Multiobjective Contaminant Sensor Network Design for Water Distribution Systems

Abstract

A contaminant intentional intrusion into a water distribution system is one of the most difficult threats to address. This is because of the uncertainty of the type of the injected contaminant and its consequences, and the uncertainty of the location and intrusion time. An online contaminant sensor network is the main constituent to enhance the security of a water distribution system against such a threat. In this study a multiobjective model for water distribution system optimal sensor placement using the nondominated sorted genetic algorithm II is developed and demonstrated using two water distribution systems of increasing complexity. Tradeoffs between three objectives are explored: ͑1͒ sensor detection likelihood; ͑2͒ sensor detection redundancy; and ͑3͒ sensor expected detection time. Pareto fronts are plotted for pairs of conflicting objectives, and simultaneously for all three. A contamination event heuristic sampling methodology is developed for overcoming the problem of contamination event sampling.

Sensor Placement in Water Networks Using a Population-Based Ant Colony Optimization Algorithm

“…Its performance was tested on two realistic water networks which have already been used in previous sensor placement studies [15,16] 1 . The solutions presented here constitute the non-dominated solutions found via WSP-PACO over 20 simulation runs with a single run lasting 10000 solution construction steps.…”

Greedy Algorithms for Sensor Location in Sewer Systems

“…However, when distinct fitness functions with different objectives are considered (e.g., [20][21][22]), many optimal solutions are reported in the form of a Pareto front, and then a further criterion has to been individuated to select which to implement. Differently, to obtain a single optimal solution, procedures 4, 5 and 6 use the GR_M approach with the optimization problem formulated considering one fitness function including different objectives.…”

“…The different objectives may be applied either separately (single-objective procedure) or simultaneously (multi-objective procedure) in the optimization formulation. Some multi-objective approaches consider different objectives grouped together in a single function (e.g., [12,18,19]), while in other formulations they remain distinct (e.g., [18,[20][21][22]). In the latter procedures, a group of solutions are reported in the form of Pareto front without individuating the single best solution to implement.…”

“…Among the methodologies assuming separate objective functions, in [20], network detection likelihood, redundancy and expected detection time are considered, and tradeoff curves are obtained simultaneously for all three objectives and for exploring pairs of objectives. The procedure has been tested on the real water system of Richmond.…”

Test System for Mapping Interdependencies of Critical Infrastructures for Intelligent Management in Smart Cities

“…In most works, sensor placement has been addressed as an optimization problem which aims to choose a finite subset of nodes out of the set of all the network nodes where it is feasible to install sensors, by minimizing a set of objectives (e.g., risk) with respect to certain impact metrics (e.g., the number of people affected) [28], [29]. Various challenges have been identified in research, which affect the sensor placement solutions: the uncertainties in the model and the water demands, the impact metrics and the risk objectives selection, the contamination scenarios selection, the sensor measurement uncertainties, the response time delays, the solution methodology and its computational efficiency [30][31][32][33]. The state-of-the-art in application software is the TEVA-SPOT, which is available under an open-source software license [34].…”