Many real-time applications make use of advanced wireless sensor networks (WSNs). Because of the limited memory, power limits, narrow communication bandwidth, and low processing units of wireless sensor nodes (SNs), WSNs suffer severe resource constraints. Data prediction algorithms in WSNs have become crucial for reducing redundant data transmission and extending the network's longevity. Redundancy can be decreased using proper machine learning (ML) techniques while the data aggregation process operates. Researchers persist in searching for effective modelling strategies and algorithms to help generate efficient and acceptable data aggregation methodologies from preexisting WSN models. This work proposes an energy-efficient Adaptive Seagull Optimization Algorithm (ASOA) protocol for selecting the best cluster head (CH). An extreme learning machine (ELM) is employed to select the data corresponding to each node as a way to generate a tree to cluster sensor data. The Dual Graph Convolutional Network (DGCN) is an analytical method that predicts future trends using time series data. Data clustering and aggregation are employed for each cluster head to efficiently perform sample data prediction across WSNs, primarily to minimize the processing overhead caused by the prediction algorithm. Simulation findings suggest that the presented method is practical and efficient regarding reliability, data reduction, and power usage. The results demonstrate that the suggested data collection approach surpasses the existing Least Mean Square (LMS), Periodic Data Prediction Algorithm (P-PDA), and Combined Data Prediction Model (CDPM) methods significantly. The proposed DGCN method has a transmission suppression rate of 92.68%, a difference of 22.33%, 16.69%, and 12.54% compared to the current methods (i.e., LMS, P-PDA, and CDPM).
S. Thomas and T. Mathew, “Secure Data Aggregation in Wireless Sensor Network using Chinese Remainder Theorem,” International Journal of
Electronics and Telecommunications, Jul. 2023, doi: 10.24425/ijet.2022.139886.
O. Ojuroye, R. Torah, S. Beeby, and A. Wilde, “Smart Textiles for Smart Home Control and Enriching Future Wireless Sensor Net work Data,”
Smart Sensors, Measurement and Instrumentation, pp. 159–183, Oct. 2016, doi: 10.1007/978-3-319-47319-2_9.
S. Z. Erdogan and T. T. Bilgin, “A data mining approach for fall detection by using k -nearest neighbour algorithm on wireless sensor network
data,” IET Communications, vol. 6, no. 18, pp. 3281–3287, Dec. 2012, doi: 10.1049/iet-com.2011.0228.
S. Boubiche, D. E. Boubiche, A. Bilami, and H. Toral-Cruz, “Big Data Challenges and Data Aggregation Strategies in Wireless Sensor
Networks,” IEEE Access, vol. 6, pp. 20558–20571, 2018, doi: 10.1109/access.2018.2821445.
X. Li, X. Tao, and Z. Chen, “Spatio-Temporal Compressive Sensing-Based Data Gathering in Wireless Sensor Networks,” IEEE Wireless
Communications Letters, vol. 7, no. 2, pp. 198–201, Apr. 2018, doi: 10.1109/lwc.2017.2764899.
Ben Arbi, F. Derbel, and F. Strakosch, “Forecasting methods to reduce energy consumption in WSN,” 2017 IEEE International Instrumentation
and Measurement Technology Conference (I2MTC), May 2017, doi: 10.1109/i2mtc.2017.7969960.
Hongbo Jiang, Shudong Jin, and Chonggang Wang, “Prediction or Not? An Energy-Efficient Framework for Clustering-Based Data Collection in
Wireless Sensor Networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 6, pp. 1064–1071, Jun. 2011, doi:
10.1109/tpds.2010.174.
S. C, S. D, and N. A, “Accurate Data Aggregation Created by Neural Network and Data Classification Processed Through Machine Learning in
Wireless Sensor Networks,” Sep. 2021, doi: 10.21203/rs.3.rs-895195/v1.
Hongbo Jiang, Shudong Jin, and Chonggang Wang, “Prediction or Not? An Energy-Efficient Framework for Clustering-Based Data Collection in
Wireless Sensor Networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 6, pp. 1064–1071, Jun. 2011, doi:
10.1109/tpds.2010.174.
G. Mustafaraj, G. Lowry, and J. Chen, “Prediction of room temperature and relative humidity by autoregressive linear and nonlinear neural
network models for an open office,” Energy and Buildings, vol. 43, no. 6, pp. 1452–1460, Jun. 2011, doi: 10.1016/j.enbuild.2011.02.007.
A. Agarwal and A. Dev, "A Data Prediction Model Based on Extended Cosine Distance for Maximizing Network Lifetime of WSN," WSEAS
Transactions on Computer Research, Vol 7, pp. 23–28, 2019.
G. B. Tayeh, A. Makhoul, D. Laiymani, and J. Demerjian, “A distributed real-time data prediction and adaptive sensing approach for wireless
sensor networks,” Pervasive and Mobile Computing, vol. 49, pp. 62–75, Sep. 2018, doi: 10.1016/j.pmcj.2018.06.007.
S. A. Soleymani et al., “A Hybrid Prediction Model for Energy-Efficient Data Collection in Wireless Sensor Networks,” Symmetry, vol. 12, no.
12, p. 2024, Dec. 2020, doi: 10.3390/sym12122024.
G. C. Jagan and P. J. Jayarin, “A Novel Machine Language-Driven Data Aggregation Approach to Predict Data Redundancy in IoT -Connected
Wireless Sensor Networks,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1–20, Oct. 2022, doi: 10.1155/2022/7096561.
