An Intelligent IoT based Wireless Sensor Network for Monitoring Water Quality by using RNN in Real Time

Water uses is increasing day by day. As development continues, the demand for water is increasing. Water is require for daily routine, for irrigation, for fish and wildlife and for industrial use, not only water but pure water is require. This is a helpful approach to make people or authorities awar...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of innovative technology and exploring engineering 2022-09, Vol.11 (10), p.37-42
Hauptverfasser: Afreen, Sana, Singh, Dr. Shashank, Singh, Sarika, Dwivedi, Archana, Chaudhary, Vipin Kumar
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Water uses is increasing day by day. As development continues, the demand for water is increasing. Water is require for daily routine, for irrigation, for fish and wildlife and for industrial use, not only water but pure water is require. This is a helpful approach to make people or authorities aware and alert about water quality in real-time situation. In this paper, the proposed technology helps to monitor the water quality in real time situation or environment. The technology such as Internet of Things, Wireless Sensor Networkand Cloud Computingare used in this approach for water quality parameters (pH, minerals and Temperature) measuring in real-time environment. For water quality prediction and analysis, a training data set has been prepared and these training data sets use for categorize utility of water in different field. The sensor sensed the water parameters and send this sensed value to the cloud server for processing. These data compared with training data set. In this paper monitor data classify by using Naive Bayes and the utility of water can be predicted by Recurrent Neural Network. The resultant of this proposed approach are: it gives high accuracy and the response time of this approach is very less comparatively.
ISSN:2278-3075
2278-3075
DOI:10.35940/ijitee.K7663.09111022