Open AccessInternet of Things based Real time Water Monitoring System
Author(s) -
L. Raja,
Dr.G. Santhi,
Dr.P.S. Periasamy
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b2026.078219
Subject(s) - creatures , environmental science , pollution , hazardous waste , cloud computing , computer science , turbidity , environmental monitoring , water quality , internet of things , damages , pollutant , environmental engineering , computer security , engineering , waste management , operating system , ecology , history , archaeology , natural (archaeology) , biology , law , political science
In real time computing, the embedded systems are a collection of electrical and mechanical systems with a association of small computers. The advancement of such electro mechanical systems with the environment is crucial in today’s scenario. Nowadays, the environmental pollution creates hazardous effect on the living creatures. The harmful pollutants should be identified and should be evacuated for the survival of mankind and other living organisms. Among the environmental pollutions, water pollution is more contaminated than air and soil. The water pollution damages the individual species and all biological creatures. The qualitative analysis of water is a thrust area of research with intelligent systems. At present, testing equipment are used in the laboratories to perform investigations with various water samples. These laboratory analysis make use of chemicals, reagents and sensors for determining the suitability of water for drinking purposes. Such laboratory methods are time consuming and are relatively expensive. To address this issue, quality of drinking water needs to be measured and monitored in real time for the safety of the people. This paper attempts to develop a low cost real time monitoring system to measure various parameters such as PH value, turbidity level and temperature. The measured values are processed through Raspberry Pi 3 module. The results are validated with real-time samples collected from various places and conveyed to the remote user through cloud server