed Sensor Selection

A key problem in sensor networks is to decide which sensors to query when, in order to obtain the most useful information (e.g., for performing accurate prediction), subject to constraints (e.g., on power and bandwidth). In many applications the utility function is not known a priori, must be learned from data, and can even change over time. Furthermore for large sensor networks solving a centralized optimization problem to select sensors is not feasible, and thus we seek a fully distributed solution. In this paper, we present Distributed Online Greedy (DOG), an efficient, distributed algorithm for repeatedly selecting sensors online, only receiving feedback about the utility of the selected sensors. We prove very strong theoretical no-regret guarantees that apply whenever the (unknown) utility function satisfies a natural diminishing returns property called submodularity. Our algorithm has extremely low communication requirements, and scales well to large sensor deployments. We extend DOG to allow observation-dependent sensor selection. We empirically demonstrate the effectiveness of our algorithm on several real-world sensing tasks