Abstract
With the inspiration of applicability of sensor nodes in various applications, such as wildlife monitoring, military target tracking and surveillance, hazardous environment exploration, and natural disaster relief, the continuous monitoring of water quality and characteristics can also be a significant application to monitor the physicochemical parameters for maximizing the yields. Accordingly, a variety of sensors can be located inside the ponds to collect the required parameters and the detection of water quality can be done using the data classification algorithms. In this paper, we have proposed a distributed functional tangent decision tree (DFTDT) classifier to predict the quality of water in wireless sensor networks. At first, the wireless sensor node is used to sense the data from the pond and the functional tangent decision tree is constructed by utilizing the functional tangent entropy for selection of attributes and split points. Here, routing path is optimally identified using cluster head-based routing protocol based on fractional calculus artificial bee colony algorithm, in which the individual decision trees are merged along the routing path. Then, the results of cluster head-based routing protocol are sent to sink node, in which the proposed DFTDT classifier is used to classify the water quality parameter using the randomly generated pseudo data. Finally, the networking performance of the proposed algorithm can be evaluated using normalized energy consumption with the existing works. From the results, we proved that, the proposed algorithm achieves the better prediction accuracy as 80%.












Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Haron NS, Mahamad MKB, Aziz IA, Mehat M (2008) A system architecture for water quality monitoring system using wiredsensors. In: Proceedings of international symposium on information technology 4:1–7
Li N, Wang R, Zhang J, Fu Z, Zhang X (2009) Developing a knowledge-based early warning system for fish disease/health via water quality management. Expert Syst Appl 36(3):6500–6511
Xu E, Lin S, Jin L (2014) Data recovery method for seafood quality safety system based on rough set theory. Int J Secur Appl 8(5):195–202
Li C, Huang Y (2006) Food Safety monitoring and early warning system. Chemical Industry Press, Beijing
Daraigan SG, Wahdain AS, BaMosa AS, Obid MH (2011) Linear correlation analysis study of drinking water quality data for AlMukalla City, Hadhramout, Yemen. Int J Environ Sci 1(7):1692–1701
Bazartseren B, Hildebrandt G, Holz KP (2003) Short-term water level prediction using neural networks and neuro-fuzzy approach. Neurocomputing 55(3–4):439–450
Rossi F, Villa N (2006) Support vector machine for functional data classification. Neurocomputing 69(7–9):730–742
Rizzon L, Rossi M, Passerone R, Brunelli D (2013) Wireless sensor networks for environmental monitoring powered by microprocessors heat dissipation. In: Proceedings of 1st international workshop on Energy Neutral Sensing Systems, no. 8
Dyo V et al. (2012) WILDSENDING: design and deployment of asustainable sensor network for wildlife monitoring. ACM Trans Sensor Netw 8(4):29
Heydari MM, Abasi A, Rohani SM, Hosseini SMA (2013) Correlation study and regression analysis of drinking water quality in Kashan city, Iran. Middle East J Sci Res 13(9):238–1244
Avancha S, Baxi A, Kotz D (2012) Privacy in mobile technology for personal healthcare. ACM Comput Surv 45(1):3
Yoon JP (2013) Three-tiered data mining for big data patters of wirelesssensor networks in medical and healthcare domains. In: Proceedings of international conference on Internet and web applications and services, pp 18–24, .
