International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 17, No. 2, 8 Apr. 2025
Cover page and Table of Contents: PDF (size: 2596KB)
Design of Congestion Prediction Model on Network Towers using Rough Set Theory for High Speed Low Latency Communication
PDF (2596KB), PP.1-18
Views: 0 Downloads: 0
Author(s)
Index Terms
Super MICE, RST, Hypertuned SVM, GOA, Congestion Prediction
Abstract
Wireless communication for data and a variety of wireless interacted devices have increased dramatically in the past few years. Millimeter wave (mmWave) technology can serve the primary objectives of 5G networks, which include high data throughput and low latency. But mmWave signals for communications lacking substantial diffraction and are consequently more susceptible to obstruction by environmental physical objects, which could cause communication lines to be disrupted and congestion takes place. Wireless data transmission suffers from blockages and path loss, causes high latency as well as reduces the data transmission speed and degrades in quality performance. To overcome the limitations, Rough Set Theory with hypertuned SVM is implemented and designed the congestion prediction model based on the behaviour of network towers for low latency and high-speed data transmission. The data from the different towers is initially collected and created as a dataset. Super MICE is a technique to replace the missing data. Then, the Rough Set Theory is utilized to cluster the data into equivalent classes based on the behaviour of 5G, 4G and 3G wireless network. Hypertuned SVM with a Gazelle optimization algorithm is applied to predict the congestion level by accurately selecting the hyperparameter. By employing performance metrics, the proposed approach is examined and contrasted with existing techniques. The evaluation of performance measurements for the proposed method includes informedness attained as 91%, Adjusted Rand Index obtained value as 0.83, Jaccard as 0.737. Accuracy, precision, sensitivity, error, F1_score, and NPV are also achieved at 93%, 92%, 94%, 7%, 92%, and 90%, respectively. According to this evaluation, the proposed model is superior to perform than the earlier used existing methods.
Cite This Paper
D. Priyanka, Y. K. Sundara Krishna, "Design of Congestion Prediction Model on Network Towers using Rough Set Theory for High Speed Low Latency Communication", International Journal of Computer Network and Information Security(IJCNIS), Vol.17, No.2, pp.1-18, 2025. DOI:10.5815/ijcnis.2025.02.01
Reference
[1]L. Li, P. Sharma, M. Gheisari and A. Sharma, “Research on TCP performance model and transport agent architecture in broadband wireless network”, Scalable Computing: Practice and Experience, 2021, vol. 22, no. 2, pp. 193-201.
[2]M. Prakash and A. Abdrabou, “On the fidelity of ns-3 simulations of wireless multipath tcp connections”, Sensors, 2020, vol. 20, no. 24, pp. 7289.
[3]M. H. Hanin, M. Amnai and Y. Fakhri, “New adaptation method based on cross layer and TCP over protocols to improve QoS in mobile ad hoc network”, International Journal of Electrical & Computer Engineering, 2021, vol. 11, no. 3, pp. 2088-8708.
[4]M. N. Girish and A. Pande, “Improving TCP performance in wireless networks”, 2020.
[5]S. P. Kuppusamy and M. Subramaniam, “Deep learning-based TCP congestion control algorithm for disaster 5G environment”, 2023.
[6]M. R. Kanagarathinam, S. Singh, I. Sandeep, H. Kim, M. K. Maheshwari, J. Hwang, A. Roy and N. Saxena, “NexGen D-TCP: Next generation dynamic TCP congestion control algorithm”, IEEE Access, 2020, vol. 8, pp. 164482-164496.
[7]E. Sy, T. Mueller, C. Burkert, H. Federrath and M. Fischer, “Enhanced performance and privacy for TLS over TCP fast open”, arXiv preprint arXiv:1905.03518. 2019.
[8]T. Chowdhury and M. J. Alam, “Performance evaluation of TCP Vegas over TCP Reno and TCP NewReno over TCP Reno”, JOIV: International Journal on Informatics Visualization, 2019, vol. 3, no. 3, pp. 275-282.
[9]A. Hernawan, “Comparative Performance Testing of the Impact of ACK Loss in TCP Tahoe, TCP Reno, and TCP New Reno on the ns-2 Simulator”, JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 2023, vol. 7, no. 1, pp. 91-101.
[10]V. U. Devi and A. Thilaka, “A Comprehensive Analysis and Comparison of TCP Reno and TCP New Reno”.
