Sensor network design of contammination warning systems: A decision framework

Abstract

Contamination warning systems have been proposed as a promising approach for detecting contaminants in drinking water systems early enough to allow for the effective reduction of public health or economic consequences. A variety of modeling and optimization tools has been developed to support the design of utility‐specific contamination warning systems. However, warning system design is not a straightforward application of a software tool; it requires a series of significant decisions about the warning system—s intended nature, purpose, and use. In this article, the authors describe a decision framework that uses the threat ensemble vulnerability assessment and sensor placement optimization tool (TEVA‐SPOT) to determine sensor placement. The framework allows for the development of multiple sensor network designs that can be compared and evaluated against a standard set of criteria. TEVA‐SPOT and the decision framework have been applied to several large water systems; one of those utility examples is included here.

Detection

“…The U.S. EPA worked on the development of a CWS as part of the Water Security Initiative that was initially referred to as the WaterSentinal project (U.S. EPA, 2007(U.S. EPA, , 2008b(U.S. EPA, , 2008c. In addition, Murray et al (2008) summarized the framework for designing a CWS, including guidance on determining the number of sensors used and determining system objectives. A comprehensive CWS should consist of the following elements ( Each of the five elements of a CWS involves a concentrated effort by water utilities.…”

“…Sensor network design typically involves determining the number of sensors to place in the network and the optimal locations for these sensors. Review papers summarizing the use of optimization protocols and development of CWS are available (Ailamaki et al, 2003;Murray et al, 2008Murray et al, , 2009.…”

Protecting Water Supply Critical Infrastructure: An Overview

“…Response delay time can be defined as the time period from the first uncertain detection to the cessation of any additional public health consequences. Numerous researchers have examined the influence of response time on the magnitude of public health consequences Skadsen et al 2008;Murray et al 2008) and showed that the effectiveness of a CWS to reduce public health exposures can decrease by 50 % or more when response delay increases from 12 to 48 h. Murray et al (2008) show that the performance of a CWS can decrease substantially (as much as 70 %) when the response delay time is increased from 6 to 24 h. It is hard to imagine how response could be initiated in sufficient time without a continuous, real-time understanding of system flows and chemical (water quality) transport. Bristow and Brumbelow (2006) examine the ''temporal and procedural space'' between the detection of anomalous water quality event to the response decision(s) which result in the cessation of individuals ingesting contaminated tap water.…”

Stochastic Steepest-Descent Optimization of Multiple-Objective Mobile Sensor Coverage

“…In general, mobile nodes are useful in situations where the installation of extensive static sensing/communication devices is difficult. For example, chemical sensors can be deployed at specific locations in a water distribution system (WDS) to monitor the water quality for the growth of biological masses, the presence of contaminants, etc [10], [11], [12]. Long-range wireless communication under water is, however, infeasible due to high signal attenuation.…”