Water Distribution System Monitoring and Decision Support Using a Wireless Sensor Network

Water distribution systems comprise labyrinthine networks of pipes, often in poor states of repair, that are buried beneath our city streets and relatively inaccessible. Engineers who manage these systems need reliable data to understand and detect water losses due to leaks or burst events, anomalies in the control of water quality and the impacts of operational activities (such as pipe isolation, maintenance or repair) on water supply to customers. Water Wise is a platform that manages and analyses data from a network of wireless sensor nodes, continuously monitoring hydraulic, acoustic and water quality parameters. Water Wise supports many applications including rolling predictions of water demand and hydraulic state, online detection of events such as pipe bursts, and data mining for identification of longer-term trends. This paper illustrates the advantage of the Water Wise platform in resolving operational decisions

On the Use of an IoT Integrated System for Water Quality Monitoring and Management in Wastewater Treatment Plants

The deteriorating water environment demands new approaches and technologies to achieve sustainable and smart management of urban water systems. Wireless sensor networks represent a promising technology for water quality monitoring and management. The use of wireless sensor networks facilitates the improvement of current centralized systems and traditional manual methods, leading to decentralized smart water quality monitoring systems adaptable to the dynamic and heterogeneous water distribution infrastructure of cities. However, there is a need for a low-cost wireless sensor node solution on the market that enables a cost-effective deployment of this new generation of systems. This paper presents the integration to a wireless sensor network and a preliminary validation in a wastewater treatment plant scenario of a low-cost water quality monitoring device in the close-to-market stage. This device consists of a nitrate and nitrite analyzer based on a novel ion chromatography detection method. The analytical device is integrated using an Internet of Things software platform and tested under real conditions. By doing so, a decentralized smart water quality monitoring system that is conceived and developed for water quality monitoring and management is accomplished. In the presented scenario, such a system allows online near-real-time communication with several devices deployed in multiple water treatment plants and provides preventive and data analytics mechanisms to support decision making. The results obtained comparing laboratory and device measured data demonstrate the reliability of the system and the analytical method implemented in the device.Ingeniería, Industria y Construcció

Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks

Knowledge Discovery in the SCADA Databases Used for the Municipal Power Supply System

This scientific paper delves into the problems related to the develop-ment of intellectual data analysis system that could support decision making to manage municipal power supply services. The management problems of mu-nicipal power supply system have been specified taking into consideration modern tendencies shown by new technologies that allow for an increase in the energy efficiency. The analysis findings of the system problems related to the integrated computer-aided control of the power supply for the city have been given. The consideration was given to the hierarchy-level management decom-position model. The objective task targeted at an increase in the energy effi-ciency to minimize expenditures and energy losses during the generation and transportation of energy carriers to the Consumer, the optimization of power consumption at the prescribed level of the reliability of pipelines and networks and the satisfaction of Consumers has been defined. To optimize the support of the decision making a new approach to the monitoring of engineering systems and technological processes related to the energy consumption and transporta-tion using the technologies of geospatial analysis and Knowledge Discovery in databases (KDD) has been proposed. The data acquisition for analytical prob-lems is realized in the wireless heterogeneous medium, which includes soft-touch VPN segments of ZigBee technology realizing the 6LoWPAN standard over the IEEE 802.15.4 standard and also the segments of the networks of cellu-lar communications. JBoss Application Server is used as a server-based plat-form for the operation of the tools used for the retrieval of data collected from sensor nodes, PLC and energy consumption record devices. The KDD tools are developed using Java Enterprise Edition platform and Spring and ORM Hiber-nate technologies

The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholder’s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management