Multimedia ResearchISSN:2582-547X

Optimization Driven Distributed Deep Learning for Aqua Status Prediction in IoT

  • J Rajeshwar

Abstract

The continuous screening of the quality and characteristics of water in IoT (Internet of Things) is essential due to the increasing requirement on aquaculture in order to maximize the yields. There are various physicochemical parameters used in water quality monitoring, but the analysis of these parameters are needed to obtain the final decision with experts. This paper proposes an aqua status prediction model in IoT using the Fractional Gravitational Search Algorithm based distributed Deep Convolutional Neural network (FGSA-based distributed Deep CNN). Initially, the aqua parameters are analysed using the distributed IoT nodes in the aqua environment. The loss and delay in the transmission of the data related to the aqua status is controlled with the selection of cluster head optimally using the FGSA. The prediction of the aqua status is done with the distributed DeepCNN in the final step. The performance of the proposed method is analyzed with the evaluation metrics, namely accuracy, energy, and throughput. The accuracy, energy, and throughput of the proposed FGSA-based distributed Deep CNN classifier is obtained as 95.4758, 99.4293, and 99.9571, respectively, which is high as compared to the existing methods. This shows the effectiveness of the proposed method in the prediction of the aqua status.

References

  • Li, F, Wei, Y, Chen, Y, Li, D, Zhang, X, "An Intelligent Optical Dissolved Oxygen Measurement Method Based on a Fluorescent Quenching Mechanism," Sensors, vol.15, pp.30913–30926, 2015.
  • Jiang, Y.; Li, Z.; Fang, J.; Yue, J.; Li, D, "Automatic video tracking of Chinese mitten crabs based on the particle filter algorithm using a biologically constrained probe and resampling," Comput. Electron. Agric, vol.106, pp.111–119, 2014.
  • Chen, Y., Zhen, Z., Yu, H. and Xu, J., "Application of fault tree analysis and fuzzy neural networks to fault diagnosis in the internet of things (IoT) for aquaculture," Sensors, vol.17, no.1, p.153, 2017.
  • Parameswari, M. and Moses, M.B., "Online measurement of water quality and reporting system using prominent rule controller based on aqua care-IOT," Design Automation for Embedded Systems, vol.22, no.1, pp.25-44, 2018.
  • Xiang, Y. and Jiang, L., "Water quality prediction using LS-SVM and particle swarm optimization," in proceedings of Second International Workshop on Knowledge Discovery and Data Mining, pp. 900-904, 2009.
  • Zhang, Y., Hua, J. and Wang, Y.B., "Application effect of aquaculture IOT system," In Applied Mechanics and Materials, vol. 303, pp. 1395-1401, 2013.
  • Shi, B., Sreeram, V., Zhao, D., Duan, S. and Jiang, J., "A wireless sensor network-based monitoring system for freshwater fishpond aquaculture," Biosystems Engineering, vol.172, pp.57-66, 2018.
  • Chen, Y., Yu, H., Cheng, Y., Cheng, Q. and Li, D., "A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture," PloS one, vol.13, no.2, pp. 0192456, 2018.
  • Chandanapalli, S.B., Reddy, E.S. and Lakshmi, D.R., "DFTDT: distributed functional tangent decision tree for aqua status prediction in wireless sensor networks," International Journal of Machine Learning and Cybernetics, pp.1-16, 2017.
  • Yueting, W., Jiaming, L., Long, Y., Xiaoming, L. and Weiqi, L.,"Self-cleaning aquacultural water quality monitoring system design," IFAC-PapersOnLine, vol.51, no.17, pp.359-362, 2018.
  • Chavan, M.S., Patil, M.V.P., Chavan, S., Sana, S. and Shinde, C.,"Design and Implementation of IOT Based Real Time Monitoring System for Aquaculture using Raspberry Pi," International Journal on Recent and Innovation Trends in Computing and Communication, vol.6, no.3, pp.159-161, 2018.
  • Hongpin, L., Guanglin, L., Weifeng, P., Jie, S. and Qiuwei, B., "Real-time remote monitoring system for aquaculture water quality, " International Journal of Agricultural and Biological Engineering, vol.8, no.6, pp.136-143, 2015.
