Journal of Cybersecurity and Information Management

Journal DOI

https://doi.org/10.54216/JCIM

Submit Your Paper

2690-6775ISSN (Online) 2769-7851ISSN (Print)

Volume 13 , Issue 1 , PP: 76-84, 2024 | Cite this article as | XML | Html | PDF | Full Length Article

Enhancing K-Nearest Neighbors Algorithm in Wireless Sensor Networks through Stochastic Fractal Search and Particle Swarm Optimization

Ahmed Mohamed Zaki 1 * , Abdelaziz A. Abdelhamid 2 , Abdelhameed Ibrahim 3 , Marwa M. Eid 4 , El-Sayed M. El-Kenawy 5

  • 1 Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA - (azaki@jcsis.org)
  • 2 Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt - (abdelaziz@cis.asu.edu.eg)
  • 3 School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain - (abdelhameed.fawzy@polytechnic.bh)
  • 4 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt; Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt - (mmm@ieee.org)
  • 5 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt - (skenawy@ieee.org)
  • Doi: https://doi.org/10.54216/JCIM.130108

    Received: May 28, 2023 Revised: August 16, 2023 Accepted: December 21, 2023
    Abstract

    The utilization of wireless sensor networks (WSNs) holds significant importance in diverse data collection applications. Efficient operation of computers, especially in predictive tasks, is imperative for obtaining accurate results within WSNs. This research introduces an innovative approach employing Stochastic Fractal Search-Particle Swarm Optimization (SFS-PSO) to enhance the performance of the K-Nearest Neighbors (KNN) algorithm. The proposed methodology initiates with the establishment of a particle population, dynamically adjusting their positions and velocities and integrating a diffusion process. Through an iterative process of incremental adjustments and evaluations, the algorithm fine-tunes its parameters, resulting in a refined KNN regression model. The enhanced model exhibits substantial improvements, as indicated by the notable reduction in root mean square error (RMSE) and mean absolute error (MAE), accompanied by a strengthened correlation between variables. The favorable outcomes underscore the efficacy of the SFS-PSO optimization technique in augmenting the KNN algorithm's performance within wireless sensor networks. In simpler terms, the application of SFS-PSO in conjunction with KNN leads to a significant decrease in RMSE, reaching a value as low as 0.00894, demonstrating the notable effectiveness of this optimization approach in refining the predictive capabilities of the KNN algorithm in the context of WSNs.

    Keywords :

    Wireless Sensor Network , Optimization , Stochastic Fractal Search , Particle Swarm Optimization , K-Nearest Neighbors , Algorithm.

    References

    [1]     Aggarwal, K., Sreenivasula Reddy, G., Makala, R., Srihari, T., Sharma, N., & Singh, C. (2024). Studies on energy efficient techniques for agricultural monitoring by wireless sensor networks. Computers and Electrical Engineering, 113, 109052. https://doi.org/10.1016/j.compeleceng.2023.109052

    [2]     Kandris, D., Nakas, C., Vomvas, D., & Koulouras, G. (2020). Applications of Wireless Sensor Networks: An Up-to-Date Survey. Applied System Innovation, 3(1), Article 1. https://doi.org/10.3390/asi3010014

    [3]     Huanan, Z., Suping, X., & Jiannan, W. (2021). Security and application of wireless sensor network. Procedia Computer Science, 183, 486–492. https://doi.org/10.1016/j.procs.2021.02.088

    [4]     Shahraki, A., Taherkordi, A., Haugen, Ø., & Eliassen, F. (2020). Clustering objectives in wireless sensor networks: A survey and research direction analysis. Computer Networks, 180, 107376. https://doi.org/10.1016/j.comnet.2020.107376

    [5]     Rizk, F. H., Arkhstan, S., Zaki, A. M., Kandel, M. A., & Towfek, S. K. (2023). Integrated CNN and Waterwheel Plant Algorithm for Enhanced Global Traffic Detection. Journal of Artificial Intelligence and Metaheuristics, Volume 6(Issue 2), 36–45. https://doi.org/10.54216/JAIM.060204

