Oleksandr Lavrut; Tetiana Lavrut; Victoria Vysotska; Zhengbing Hu; Yuriy Ushenko; Dmytro Uhryn

Application of Tensor Networks Analysis to Optimize Traffic Management in a Critical Information and Telecommunications Network

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Author(s)

Oleksandr Lavrut ¹ Tetiana Lavrut ² Victoria Vysotska ³ Zhengbing Hu ⁴ Yuriy Ushenko ^5,6,* Dmytro Uhryn ⁶

1. Tactics Department, Hetman Petro Sahaidachnyi National Army Academy, Lviv, 79012, Ukraine

2. Department of the Science Center, Hetman Petro Sahaidachnyi National Army Academy, Lviv, 79012, Ukraine

3. Department of Information Systems and Networks, Institute of Computer Sciences and Information Technologies, Lviv Polytechnic National University, Lviv, 79013, Ukraine

4. School of Computer Science, Hubei University of Technology, Wuhan, China

5. Department of Physics, Shaoxing University, Shaoxing, Zhejiang Province 312000, China

6. Yuriy Fedkovych Chernivtsi National University, Chernivtsi, 58012, Ukraine

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2025.04.08

Received: 11 Mar. 2025 / Revised: 16 May 2025 / Accepted: 25 Jun. 2025 / Published: 8 Aug. 2025

Index Terms

Telecommunication Network, Information Exchange, Multipath Routing, Traffic Management, Tensor Analysis of Networks, Network-Centric Principle (Single Information Field)

Abstract

The article investigates the task of optimising traffic management in critical information and telecommunication networks in order to ensure a guaranteed quality of user service, particularly in emergencies. A method of tensor analysis of networks is proposed, using a formalised description of the system in the form of tensors of message lengths, delays and bandwidth of channels. The network is modelled as a simplified complex, and routing is implemented through a tensor equation of connection between network parameters in different coordinate systems. Experimental calculations using examples with dynamically variable topology have shown:

•Reduction of average multipath message delivery latency by 9–40% depending on traffic intensity,
•Probability of packet delivery at or above 0.999 under high loads (200-300 messages/s),
•Zero jitter due to the even distribution of delays between paths,
•The ability to adaptively fragment messages in nodes to reduce latency,
•Increasing the efficiency of resource use compared to single-track models.

The use of a tensor apparatus provides stable and scalable routing in an unstable network topology. The method allows you to take into account the heterogeneity of traffic, adapt to the loss of nodes or channels, and maintain guarantees of quality of service in real time. The proposed approach is of practical importance for information and telecommunication systems used in emergencies, in particular for coordinating the actions of emergency rescue services, emergency medicine, civil protection, military units, control of drones and robotic means in the face of infrastructure loss. Potential stakeholders include state and municipal security services, operators of critical networks (energy, transport, healthcare), developers of automated control systems, and manufacturers of secure communication equipment. The proposed method can be integrated into decentralised networks with limited resources and variable topology, where traditional routing approaches do not guarantee sufficient quality of service.

Cite This Paper

Oleksandr Lavrut, Tetiana Lavrut, Victoria Vysotska, Zhengbing Hu, Yuriy Ushenko, Dmytro Uhryn, "Application of Tensor Networks Analysis to Optimize Traffic Management in a Critical Information and Telecommunications Network", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.17, No.4, pp. 128-161, 2025. DOI:10.5815/ijigsp.2025.04.08

