IEEE SENSORS JOURNAL, VOL. 14, NO. 8, AUGUST 2014 2765 A Low-Cost Sensor Network for Real-Time Monitoring and Contamination Detection in Drinking Water Distribution Systems Theofanis P. Lambrou, Christos C. Anastasiou, Christos G. Panayiotou, and Marios M. Polycarpou Abstract— This paper presents a low cost and holistic approach to the water quality monitoring problem for drinking water distribution systems as well as for consumer sites. Our approach is based on the development of low cost sensor nodes for real time and in-pipe monitoring and assessment of water quality on the fly. The main sensor node consists of several in-pipe electrochemical and optical sensors and emphasis is given on low cost, light- weight implementation, and reliable long time operation. Such implementation is suitable for large scale deployments enabling a sensor network approach for providing spatiotemporally rich data to water consumers, water companies, and authorities. Extensive literature and market research are performed to iden- tify low cost sensors that can reliably monitor several parameters, which can be used to infer the water quality. Based on selected parameters, a sensor array is developed along with several microsystems for analog signal conditioning, processing, logging, and remote presentation of data. Finally, algorithms for fusing online multisensor measurements at local level are developed to assess the water contamination risk. Experiments are performed to evaluate and validate these algorithms on intentional con- tamination events of various concentrations of escherichia coli bacteria and heavy metals (arsenic). Experimental results indicate that this inexpensive system is capable of detecting these high impact contaminants at fairly low concentrations. The results demonstrate that this system satisfies the online, in-pipe, low deployment-operation cost, and good detection accuracy criteria of an ideal early warning system. Index Terms— Water quality monitoring, flat surface sensors, turbidity sensor, multi-sensor system, sensor networks, arsenic & bacteria contamination detection. I. I NTRODUCTION C LEAN drinking water is a critical resource, important for the health and well-being of all humans. Drinking water utilities are facing new challenges in their real-time operation because of limited water resources, intensive budget require- ments, growing population, ageing infrastructure, increasingly Manuscript received December 19, 2013; revised March 7, 2014; accepted March 20, 2014. Date of publication April 10, 2014; date of current version July 1, 2014. This work was supported in part by the European Research Council through the ERC Advanced Grant Fault-Adaptive and in part by the European Project Effinet under Grant FP7-ICT-2011-8-31855. The associate editor coordinating the review of this paper and approving it for publication was Dr. M. R. Yuce. T. P. Lambrou, C. G. Panayiotou, and M. M. Polycarpou are with the KIOS Research Center for Intelligent Systems and Networks, Department of Elec- trical and Computer Engineering, University of Cyprus, Nicosia 2102, Cyprus (e-mail: faniseng@ucy.ac.cy; christosp@ucy.ac.cy; mpolycar@ucy.ac.cy). C. C. Anastasiou is with the Department of Civil Engineering, Frederick University, Nicosia 1036, Cyprus (e-mail: c.anastasiou@frederick.ac.cy). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSEN.2014.2316414 stringent regulations and increased attention towards safe- guarding water supplies from accidental or deliberate conta- mination. There is a need for better on-line water monitoring systems given that existing laboratory-based methods are too slow to develop operational response and do not provide a level of public health protection in real time. Rapid detection (and response) to instances of contamination is critical due to the potentially severe consequences to human health. Traditional methods of water quality control involve the manual collection of water samples at various locations and at different times, followed by laboratory analytical techniques in order to characterize the water quality. Such approaches are no longer considered efficient [1]–[5]. Although, the current methodology allows a thorough analysis including chemical and biological agents, it has several drawbacks: a) the lack of real-time water quality information to enable critical decisions for public health protection (long time gaps between sampling and detection of contamination) b) poor spatiotemporal coverage (small number locations are sampled) c) it is labor intensive and has relatively high costs (labor, operation and equipment). Therefore, there is a clear need for continuous on-line water quality monitoring with effi- cient spatio-temporal resolution. US Environmental Protection Agency (USEPA) has carried out an extensive experimental evaluation [6] of water quality sensors to assess their perfor- mance on several contaminations. The main conclusion was that many of the chemical and biological contaminants used have an effect on many water parameters monitored including Turbidity (TU), Oxidation Reduction Potential (ORP), Electrical Conductivity (EC) and pH. Thus, it is feasible to monitor and infer the water quality by detecting changes in such parameters. Given the absence of reliable, in-line, continuous and inex- pensive sensors for monitoring all possible biological and chemical contaminants, our approach is to measure physico- chemical water parameters that can be reliably monitored with low cost sensors and develop low cost networked embedded systems (sensor nodes) as well as contamination detection algorithms to fuse these multi-sensor data in order to infer possible contamination events. Even though this approach may suffer from some false alarms, it can be compen- sated/eliminated by the large scale deployment and the possi- bility of correlating the decisions from various sensor nodes which is the topic of our future work. There is a clear need for a shift in the current monitoring paradigm and this paper proposes the idea of monitoring 1530-437X © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. 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