    [6]     Singh, A., Sharma, S., & Singh, J. (2021). Nature-inspired algorithms for Wireless Sensor Networks: A comprehensive survey. Computer Science Review, 39, 100342. https://doi.org/10.1016/j.cosrev.2020.100342

    [7]     Zivkovic, M., Bacanin, N., Tuba, E., Strumberger, I., Bezdan, T., & Tuba, M. (2020). Wireless Sensor Networks Life Time Optimization Based on the Improved Firefly Algorithm. 2020 International Wireless Communications and Mobile Computing (IWCMC), 1176–1181. https://doi.org/10.1109/IWCMC48107.2020.9148087

    [8]     Harizan, S., & Kuila, P. (2020). Nature-Inspired Algorithms for k-Coverage and m-Connectivity Problems in Wireless Sensor Networks. In S. K. Das, S. Samanta, N. Dey, & R. Kumar (Eds.), Design Frameworks for Wireless Networks (pp. 281–301). Springer. https://doi.org/10.1007/978-981-13-9574-1_12

    [9]     Wireless Sensor Network Data. (n.d.). [dataset]. Retrieved January 3, 2024, from https://www.kaggle.com/datasets/halimedogan/wireless-sensor-network-data.

    [10]    Jyoti, Singh, J., & Gosain, A. (2023). Handling Missing Values Using Fuzzy Clustering: A Review. In A. Bhattacharya, S. Dutta, P. Dutta, & V. Piuri (Eds.), Innovations in Data Analytics (pp. 341–353). Springer Nature. https://doi.org/10.1007/978-981-99-0550-8_28

    [11]    Zaki, A. M., Khodadadi, N., Lim, W. H., & Towfek, S. K. (2023). Predictive Analytics and Machine Learning in Direct Marketing for Anticipating Bank Term Deposit Subscriptions. American Journal of Business and Operations Research, Volume 11(Issue 1), 79–88. https://doi.org/10.54216/AJBOR.110110

    [12]    Smiti, A. (2020). A critical overview of outlier detection methods. Computer Science Review, 38, 100306. https://doi.org/10.1016/j.cosrev.2020.100306

    [13]    Choi, I., Koh, W., Koo, B., & Chang Kim, W. (2024). Network-based exploratory data analysis and explainable three-stage deep clustering for financial customer profiling. Engineering Applications of Artificial Intelligence, 128, 107378. https://doi.org/10.1016/j.engappai.2023.107378

    [14]    Kazemi-Khasragh, E., Fernández Blázquez, J. P., Garoz Gómez, D., González, C., & Haranczyk, M. (2024). Facilitating polymer property prediction with machine learning and group interaction modelling methods. International Journal of Solids and Structures, 286–287, 112547. https://doi.org/10.1016/j.ijsolstr.2023.112547

    [15]    Abualigah, L., Elaziz, M. A., Khodadadi, N., Forestiero, A., Jia, H., & Gandomi, A. H. (2022). Aquila Optimizer Based PSO Swarm Intelligence for IoT Task Scheduling Application in Cloud Computing. In E. H. Houssein, M. Abd Elaziz, D. Oliva, & L. Abualigah (Eds.), Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems (pp. 481–497). Springer International Publishing. https://doi.org/10.1007/978-3-030-99079-4_19

    [16]    Singh, A., Patel, S., Bhadani, V., Kumar, V., & Gaurav, K. (2024). AutoML-GWL: Automated machine learning model for the prediction of groundwater level. Engineering Applications of Artificial Intelligence, 127, 107405. https://doi.org/10.1016/j.engappai.2023.107405

