Article in Russian
For citation:
Filianin I.V., Kapitonov A.A., Martynyuk A.P. Generating spatiotemporal network load series in multi-access edge computing tasks using open data. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2026, vol. 26, no. 2, pp. 410–419 (in Russian). doi: 10.17586/2226-1494-2026-26-2-410-419
Abstract
Research into decision-making systems in multi-access edge computing systems is often based on an abstract representation of a communication network without network load profiles. The aim of this work was to develop tools for generating spatio-temporal network load data depending on the communication network architecture. In our work, we used stochastic geometry methods and statistical data to form a profile of possible load. To evaluate the performance of stochastic geometry methods, we developed a tool for generating and validating spatio-temporal series with pattern search from the OpenCellID open database of cell towers. During the work, an analysis of literature and public datasets on the location and load of cell towers was conducted. Based on the analysis, it was concluded that the data quality was low for the purposes of training decision-making systems for the placement of computing services in geographically distributed data processing nodes. A tool was also developed to generate and validate spatio-temporal series with pattern search from the OpenCellID open database of cell towers. A comparative analysis of the basic and calibrated Hard-Core Poisson Process algorithms showed significant differences in the characteristics of the generated distributions. For St. Petersburg, the calibrated model provided a 99-fold increase in station density and a 52-fold reduction in inter-station distances with an effective coverage area of 0.04 km2. In the case of Novosibirsk, similar trends were observed with less intensity: a 12.5-fold increase in density and a 21-fold reduction in distances with a coverage area of 0.32 km2. The use of spatio-temporal series obtained with the help of the developed generation tools will improve the quality of training decision-making systems for the placement of computing services through pre-training on data correlated with the actual location of cell towers. In addition, the generation tool allows you to specify the coordinates of the area of the proposed communication network which can also affect the distribution patterns of towers and which in turn will allow you to generate more accurate spatio-temporal series.
Keywords: multi-access edge computing, stochastic geometry, OpenCellID, spatiotemporal, Hard-Core Poisson Process
References
1. Al-Bahri M., Alkishri W., Ahmed F.Y.H., Alshar'e M., Maskari S.A. Enhancing IoT network security through digital object architecture-based approaches.
Qubahan Academic Journal, 2024, vol. 4, no. 1, pp. 224–239.
https://doi.org/10.48161/qaj.v4n1a413
2. Li N., Hao W., Zhou F., Chu Z., Yang S., Muta O., Gacanin H. Min–max latency optimization for IRS-aided cell-free mobile edge computing systems.
IEEE Internet of Things Journal, 2024, vol. 11, no. 5, pp. 8757–8770.
https://doi.org/10.1109/jiot.2023.3322751
3. Hua H., Li Y., Wang T., Dong N., Li W., Cao J. Edge computing with artificial intelligence: a machine learning perspective.
ACM Computing Surveys, 2023, vol. 55, no. 9, pp. 1–35.
https://doi.org/10.1145/3555802
4. Pandey C., Tiwari V., Rathore R.S., Jhaveri R.H., Roy D.S., Selvarajan S. Resource-efficient synthetic data generation for performance evaluation in mobile edge computing over 5G networks.
IEEE Open Journal of the Communications Society, 2023, vol. 4, pp. 1866–1878.
https://doi.org/10.1109/OJCOMS.2023.3306039
5. Ismail A.A., Khalifa N.E., El-Khoribi R.A. A survey on resource scheduling approaches in multi-access edge computing environment: a deep reinforcement learning study.
Cluster Computing, 2025, vol. 28, no. 3, pp. 184.
https://doi.org/10.1007/s10586-024-04893-7
6. Talpur A., Gurusamy M. DRLD-SP: A Deep-reinforcement-learning-based dynamic service placement in edge-enabled Internet of vehicles.