Reference

[1]Lemeshko, O.; Lebedenko, T.; Holoveshko, M. Development and Research of Active Queue Management Method on Interfaces of Telecommunication Networks Routers. In Data-Centric Business and Applications; Ageyev, D., Radivilova, T., Kryvinska, N., Eds.; Lecture Notes on Data Engineering and Communications Technologies; Springer: Cham, 2021; Vol. 69, pp 1–20. DOI: 10.1007/978-3-030-71892-3_1.
[2]Lemeshko, O.; Yeremenko, O.; Yevdokymenko, M.; Sleiman, B. Research and Development of Bilinear QoS Routing Model over Disjoint Paths with Bandwidth Guarantees in SDN. In Advances in Computer Science for Engineering and Education VI. ICCSEEA 2023; Hu, Z., Dychka, I., He, M., Eds.; Lecture Notes on Data Engineering and Communications Technologies; Springer: Cham, 2023; Vol. 181, pp 223–235. DOI: 10.1007/978-3-031-36118-0_20.
[3]Barkalov, A.; Lemeshko, O.; Persikov, A.; Yeremenko, O.; Titarenko, L. Evaluation of Traffic Engineering Routing Models Based on Type of Service in Communication Networks. Electronics 2024, 13, 3638. DOI: 10.3390/electronics13183638.
[4]Barkalov, A.; Lemeshko, O.; Yeremenko, O.; Titarenko, L.; Yevdokymenko, M. Solving Load Balancing Problems in Routing and Limiting Traffic at the Network Edge. Appl. Sci. 2023, 13, 9489. DOI: 10.3390/app13179489.
[5]Barreiros, M.; Lundqvist, P. QoS-Enabled networks: Tools and foundations; John Wiley & Sons: Hoboken, NJ, UA, 2015.
[6]Cristobo, L.; Ibarrola, E.; Casado-O'Mara, I.; Zabala, L. Global quality of service (QoS) management for wireless networks. Electronics 2024, 13, 3113. DOI: 10.3390/electronics13163113.
[7]Zdorenko, Y.; Lavrut, O.; Lavrut, T.; Nastishin, Y. Method of Power Adaptation for Signals Emitted in a Wireless Network in Terms of Neuro-Fuzzy System. Wirel. Pers. Commun. 2020, 115(1), 597–609. DOI: 10.1007/s11277-020-07588-5.
[8]Lavrut, O.O.; Lavrut, T.V.; Klimovych, O.K.; Zdorenko, Yu.M. The latest technologies and means of communication in the Armed Forces of Ukraine: the path of transformation and prospects for development. Science and Technology of the Air Force of the Armed Forces of Ukraine 2019, 1(34), 91–101. DOI: 10.30748/nips.2019.34.13.
[9]Lemeshko, O.; Yeremenko, O.; Yevdokymenko, M.; Radivilova, T. Research of the QoE Fast ReRouting Processes with Differentiated R-Factor Maximization for VoIP-Flows Using the Tensor Model of the Corporate Telecommunication Network. In Current Trends in Communication and Information Technologies; Vorobiyenko, P., Ilchenko, M., Strelkovska, I., Eds.; Lecture Notes in Networks and Systems; Springer: Cham, 2021; Vol. 212, pp 95–110. DOI: 10.1007/978-3-030-76343-5_6.
[10]Lemeshko, O.; Yeremenko, O.; Yevdokymenko, M.; Lemeshko, V.; Persikov, M. QoS-Aware Adaptation Traffic Engineering Solution for Multipath Routing in Communication Network. In Information and Communication Technologies and Sustainable Development. ICT&SD 2022; Dovgyi, S., Trofymchuk, O., Ustimenko, V., Globa, L., Eds.; Lecture Notes in Networks and Systems; Springer: Cham, 2022; Vol. 809, pp 133–150. DOI: 10.1007/978-3-031-46880-3_9.
[11]Eremenko, O. S.; Solomyanyi, M. V.; Shestovalov, S. S.; Persikov, M. A. Research of the process of reliable routing to ensure the stability of the communication network. Problems of telecommunications 2024, 1(34), 12–29. DOI: 10.30837/pt.2024.1.02
[12]Bertsekas, D.; Gallager, R. Data networks, 2nd edition; Athena Scientific: Belmont, 2022.
[13]Kleinrock, L. A delay-dependent queue discipline. Naval Research Logistics Quarterly 1964, 11(3-4), 329-341.