H. Wang, Z. Yemeni, W. M. Ismael, A. Hawbani, and S. H. Alsamhi, “A reliable and energy efficient dual prediction data reduct ion approach for
WSNs based on Kalman filter,” IET Communications, vol. 15, no. 18, pp. 2285–2299, Jul. 2021, doi: 10.1049/cmu2.12262.
S. Loganathan, J. Arumugam, and V. Chinnababu, “An energy‐efficient clustering algorithm with self‐diagnosis data fault detection and
prediction for wireless sensor networks,” Concurrency and Computation: Practice and Experience, vol. 33, no. 17, Apr. 2021, doi:
10.1002/cpe.6288.
Uma Maheswari Arumugam, Suganthi Perumal, “Trust based Secure and Reliable Routing Protocol of Military Communication on MANETs”,
Journal of Machine and Computing, pp. 047-057, January 2023. doi: 10.53759/7669/jmc202303006.
K. Jain and A. Kumar, “An energy-efficient prediction model for data aggregation in sensor network,” Journal of Ambient Intelligence and
Humanized Computing, vol. 11, no. 11, pp. 5205–5216, Mar. 2020, doi: 10.1007/s12652-020-01833-2.
W. Zheng, “Current Technologies and Applications of Digital Image Processing,” Journal of Biomedical and Sustainable Healthca re
Applications, pp. 13–23, Jan. 2023, doi: 10.53759/0088/jbsha202303002.
N. Wambui, “Medical Identification and Sensing Technology for Assisting and E-Health Monitoring Systems for Disabled and Elderly Persons,”
Journal of Biomedical and Sustainable Healthcare Applications, pp. 9–17, Jan. 2022, doi: 10.53759/0088/jbsha202202002.
A. Agarwal, K. Jain, and A. Dev, “Modeling and Analysis of Data Prediction Technique Based on Linear Regression Model (DP -LRM) for
Cluster-Based Sensor Networks,” International Journal of Ambient Computing and Intelligence, vol. 12, no. 4, pp. 98–117, Oct. 2021, doi:
10.4018/ijaci.2021100106.
S. Ninisha Nels and J. Amar Pratap Singh, “Hierarchical Fractional Quantized Kernel Least mean Square Filter in Wireless Sensor Network for
Data Aggregation,” Wireless Personal Communications, vol. 120, no. 2, pp. 1171–1192, May 2021, doi: 10.1007/s11277-021-08509-w.
H. Wang, Z. Yemeni, W. M. Ismael, A. Hawbani, and S. H. Alsamhi, “A reliable and energy efficient dual prediction data reduct ion approach for
WSNs based on Kalman filter,” IET Communications, vol. 15, no. 18, pp. 2285–2299, Jul. 2021, doi: 10.1049/cmu2.12262.
K. Jain and A. Kumar, “A lightweight data transmission reduction method based on a dual prediction technique for sensor networks,”
Transactions on Emerging Telecommunications Technologies, vol. 32, no. 11, Aug. 2021, doi: 10.1002/ett.4345.
S. Famila, A. Jawahar, S. L. S. Vimalraj, and J. Lydia, “Integrated Energy and Trust-Based Semi-Markov Prediction for Lifetime Maximization
in Wireless Sensor Networks,” Wireless Personal Communications, vol. 118, no. 1, pp. 505–522, Jan. 2021, doi: 10.1007/s11277-020-08028-0.
X. Liu, G. Li, and P. Shao, “A Multi-Mechanism Seagull Optimization Algorithm Incorporating Generalized Opposition-Based Nonlinear
Boundary Processing,” Mathematics, vol. 10, no. 18, p. 3295, Sep. 2022, doi: 10.3390/math10183295.
X.-J. Mao, C. Shen, and Y.-B. Yang, "Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip
Connections," Neural Information Processing Systems (NIPS 2016).
T. N. Kipf and M. Welling, "Semi-Supervised Classification with Graph Convolutional Networks," Conference Paper at ICLR 2017, doi:
10.48550/arXiv.1609.02907.
X. Fu, Q. Qi, Z.-J. Zha, Y. Zhu, and X. Ding, “Rain Streak Removal via Dual Graph Convolutional Network,” Proceedings of the AAAI
Conference on Artificial Intelligence, vol. 35, no. 2, pp. 1352–1360, May 2021, doi: 10.1609/aaai.v35i2.16224.
X.-Y. Liu et al., “<italic>CDC</italic>: Compressive Data Collection for Wireless Sensor Networks,” IEEE Transactions on Parallel
and Distributed Systems, vol. 26, no. 8, pp. 2188–2197, Aug. 2015, doi: 10.1109/tpds.2014.2345257.
D. Fernandes, A. G. Ferreira, R. Abrishambaf, J. Mendes, and J. Cabral, “A machine learning-based dynamic link power control in wearable
sensing devices,” Wireless Networks, vol. 27, no. 3, pp. 1835–1848, Jan. 2021, doi: 10.1007/s11276-020-02539-1.
Acknowledgements
Author(s) thanks to Annamalai University for research lab and equipment support.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Balakumar D
Balakumar D
Department of ECE, Annamalai University, Chidambaram, Tamil Nadu, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Balakumar D and Rangaraj J, “A Prediction Model Based Energy Efficient Data Collection for Wireless Sensor Networks”, Journal of Machine and Computing, vol.3, no.4, pp. 360-378, October 2023. doi: 10.53759/7669/jmc202303031.