Srinivas K, RaghavendraRao G, Govardhan A (2014) Rough-fuzzy classifier: a system to predict the heart disease by blending two different set theories. Arab J Sci Eng 39(4):2857–2868
Simon A, Shanmugam P (2012) An algorithm for classification of algal blooms using MODIS-aqua data in oceanic waters around India. Adv Remote Sens 1(2):35–51
Bhatnagar A, Devi P (2013) Water quality guidelines for the management of pond fish culture. Environ Sci 3:1980–2009 (No. 6)
Suresh GV, Reddy EV, Reddy ES (2012) Uncertain data classification using rough set theory. In: Proceedings of international conference on information systems design and intelligent applications, pp 869–877
Pawlak Z (2002) Rough sets and intelligent data analysis. Inf Sci 147(1–4):1–12
Thangavel K, Pethalakshmi A (2009) Dimensionality reduction based on rough set theory: a review. Appl Soft Comput 9(1):1–12
Mazumder RU, Begum SA, Biswas D (2015) Rough Fuzzy classification for class imbalanced data. In: Proceedings of fourth international conference on soft computing for problem solving. Advances in intelligent systems computing 335:159–171
Kumar R, Kumar D (2016) Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network. Wirel Netw 22(5):1461–1474
Hu YC (2013) Rough sets for pattern classification using pairwise-comparison-based tables. Appl Math Modell 37(12–13):7330–7337
Al-Aidaroos K, Bakar AA, Othman Z (2010) Data classification using rough sets and naïve bayes. In: Proceedings of international conference on rough set and knowledge technology, pp 134–142
Pitarch Y, Ienco D, Vintrou E, Bégué A, Laurent A, Poncelet P, Sala M, Teisseire M (2015) Spatio-temporal data classification through multidimensional sequential patterns: application to crop mapping in complex landscape. Eng Appl Artif Intell 37:91–102
Cheng X, Xu J, Pei J, Liu J (2010) Hierarchical distributed data classification in wireless sensor networks. Comput Commun 33(12):1404–1413
Stankovic SV, Rakocevic O, Kojic N, Milicev D (2012) A classification and comparison of data mining algorithms for wireless sensor networks. In: Proceedings of international conference on industrial technology, pp 265–270
Fawzy A, Mokhtar HMO, Hegazy O (2013) Outliers detection and classification in wireless sensor networks. Egypt Inf 14(2):157–164
Yoon JP, Ortiz J (2015) Data mining approach to situation-aware sensoractuation in wireless sensor networks. In: Proceedings of international conference on future generation communication technologies, pp 1–6
Hong-Gui H, Chen QL, Qiao JF (2011) An efficient self-organizing RBF neural network for water quality prediction. Neural Netw 24(7):717–725
Xiang Y, Jiang L (2009) Water quality prediction using LS-SVM and particle swarm optimization. In: Proceedings of second international workshop in knowledge discovery and data mining, pp 900–904
Karabudak D Security optimization and data classification in wireless sensor networks. http://www.cs.bilkent.edu.tr/~guvenir/courses/CS550/Workshop/Dilek_Karabudak.pdf. Accessed 15 Jan 2016
Duarte-Melo EJ, Liu M (2002) Analysis of energy consumption and lifetime of heterogeneous wireless sensor networks. In: Proceedings of international conference on IEEE global telecommunications 21:21–25
Su CT, Hsiao YH (2009) Multiclass MTS for simultaneous feature selection and classification. IEEE Trans Knowl Data Eng 21:192–205
Chu F, Wang LP (2005) Applications of support vector machines to cancer classification with microarray data. Int J Neural Syst 15(6):475–484
Steyrl D, Scherer R, Faller J, Müller-Putz GR (2016) Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier. Biomed Eng 61:77–86 (No.1)
Mitra V, Chia-Jiu Wang, Banerjee S (2006) Lidar detection of underwater objects using a neuro-SVM-based architecture. IEEE Trans Neural Netw 17:717–731
Boussouf M, Quafafou M (2001) Scalable feature selection using rough set theory. Rough Sets CurrTrends Comput LNCS 2005:131–138
Caballero Y, Alvarez D, Bello R, Garcia MM (2007) Feature selection algorithms using rough set theory. In: Seventh international conference on intelligent systems design and applications, ISDA 2007, pp 407–411
Fazayeli F, Wang LP, Mandziuk J (2008) Feature selection based on the rough set theory and expectation-maximization clustering algorithm. Rough Sets Curr Trends Comput LNCS 5306:272–282
Wang LP, Zhou N, Chu F (2008) A general wrapper approach to selection of class-dependent features. IEEE Trans Neural Netw 19(7):1267–1278
Tsang S, Kao B, Yip KY, Ho WS, Lee SD (2009) Decision trees for uncertain data. IEEE Trans Knowl Data Eng 23(1):64–78
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chandanapalli, S.B., Reddy, E.S. & Lakshmi, D.R. DFTDT: distributed functional tangent decision tree for aqua status prediction in wireless sensor networks. Int. J. Mach. Learn. & Cyber. 9, 1419–1434 (2018). https://doi.org/10.1007/s13042-017-0653-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13042-017-0653-0