By the motivation of applicability of sensor nodes in various appliances like military target tracking, wildlife monitoring, and surveillance, and natural disaster relief, hazardous environment exploration, the incessant monitoring of water quality and features can be too an important technology to scrutinize the physicochemical parameters for increasing the succumbs. For that reason, a diversity of sensors can be positioned in the ponds to gather the need parameters and water quality detection can be performed by exploiting the data classification methods. Here, the Convolutional Neural Network (CNN) is exploited to predict the water quality of in Wireless Sensor Network (WSN). Initially, from the pond, the wireless Sensor Nodes (SN) is exploited to sense the data and CNN is modeled to select the output of the layer. Moreover, exploiting the Firefly with Dual update Process (FF-DUP) is used to choose the routing path in an optimal manner. After that, the outputs of Cluster Head (CH)-based routing protocols are transmitted to the sink node, in that the developed CNN is exploited to classify the water quality parameter. At last, the networking performance of the proposed method is evaluated by exploiting normalized energy utilization with the conventional models such as Low-energy adaptive clustering hierarchy based decision tree (LEACH+DT), LEACH based functional tangent decision tree (LEACH+FDT), fractional artificial bee colony algorithm based decision tree (FABC+DT).
S. O. Olatinwo and T. Joubert, "Energy Efficient Solutions in Wireless Sensor Systems for Water Quality Monitoring: A Review," IEEE Sensors Journal, vol. 19, no. 5, pp. 1596-1625, 1 March1, 2019.
Zeshi Zheng, Noah P. Molotch, Carlos A. Oroza, Martha H. Conklin, Roger C. Bales,"Spatial snow water equivalent estimation for mountainous areas using wireless-sensor networks and remote-sensing products",Remote Sensing of Environment, Volume 215, 15 September 2018, Pages 44-56.
Ali Najah Ahmed, Faridah Binti Othman, Haitham Abdulmohsin Afan, Rusul Khaleel Ibrahim, Ahmed Elshafie,"Machine learning methods for better water quality prediction",Journal of Hydrology, Volume 578, November 2019.
Rodelyn Avila, Beverley Horn, Elaine Moriarty, Roger Hodson, Elena Moltchanova,"Evaluating statistical model performance in water quality prediction", Journal of Environmental Management, Volume 206, 15 January 2018, Pages 910-919.
Sadat Mazhar, Allah Ditta, Laura Bulgariu, Iftikhar Ahmad, Ata Allah Nadiri,"Sequential treatment of paper and pulp industrial wastewater: Prediction of water quality parameters by Mamdani Fuzzy Logic model and phytotoxicity assessment",Chemosphere, Volume 227, July 2019, Pages 256-268.
L. Guo, F. Chen, Z. Dai and Z. Liu, "WSN Cluster Head Selection Algorithm Based on Neural Network," 2010 International Conference on Machine Vision and Human-machine Interface, Kaifeng, 2010, pp. 258-260.
A Shankar, N Jaisankar,"Security enabled cluster head selection for wireless sensor network using improved firefly optimization",International conference on soft computing and pattern recognition, 176-192.
Enrique J. Duarte-Melo, Mingyan Liu, "Analysis of energy consumption and lifetime of heterogeneous wireless sensor networks", In proceedings of international conference on IEEE Global Telecommunications, vol. 21, pp. 21 - 25, 2002.
W. Puyati and A. Walairacht, "Efficiency Improvement for Unconstrained Face Recognition by Weightening Probability Values of Modular PCA and Wavelet PCA," 2008 10th International Conference on Advanced Communication Technology, Gangwon-Do, 2008, pp. 1449-1453.
Y. Xiao and J. Keung, "Improving Bug Localization with Character-Level Convolutional Neural Network and Recurrent Neural Network," 2018 25th Asia-Pacific Software Engineering Conference (APSEC), Nara, Japan, 2018, pp. 703-704.
W. Shen and W. Wang, "Node Identification in Wireless Network Based on Convolutional Neural Network," 2018 14th International Conference on Computational Intelligence and Security (CIS), Hangzhou, 2018, pp. 238- 241.
Nazleeni Samiha Haron, Mohd Khuzaimi B Mahamad, Izzatdin Abdul Aziz, Mazlina Mehat, "A System Architecture for Water Quality Monitoring System Using Wired Sensors", In Proceedings of International Symposium on Information Technology, vol. 4, pp. 1 - 7, 2008.
Li Nan, Wang Ruimei, Zhang Jian, Fu Zetian, Zhang XiaoShuan,"Developing a knowledge-based early warning system for fish disease/health via water quality management", Expert Systems with Applications, vol. 36, pp. 6500–6511, 2009.
Fabrice Rossi, Nathalie Villa, "Support Vector Machine For Functional Data Classification", Neurocomputing, Vol. 69, no. 7–9, pp.730–742, March 2006.
Pawlak, Z.: “Rough Sets and Intelligent Data Analysis”, Information Sciences, vol. 147, pp. 1–12, 2002
Thangavel, K., Pethalakshmi, “A.: Dimensionality Reduction based on Rough Set Theory: A Review”, Applied Soft Computing, vol. 9, pp. 1–12, 2009.
Sami G. Daraigan, Ahmed S. Wahdain, Ahmed S. Ba-Mosa, Manal H. Obid, "Linear correlation analysis study of drinking water quality data for AlMukalla City, Hadhramout, Yemen", international journal of environmental sciences, vol. 1, no 7, 2011.
W.Brajula and Praveena S,"Energy Efficient Genetic Algorithm Based Clustering Technique for Prolonging the Life Time of Wireless Sensor Network",Journal of Networking and Communication Systems (JNACS),Volume 1, Issue 1, October2018.
Buy article PDF