  • Bazartseren B, Hildebrandt G, Holz KP, "Short-term water level prediction using neural networks and neuro- fuzzy approach, "Neurocomputing, vol.55, no.3, pp.439–450, 2003.
  • Rossi F, Villa N, "Support vector machine for functional data classification," Neurocomputing, vol.69, no.7, pp.730–742, 2006.
  • Chandanapalli, S.B., Sreenivasa Reddy, E. and Rajya Lakshmi, D., "FTDT: Rough set integrated functional tangent decision tree for finding the status of aqua pond in aquaculture," Journal of Intelligent & Fuzzy Systems, vol.32, no.3, pp.1821-1832, 2017.
  • Han, H.G., Chen, Q.L. and Qiao, J.F., "An efficient self-organizing RBF neural network for water quality prediction," Neural networks, vol.24, no.7, pp.717-725, 2011.
  • Dhumane, A.V. and Prasad, R.S., "Multi-objective fractional gravitational search algorithm for energy efficient routing in IoT," Wireless networks, pp.1-15, 2017.
  • Satish Chander, P. Vijaya, Praveen Dhyani, "Fractional Lion Algorithm-An Optimization Algorithm for Data Clustering", Journal of Computer Science, Vol. 12, no. 7, pp. 323-340, 2016.
  • Rashedi, Esmat, Hossein Nezamabadi-Pour, and Saeid Saryazdi. "GSA: a gravitational search algorithm", Information sciences, vol. 179, no. 13, pp. 2232-2248, 2009.
  • Alexander Rakhlin, Alexey Shvets, Vladimir Iglovikov, and Alexandr A. Kalinin, "Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis", International Conference Image Analysis and Recognition ICIAR, Image Analysis and Recognition, pp. 737-744, 2018.
  • Jamroen, C., Yonsiri, N., Odthon, T., Wisitthiwong, N. and Janreung, S., “A standalone photovoltaic/battery energy-powered water quality monitoring system based on narrowband internet of things for aquaculture: Design and implementation”, Smart Agricultural Technology, Vol. 3, pp.100072, 2023.
  • Kimothi, S., Thapliyal, A., Singh, R., Rashid, M., Gehlot, A., Akram, S.V. and Javed, A.R., “Comprehensive Database Creation for Potential Fish Zones Using IoT and ML with Assimilation of Geospatial Techniques”, Sustainability, Vol. 15(2), pp.1062, 2023.
  • Ramanathan, R., Duan, Y., Valverde, J., Van Ransbeeck, S., Ajmal, T. and Valverde, S., “Using IoT Sensor Technologies to Reduce Waste and Improve Sustainability in Artisanal Fish Farming in Southern Brazil”, Sustainability, Vol. 15(3), pp.2078, 2023.
  • Syrmos, E., Sidiropoulos, V., Bechtsis, D., Stergiopoulos, F., Aivazidou, E., Vrakas, D., Vezinias, P. and Vlahavas, I., “An Intelligent Modular Water Monitoring IoT System for Real-Time Quantitative and Qualitative Measurements”, Sustainability, Vol. 15(3), pp.2127, 2023.
  • Arrighi, C. and Castelli, F., “Prediction of ecological status of surface water bodies with supervised machine learning classifiers”, Science of The Total Environment, Vol. 857, pp.159655, 2023.
  • Armitage, D.W., “Global maps of lake surface water temperatures reveal pitfalls of air‐for‐water substitutions in ecological prediction”, Ecography, Vol. 2023(2), pp.e06595, 2023.
  • Zhou, Y., Wang, X., Li, W., Zhou, S. and Jiang, L., “Water Quality Evaluation and Pollution Source Apportionment of Surface Water in a Major City in Southeast China Using Multi-Statistical Analyses and Machine Learning Models”, International Journal of Environmental Research and Public Health, Vol. 20(1), pp.881, 2023.
  • Lv, M., Niu, X., Zhang, D., Ding, H., Lin, Z., Zhou, S. and Zhu, Y., “A Data-Driven Framework for Spatiotemporal Analysis and Prediction of River Water Quality: A Case Study in Pearl River, China Water”, Vol. 15(2), pp.257, 2023.
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DOI :

Author information

Affiliations

Department of CSE Guru Nanak Institutions Technical Campus, Hyderabad, Telangana, India.

Publisher Information

  • Received

    28 September

  • Revised

    20 December

  • Accepted

    3 January

Keywords
Aquaculture, Aqua Parameters, Deep Convolutional Neural Network, Internet of Things, Prediction,

Publisher : Resbee Info Technologies Pvt Ltd