    [17]    Eid, M. M., El-Kenawy, E.-S. M., Khodadadi, N., Mirjalili, S., Khodadadi, E., Abotaleb, M., Alharbi, A. H., Abdelhamid, A. A., Ibrahim, A., Amer, G. M., Kadi, A., & Khafaga, D. S. (2022). Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Prediction of Monkeypox Cases. Mathematics, 10(20), Article 20. https://doi.org/10.3390/math10203845

    [18]    Sanni, O., Adeleke, O., Ukoba, K., Ren, J., & Jen, T.-C. (2024). Prediction of inhibition performance of agro-waste extract in simulated acidizing media via machine learning. Fuel, 356, 129527. https://doi.org/10.1016/j.fuel.2023.129527

    [19]    Zaki, A. M., Towfek, S. K., Gee, W., Zhang, W., & Soliman, M. A. (2023). Advancing Parking Space Surveillance using A Neural Network Approach with Feature Extraction and Dipper Throated Optimization Integration. Journal of Artificial Intelligence and Metaheuristics, Volume 6(Issue 2), 16–25. https://doi.org/10.54216/JAIM.060202

    [20]    Khazalah, A., Prasanthi, B., Thomas, D., Vello, N., Jayaprakasam, S., Sumari, P., Abualigah, L., Ezugwu, A. E., Hanandeh, E. S., & Khodadadi, N. (2023). Image Processing Identification for Sapodilla Using Convolution Neural Network (CNN) and Transfer Learning Techniques. In L. Abualigah (Ed.), Classification Applications with Deep Learning and Machine Learning Technologies (pp. 107–127). Springer International Publishing. https://doi.org/10.1007/978-3-031-17576-3_5

    Cite This Article As :
    Mohamed, Ahmed. , A., Abdelaziz. , Ibrahim, Abdelhameed. , M., Marwa. , M., El-Sayed. Enhancing K-Nearest Neighbors Algorithm in Wireless Sensor Networks through Stochastic Fractal Search and Particle Swarm Optimization. Journal of Cybersecurity and Information Management, vol. , no. , 2024, pp. 76-84. DOI: https://doi.org/10.54216/JCIM.130108
    Mohamed, A. A., A. Ibrahim, A. M., M. M., E. (2024). Enhancing K-Nearest Neighbors Algorithm in Wireless Sensor Networks through Stochastic Fractal Search and Particle Swarm Optimization. Journal of Cybersecurity and Information Management, (), 76-84. DOI: https://doi.org/10.54216/JCIM.130108
    Mohamed, Ahmed. A., Abdelaziz. Ibrahim, Abdelhameed. M., Marwa. M., El-Sayed. Enhancing K-Nearest Neighbors Algorithm in Wireless Sensor Networks through Stochastic Fractal Search and Particle Swarm Optimization. Journal of Cybersecurity and Information Management , no. (2024): 76-84. DOI: https://doi.org/10.54216/JCIM.130108
    Mohamed, A. , A., A. , Ibrahim, A. , M., M. , M., E. (2024) . Enhancing K-Nearest Neighbors Algorithm in Wireless Sensor Networks through Stochastic Fractal Search and Particle Swarm Optimization. Journal of Cybersecurity and Information Management , () , 76-84 . DOI: https://doi.org/10.54216/JCIM.130108
    Mohamed A. , A. A. , Ibrahim A. , M. M. , M. E. [2024]. Enhancing K-Nearest Neighbors Algorithm in Wireless Sensor Networks through Stochastic Fractal Search and Particle Swarm Optimization. Journal of Cybersecurity and Information Management. (): 76-84. DOI: https://doi.org/10.54216/JCIM.130108
    Mohamed, A. A., A. Ibrahim, A. M., M. M., E. "Enhancing K-Nearest Neighbors Algorithm in Wireless Sensor Networks through Stochastic Fractal Search and Particle Swarm Optimization," Journal of Cybersecurity and Information Management, vol. , no. , pp. 76-84, 2024. DOI: https://doi.org/10.54216/JCIM.130108