IEEE Internet of Things Journal, 2022, vol. 9, no. 8, pp. 6239–6251.
https://doi.org/10.1109/jiot.2021.3110913
7. Kabeer M., Nordin R., Behjati M., Shaharuddin F.Y.B.M. An urban multi-operator QoE-aware dataset for cellular networks in dense environments.
arXiv, 2025. arXiv:2506.22484.
https://doi.org/10.48550/arXiv.2506.22484
8. Luo F., Zheng S., Ding W., Fuentes J., Li Y. An edge server placement method based on reinforcement learning.
Entropy, 2022, vol. 24, no. 3, pp. 317.
https://doi.org/10.3390/e24030317
9. Pandey C., Tiwari V., Rodrigues J.J.P.C., Roy D.S. 5GT-GAN-NET: Internet traffic data forecasting with supervised loss based synthetic data over 5G.
IEEE Transactions on Mobile Computing, 2024, vol. 23, no. 11, pp. 10694–10705.
https://doi.org/10.1109/TMC.2024.3364655
12. Liu P., Lei J., Cao H., Garg S., Kaur K., Kaddoum G. A Stochastic geometry model and analysis scheme for SCMA aided mobile edge computing.
Proc. of the IEEE Wireless Communications and Networking Conference (WCNC), 2024, pp. 1-–6.
https://doi.org/10.1109/WCNC57260.2024.10570726
13. Gu Y., Yao Y., Li C., Xia B., Xu D., Zhang C. Modeling and analysis of stochastic mobile-edge computing wireless networks.
IEEE Internet of Things Journal, 2021, vol. 8, no. 18, pp. 14051–14065.
https://doi.org/10.1109/JIOT.2021.3068382
14. Facchini C., Holland O., Granelli F., da Fonseca N.L.S., Aghvami H.Dynamic green self-configuration of 3G base stations using fuzzy cognitive maps.
Computer Networks, 2013, vol. 57, no. 7, pp. 1597–1610.
https://doi.org/10.1016/j.comnet.2013.02.011
15. Bufetov A.I. Rigidity of determinantal point processes with the Airy, the Bessel and the Gamma kernel.
Bulletin of Mathematical Sciences, 2016, vol. 6, no. 1, pp. 163–172.
https://doi.org/10.1007/s13373-015-0080-z
16. Deng N., Zhou W., Haenggi M. The Ginibre point process as a model for wireless networks with repulsion.
IEEE Transactions on Wireless Communications, 2015, vol. 14, no. 1, pp. 107–121.
https://doi.org/10.1109/twc.2014.2332335
18. Illian J., Penttinen A., Stoyan H., Stoyan D. Statistical Analysis and Modelling of Spatial Point Patterns. Wiley-Interscience, 2008, 560 p.
19. Stoyan D., Kendall W.S., Mecke J. Stochastic Geometry and its Applications. John Wiley & Sons, 2013, 584 p.
20. ElSawy H., Hossain E., Haenggi M. Stochastic geometry for modeling, analysis, and design of multi-tier and cognitive cellular wireless networks: a survey.
IEEE Communications Surveys and Tutorials, 2013, vol. 15, no. 3, pp. 996–1019.
https://doi.org/10.1109/surv.2013.052213.00000
21. Ester M., Kriegel H.P., Sander J., Xu X. A density-based algorithm for discovering clusters. Proc. of the 2nd International Conference on Knowledge Discovery and Data Mining, 1996, pp. 226–231.
22. Bogatyrev V.A., Bogatyrev S.V., Bogatyrev A.V. Boundary estimation of the reliability of cluster systems based on the decomposition of the Markov model with limited recovery of nodes with accumulated failures.
Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2025, vol. 25, no. 3, pp. 574–583. (in Russian).
https://doi.org/10.17586/2226-1494-2025-25-3-574-583
23. Bogatyrev V.A., Bogatyrev S.V., Bogatyrev A.V. Assessment of the readiness of a computer system for timely servicing of requests when combined with information recovery of memory after failures.
Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 3, pp. 608–617. (in Russian).
https://doi.org/10.17586/2226-1494-2023-23-3-608-617
';
