On-line water quality inspection system: the role of the wireless sensory network

Abstract

Abstract There is an increasing dependence on freshwater sources for various human activities because of population growth and rising industrialization across the globe. Meanwhile, the safety of available freshwater is threatened by the massive generation of waste from increasing domestic and industrial activities. The need for continuous assessment of the quality of the environmental water available has become a crucial research concern. The conventional techniques commonly used are not sufficient to meet the expanding demand for real-time, rapid, low-cost, reliable, and sensitive water quality monitoring (WQM). The use of wireless sensor networks (WSN) has been proposed by various researchers as a sustainable substitute for the traditional processes of monitoring water quality. In this work, an array of the literature on the practical applications of the networks in the assessment of vital water quality parameters such as pH, turbidity, temperature, dissolved oxygen (DO), chlorine content, etc., were surveyed and analyzed. Various technologies such as machine learning, blockchain, internet of things (IoT), deep reconstruction model, etc., were incorporated with WSN for real-time monitoring of water quality, data acquisition, and reporting for a broad range of water bodies. The survey shows that the networks are comparatively affordable and allow remote, real-time, and sensitive measurement of these parameters with minimal human involvement. The use of a low-power wide area network (LPWAN) was also introduced to solve a major problem of power supply often associated with the use of WSN. Recent developments also showed the capacity of WSN to assess simultaneously multiple water quality parameters from several locations using unmanned aerial vehicles (UAV). However, the networks rely on established parameters to indicate a compromise in water quality, but in most cases, fail to identify which pollutant species are responsible.