[14]Kron, G. Tensor analysis of networks; John Wiley & Sons: New York, 1939.
[15]Lemeshko, О.; Evseeva, O. Tensor geometrization of the structural and functional representation of the telecommunication system in the basis of interpolar paths and internal sections. Scientific notes of Ukrainian Research Institute of Communications 2010, 1(13), 14–26. https://alem.ucoz.ua/_ld/0/32_Lemeshko.pdf.
[16]Lemeshko, O.; Yeremenko, O.; Yevdokymenko, M.; Ahmad, M. H. Tensor Multiflow Routing Model to Ensure the Guaranteed Quality of Service Based on Load Balancing in Network. In Advances in Computer Science for Engineering and Education III (ICCSEEA 2020); Springer: Kyiv, 2020; pp 1–12. DOI: 10.1007/978-3-030-55506-1_11.
[17]Matseliukh, Y.; Bublyk, M.; Vysotska, V. Development of Intelligent System for Visual Passenger Flows Simulation of Public Transport in Smart City Based on Neural Network. CEUR Workshop Proceedings 2021, 2870, 1087–1138.
[18]Matseliukh, Y.; Vysotska, V.; Bublyk, M. Intelligent system of visual simulation of passenger flows. CEUR Workshop Proceedings 2020, 2604, 906–920.
[19]Shuaishuai Zhang, Xiaofeng Wang. Robust tensor decomposition with kernel rescaled error loss for feature extraction and dimensionality reduction. Expert Systems with Applications. 25 April 2025. https://doi.org/10.1016/j.eswa.2025.126514
[20]Yue Hu, Fangsen Cui, Yifan Zhao, Fucai Li, Shuai Cao, Fu-zhen Xuan. Tensor robust principal component analysis based on Bayesian Tucker decomposition for thermographic inspection. Mechanical Systems and Signal Processing. Volume 204, 1 December 2023, 110761. https://doi.org/10.1016/j.ymssp.2023.110761
[21]Hadi Salehi, Alex Gorodetsky, Roya Solhmirzaei, Pengcheng Jiao. High-dimensional data analytics in civil engineering: A review on matrix and tensor decomposition. Engineering Applications of Artificial Intelligence. Volume 125, October 2023, 106659. https://doi.org/10.1016/j.engappai.2023.106659
[22]Keqian Min, Qing Mai, Junge Li. Optimality in high-dimensional tensor discriminant analysis. Pattern Recognition. Volume 143, November 2023, 109803. https://doi.org/10.1016/j.patcog.2023.109803
[23]Bin Xiao, Heng-Chao Li, Rui Wang, Yu-Bang Zheng. Fully-connected tensor network decomposition with gradient factors regularization for robust tensor completion. Signal Processing.Volume 233, August 2025, 109933. https://doi.org/10.1016/j.sigpro.2025.109933
[24]Yun-Yang Liu, Xi-Le Zhao, Gemine Vivone. Rank-revealing fully-connected tensor network decomposition and its application to tensor completion. Pattern Recognition. Volume 165, September 2025, 111610. https://doi.org/10.1016/j.patcog.2025.111610
[25]Chao Luo, Jie Zhang, Xiaoqian Zhang. Tensor multi-view clustering method for natural image segmentation. Expert Systems with Applications. Volume 260, 15 January 2025, 125431. https://doi.org/10.1016/j.eswa.2024.125431
[26]Daoyuan Wang, Lianzhi Wang, Wenlan Chen, Hong Wang, Cheng Liang. Unsupervised multi-view feature selection based on weighted low-rank tensor learning and its application in multi-omics datasets. Engineering Applications of Artificial Intelligence. Volume 143, 1 March 2025, 110041. https://doi.org/10.1016/j.engappai.2025.110041
[27]Jianfu Zhang, Yan Hong, Dawei Cheng, Liqing Zhang, Qibin Zhao. Defending adversarial attacks in Graph Neural Networks via tensor enhancement. Pattern Recognition. Volume 158, February 2025, 110954. https://doi.org/10.1016/j.patcog.2024.110954