[11]J. Lorincz, Z. Klarin and J. Ožegović, “A comprehensive overview of TCP congestion control in 5G networks: Research challenges and future perspectives”, Sensors, 2021, vol. 21, no. 13, pp. 4510.
[12]A. Khan, U. Shoaib and M. Sarfraz, “A comprehensive study of modern and high speed TCP-variant in linux kernel: TCP CUBIC”, Int. Arab J. Inf. Technol., 2019, vol. 16, no. 6, pp. 1028-1035.
[13]B. Jaeger, D. Scholz, D. Raumer, F. Geyer and G. Carle, “Reproducible measurements of TCP BBR congestion control”, Computer Communications, 2019, vol. 144, pp. 31-43.
[14]A. Jannesari, M. A. Sarram and R. Sheikhpour, “A novel network coding algorithm to improve tcp in wireless networks”, Wireless Personal Communications, 2020, vol. 110, pp. 1199-1216.
[15]W. Yang, P. Dong, L. Cai and W. Tang, “Loss-aware throughput estimation scheduler for multi-path TCP in heterogeneous wireless networks”, IEEE Transactions on Wireless Communications, 2021, vol. 20, no. 5, pp. 3336-3349.
[16]T. Saedi and H. El-Ocla, “TCP CERL+: Revisiting TCP congestion control in wireless networks with random loss”, Wireless Networks, 2021, vol. 27, pp. 423-440.
[17]G. Olmedo, R. Lara-Cueva, D. Martínez and C. de Almeida, “Performance analysis of a novel TCP protocol algorithm adapted to wireless networks”, Future Internet, 2020, vol. 12, no. 6, pp. 101.
[18]W. Na, B. Bae, S. Cho and N. Kim, “DL-TCP: Deep learning-based transmission control protocol for disaster 5G mmWave networks”, IEEE Access., 2019, vol. 7, pp. 145134-145144.
[19]R. Poorzare and A. C. Augé, “FB-TCP: A 5G mmWave friendly TCP for urban deployments”, IEEE access, 2021, vol. 9, pp. 82812-82832.
[20]M. A. Alrshah, M. A. Al-Maqri and M. Othman, “Elastic-TCP: Flexible congestion control algorithm to adapt for high-BDP networks”, IEEE Systems Journal, 2019, vol. 13, no. 2, pp. 1336-1346.
[21]M. Joseph Auxilius Jude, V. C. Diniesh and M. Shivaranjani, “Throughput stability and flow fairness enhancement of TCP traffic in multi-hop wireless networks”, Wireless Networks., 2020, vol. 26, no. 6, pp. 4689-4704.
[22]H. S. Laqueur, A. B. Shev and R. M. Kagawa, “SuperMICE: An ensemble machine learning approach to multiple imputation by chained equations”, American journal of epidemiology., 2022, vol. 191, no. 3, pp. 516-525.
[23]T. Carpenito and J. Manjourides, “MISL: Multiple imputation by super learning”, Statistical methods in medical research, 2022, vol. 31, no. 10, pp. 1904-1915.
[24]E. Forghani, R. Sheikh, S. M. H. Hosseini and S. S. Sana, “The impact of digital marketing strategies on customer’s buying behavior in online shopping using the rough set theory”, International journal of system assurance engineering and management, 2022, pp. 1-16.
[25]L. Lei, W. Chen, B. Wu, C. Chen and W. Liu, “A building energy consumption prediction model based on rough set theory and deep learning algorithms”, Energy and Buildings, 2021, vol. 240, pp. 110886.
[26]R. Guido, M. C. Groccia and D. Conforti, “A hyper-parameter tuning approach for cost-sensitive support vector machine classifiers”, Soft Computing, 2023, vol. 27, no. 18, pp. 12863-12881.
[27]D. J. Kalita, V. P. Singh and V. Kumar, “A dynamic framework for tuning SVM hyper parameters based on Moth-Flame Optimization and knowledge-based-search”, Expert Systems with Applications, 2021, vol. 168, pp. 114139.
[28]J. O. Agushaka, A. E. Ezugwu and L. Abualigah, “Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer”, Neural Computing and Applications, 2023, vol. 35, no. 5, pp. 4099-4131.
[29]L. Abualigah, A. Diabat and R. A. Zitar, “Orthogonal learning Rosenbrock’s direct rotation with the gazelle optimization algorithm for global optimization”, Mathematics, 2022, vol. 10, no. 23, pp. 4509.
[30]Dataset 1: https://github.com/uccmisl/5Gdataset/blob/master/5G-production-dataset.zip