4.8 Article

On Cost-Efficient Sensor Placement for Contaminant Detection in Water Distribution Systems

Journal

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 12, Issue 6, Pages 2177-2185

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2016.2569413

Keywords

Contamination detection; cost efficiency; sensor placement; water distribution system (WDS)

Funding

  1. National Science Foundation of China [61272470, 61305087, 61402425, 61502439, 61501412]
  2. Fundamental Research Funds for National University, China University of Geosciences, Wuhan [CUG14065, CUGL150829]
  3. Provincial Natural Science Foundation of Hubei [2015CFA065]

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In recent years, water pollution or contamination incidents happened frequently, causing serious disasters and negative social impact. To reduce the water contamination risk, water quality monitoring sensors should be deployed in water distribution system (WDS) to enable real-time pollution detection. It is desirable to deploy sensors everywhere so that any contamination event can be detected and reported in a timely manner. Unfortunately, this is a luxury and unrealistic vision because of high deployment cost. It is significant to lower the deployment cost provided that the quality-of-sensing, e.g., coverage and contamination detection time, can be guaranteed for effective depollution action. In this paper, we consider a water quality monitoring sensor network consisting of two kinds of sensors with different prices. The expensive one is of cellular communication capability and therefore is able to send sensing information to control center directly, while the cheaper one is of only sensor-to-sensor communication capability. We investigate a cost-efficient sensor deployment problem on how to deploy these two kinds of sensors in a given WDS to minimize the deployment cost, without violating the quality-of-sensing requirement. We first formulate the problem into a mixed integer quadratically constrained programming problem, which is then linearized into an equivalent mixed integer linear programming. We further propose a polynomial two-stage heuristic algorithm and evaluate its efficiency via extensive simulation-based studies.

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