[28]Ryan Solgi a b, Zichang He a, William Jiahua Liang c, Zheng Zhang a, Hugo A. Loaiciga. Tensor shape search for efficient compression of tensorized data and neural networks. Applied Soft Computing.Volume 149, Part A, December 2023, 110987. https://doi.org/10.1016/j.asoc.2023.110987
[29]Betania Silva Carneiro Campello a, Leonardo Tomazeli Duarte b, João Marcos Travassos Romano. Exploiting temporal features in multicriteria decision analysis by means of a tensorial formulation of the TOPSIS method. Computers & Industrial Engineering. Volume 175, January 2023, 108915. https://doi.org/10.1016/j.cie.2022.108915
[30]Derong Xie, Sihao Chen, Haotong Duan, Xinwei Li, Caotong Luo, Yuxuan Ji, Huiming Duan. A novel grey prediction model based on tensor higher-order singular value decomposition and its application in short-term traffic flow. Engineering Applications of Artificial Intelligence. Volume 126, Part D, November 2023, 107068. https://doi.org/10.1016/j.engappai.2023.107068
[31]Padraig Corcoran, Rhyd Lewis. Optimisation of livestock routing on farms. Computers & Industrial Engineering. Volume 188, February 2024, 109882. https://doi.org/10.1016/j.cie.2024.109882
[32]Gerald C. Nwalozie a, André L.F. de Almeida b, Martin Haardt. Enhanced channel estimation for double RIS-aided MIMO systems using coupled tensor decompositions. Signal Processing. Volume 234, September 2025, 109979. https://doi.org/10.1016/j.sigpro.2025.109979
[33]Jinli Chen, Yijun Tang, Xicheng Zhu, Jiaqiang Li. Angle estimation based on Vandermonde constrained CP tensor decomposition for bistatic MIMO radar under spatially colored noise. Signal Processing. Volume 220, July 2024, 109429. https://doi.org/10.1016/j.sigpro.2024.109429
[34]Danilo S. Rocha, C. Alexandre R. Fernandes, Gérard Favier. Doubly coupled nested tensor decompositions with application to multirelay multicarrier MIMO communication networks. Digital Signal Processing. Volume 140, August 2023, 104143. https://doi.org/10.1016/j.dsp.2023.104143
[35]Jinfeng Huang, Feibin Zhang, Babak Safaei, Zhaoye Qin, Fulei Chu. The flexible tensor singular value decomposition and its applications in multisensor signal fusion processing. Mechanical Systems and Signal Processing. Volume 220, 1 November 2024, 111662. https://doi.org/10.1016/j.ymssp.2024.111662
[36]Bayram Ibrаhimov, Arif Hasanov, Elshan Hashimov. Research and analysis of efficiency indicators of critical infrastructures in the communication system. Advanced Information Systems. 2024. Vol. 8, No. 2. https://doi.org/10.20998/2522-9052.2024.2.07
[37]Fan, R.; Fan, Q.; Li, X.; Wang, P.; Xu, J.; Jin, X.; Yao, S.; Liu, P. A novel multi-modal incremental tensor decomposition for anomaly detection in large-scale networks. Information Sciences 681, 2024, 121210. DOI: 10.1016/j.ins.2024.121210.
[38]Laishui Lv, Kun Zhang, Ting Zhang, Xun Li, Qi Sun a, Lilinqing Zhang, Wei Xue. Eigenvector-based centralities for multilayer temporal networks under the framework of tensor computation Expert Systems with Applications. Volume 184, 1 December 2021, 115471. https://doi.org/10.1016/j.eswa.2021.115471
[39]Zhinong Li, Chenyu Liu, Wenjing Huang, Fengtao Wang, Wenxian Yang. Fault diagnosis method based on multimodal-deep tensor projection network under variable working conditions. Mechanical Systems and Signal Processing. Volume 225, 15 February 2025, 112336. https://doi.org/10.1016/j.ymssp.2025.112336

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