Evaluating Clustering Algorithms for Optimized Performance in Wireless Sensor Networks

Farij Omar Ehtiba; Fatma Elzahra Abdulwahab Krayem

doi:10.65540/jar.v29i1.993

المؤلفون

Farij Omar Ehtiba
Fatma Elzahra Abdulwahab Krayem

DOI:

https://doi.org/10.65540/jar.v29i1.993

الكلمات المفتاحية:

component، Wireless Sensor Networks، WSN، Energy-Efficient، Routing Protocols

الملخص

Wireless Sensor Network (WSN) has gradually become an essential technology in recent years and is employed in various fields. One of the main challenges of WSNs is their short lifespan; due to the energy consumption which is affected by communication protocols, packet data transfer, and limited battery power factors. And among these factors, many researchers are interested in energy efficiency. In this paper, a new enhanced LEACH routing technique has been proposed. The proposed technique selects cluster heads based on current energy, and it employs a root cluster head with greater current energy and a short distance to the sink to gather all data before sending them to the sink. The results exposed that the proposed technique is outperformed the standard LEACH protocol and extended the WSN network lifespan.

المراجع

References

Singh, A. and R. Kumar. Heart disease prediction using machine learning algorithms. in 2020 international conference on electrical and electronics engineering (ICE3). 2020. IEEE. DOI: https://doi.org/10.1109/ICE348803.2020.9122958

Terrada, O., et al., Supervised machine learning based medical diagnosis support system for prediction of patients with heart disease. Advances in Science, Technology and Engineering Systems Journal, 2020. 5(5): p. 269-277. DOI: https://doi.org/10.25046/aj050533

Yadav, S.S., et al. Automated cardiac disease diagnosis using support vector machine. in 2020 3rd International Conference on Communication System, Computing and IT Applications (CSCITA). 2020. IEEE. DOI: https://doi.org/10.1109/CSCITA47329.2020.9137817

Gupta, A., et al., MIFH: A machine intelligence framework for heart disease diagnosis. IEEE access, 2019. 8: p. 14659-14674. DOI: https://doi.org/10.1109/ACCESS.2019.2962755

Alotaibi, F.S., Implementation of machine learning model to predict heart failure disease. International Journal of Advanced Computer Science and Applications, 2019. 10(6). DOI: https://doi.org/10.14569/IJACSA.2019.0100637

Kavitha, M., et al. Heart disease prediction using hybrid machine learning model. in 2021 6th international conference on inventive computation technologies (ICICT). 2021. IEEE. DOI: https://doi.org/10.1109/ICICT50816.2021.9358597

Chetankumar, Y.S., D.K. Singh, and S. Menaria, A review on: heart disease prediction using data mining. J Emerg Technol Innov Res (JETIR), 2019.

Mohan, S., C. Thirumalai, and G. Srivastava, Effective heart disease prediction using hybrid machine learning techniques. IEEE access, 2019. 7: p. 81542-81554. DOI: https://doi.org/10.1109/ACCESS.2019.2923707

El Hamdaoui, H., et al. A clinical support system for prediction of heart disease using machine learning techniques. in 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP). 2020. IEEE. DOI: https://doi.org/10.1109/ATSIP49331.2020.9231760

10. Valarmathi, R. and T. Sheela, Heart disease prediction using hyper parameter optimization (HPO) tuning. Biomedical Signal Processing and Control, 2021. 70: p. 103033. DOI: https://doi.org/10.1016/j.bspc.2021.103033

Shorewala, V., Early detection of coronary heart disease using ensemble techniques. Informatics in Medicine Unlocked, 2021. 26: p. 100655. DOI: https://doi.org/10.1016/j.imu.2021.100655

Morya, R. and S. Singh. Prediction model of heart diseases based on hybrid model. in Journal of Physics: Conference Series. 2022. IOP Publishing. DOI: https://doi.org/10.1088/1742-6596/2327/1/012071

Singh, P., S. Singh, and G.S. Pandi-Jain, Effective heart disease prediction system using data mining techniques. International journal of nanomedicine, 2018. 13(sup1): p. 121124. DOI: https://doi.org/10.2147/IJN.S124998

Anitha, S. and N. Sridevi, Heart disease prediction using data mining techniques. Journal of analysis and Computation, 2019.

Ray, S., et al. Chi-squared based feature selection for stroke prediction using AzureML. in 2020 Intermountain Engineering, Technology and Computing (IETC). 2020. IEEE. DOI: https://doi.org/10.1109/IETC47856.2020.9249117

Sekulić, A., et al., Random forest spatial interpolation. Remote Sensing, 2020. 12(10): p. 1687. DOI: https://doi.org/10.3390/rs12101687

Boateng, E.Y., J. Otoo, and D.A. Abaye, Basic tenets of classification algorithms Knearest-neighbor, support vector machine, random forest and neural network: a review.

Journal of Data Analysis and Information Processing, 2020. 8(4): p. 341-357.

Xiong, L. and Y. Yao, Study on an adaptive thermal comfort model with K-nearestneighbors (KNN) algorithm. Building and Environment, 2021. 202: p. 108026. DOI: https://doi.org/10.1016/j.buildenv.2021.108026

Das, A., Logistic regression, in Encyclopedia of Quality of Life and Well-Being Research. 2024, Springer. p. 3985-3986. DOI: https://doi.org/10.1007/978-3-031-17299-1_1689

Schober, P. and T.R. Vetter, Logistic regression in medical research. Anesthesia & Analgesia, 2021. 132(2): p. 365-366. DOI: https://doi.org/10.1213/ANE.0000000000005247

Cervantes, J., et al., A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 2020. 408: p. 189-215. DOI: https://doi.org/10.1016/j.neucom.2019.10.118

Yang, L. and A. Shami, On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 2020. 415: p. 295-316. DOI: https://doi.org/10.1016/j.neucom.2020.07.061

Daanoune, I., Baghdad, A., & Balllouk, A. (2019, October). BRE-LEACH: A new approach to extend the lifetime of wireless sensor network. In 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS) (pp. 1-6). IEEE.‏ DOI: https://doi.org/10.1109/ICDS47004.2019.8942253

Al-Sadoon, M. E., Jedidi, A., & Al-Raweshidy, H. (2023). Dual-tier cluster-based routing in mobile wireless sensor network for IoT application. IEEE Access, 11, 4079-4094.‏ DOI: https://doi.org/10.1109/ACCESS.2023.3235200