Devising a method for managing computing resources in a fog layer of the mobile high-density internet of things

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.344553

Keywords:

mobile device, gateway, simulated annealing algorithm, genetic algorithm, IoT ecosystem

Abstract

This study considers a process that manages the distribution of computing resources in the fog layer of the mobile high-density Internet of Things. The task addressed is to reduce the load imbalance of fog servers by devising a method for controlling computing resources in the fog layer when processing information flows.

Information flows are formed by intelligent gateways of the mobile high-density Internet of Things, which receive data from the boundary layer. In the process of research, a mathematical model for the process of controlling computing resources in the fog layer was built. Its main difference from existing ones is a module hierarchical structure according to the basic levels of decision-making when managing computing resources.

When constructing the model, the principle of process decomposition into adjacent time intervals was used. Its application made it possible to carry out local optimization of the process of managing computing resources in short time intervals. The mathematical model has made it possible to devise a method for controlling computing resources in the fog layer.

The main difference of this method from existing ones is that the process optimization is carried out according to the area of the relative deviation from the balanced load in the time interval under study. In addition, a two-stage algorithm for distributing tasks of free fog devices across fog layer servers is also used. That made it possible to reduce the time for finding an approximate solution for distributing computing resources of fog servers by up to 50%.

The research results can be attributed to the combined use of the simulated annealing algorithm and the genetic algorithm. The method is effective when the load on the fog layer is from 20% to 70% of the maximum possible load

Author Biographies

Heorhii Kuchuk, National Technical University "Kharkiv Polytechnic Institute"

Doctor of Technical Sciences, Professor

Department of Computer Engineering and Programming

Oleksandr Mozhaiev, Kharkiv National University of Internal Affairs

Doctor of Technical Sciences, Professor

Department of Cyber Security and DATA Technologies

Serhii Tiulieniev, Scientific Research Center for Forensic Expertise in the Field of Information Technologies and Intellectual Property of the Ministry of Justice of Ukraine

PhD

Director

Mykhailo Mozhaiev, Dnipropetrovsk Research Institute of Forensic Examinations of the Ministry of Justice of Ukraine

Doctor of Technical Sciences, Senior Researcher, Acting Director

Nina Kuchuk, National Technical University "Kharkiv Polytechnic Institute"

Doctor of Technical Sciences, Professor

Department of Computer Engineering and Programming

Pavlo Khorobrykh, Dnipropetrovsk Research Institute of Forensic Examinations of the Ministry of Justice of Ukraine

PhD, Acting First Deputy Director

Yurii Gnusov, Kharkiv National University of Internal Affairs

PhD, Associate Professor

Department of Cyber Security and DATA Technologies

Yurii Horelov, Kharkiv National University of Internal Affairs

PhD, Associate Professor

Department of Cyber Security and DATA Technologies

Vitalii Svitlychnyi, Kharkiv National University of Internal Affairs

PhD, Associate Professor

Department of Information Systems and Technologies

Oleksandr Bilyk, Kharkiv National University of Radio Electronics

PhD Student

V.V. Popovskyy Department of Infocommunication Engineering

References

  1. Kaur, G., Balyan, V., Gupta, S. H. (2025). Nature inspired optimization of IoT network for delay resistant and energy efficient applications. Scientific Reports, 15 (1). https://doi.org/10.1038/s41598-025-95138-z
  2. Kuchuk, H., Malokhvii, E. (2024). Integration of IoT with cloud, fog, and edge computing: a review. Advanced Information Systems, 8 (2), 65–78. https://doi.org/10.20998/2522-9052.2024.2.08
  3. Zhurylo, O., Liashenko, O. (2024). Architecture and iot security systems based on fog computing. Innovative technologies and scientific solutions for industries, 1 (27), 54–66. https://doi.org/10.30837/itssi.2024.27.054
  4. Kuchuk, H., Chumachenko, I., Marchenko, N., Kuchuk, N., Lysytsia, D. (2025). Method for calculating the number of IoT sensors in environmental monitoring systems. Advanced Information Systems, 9 (3), 66–72. https://doi.org/10.20998/2522-9052.2025.3.08
  5. Bhajantri, L. B., Gangadharaiah, S. (2022). Heuristic-Based Resource Allocation for Internet of Things in Gateway Centric Multi-layer Fog Computing. ICT Systems and Sustainability, 567–579. https://doi.org/10.1007/978-981-19-5221-0_54
  6. Liu, Q., Mo, R., Xu, X., Ma, X. (2020). Multi-objective resource allocation in mobile edge computing using PAES for Internet of Things. Wireless Networks, 30 (5), 3533–3545. https://doi.org/10.1007/s11276-020-02409-w
  7. Yu, J., Hou, K., Zhang, H., Kostic, B., Yang, M., Nazif, H. (2025). A new energy-aware resources scheduling method for mobile internet of things using a hybrid optimisation algorithm. International Journal of Mobile Communications, 25 (2), 176–207. https://doi.org/10.1504/ijmc.2025.144192
  8. Kuchuk, N., Kashkevich, S., Radchenko, V., Andrusenko, Y., Kuchuk, H. (2024). Applying edge computing in the execution IoT operative transactions. Advanced Information Systems, 8 (4), 49–59. https://doi.org/10.20998/2522-9052.2024.4.07
  9. Muñoz, L. A., Berná Martínez, J. V., Asensi, C. C., Pastor, D. S. (2024). RESEARCH NOTES: Design of a Distributed and Highly Scalable Fog Architecture for Heterogeneous IoT Infrastructures. International Journal of Software Engineering and Knowledge Engineering, 35 (02), 195–215. https://doi.org/10.1142/s0218194025430016
  10. Lea, R., Adame, T., Berne, A., Azaiez, S. (2025). The Internet of Things, Fog, and Cloud Continuum: Integration Challenges and Opportunities for Smart Cities. Future Internet, 17 (7), 281. https://doi.org/10.3390/fi17070281
  11. Zhurylo, O., Liashenko, O., Avetisova, K. (2023). Hardware security overview of fog computing end devices in the internet of things. Innovative Technologies and Scientific Solutions for Industries, 1 (23), 57–71. https://doi.org/10.30837/itssi.2023.23.057
  12. Kuchuk, H., Kalinin, Y., Dotsenko, N., Chumachenko, I., Pakhomov, Y. (2024). Decomposition of integrated high-density IoT data flow. Advanced Information Systems, 8 (3), 77–84. https://doi.org/10.20998/2522-9052.2024.3.09
  13. Rezanov, B., Kuchuk, H. (2023). Model of elemental data flow distribution in the internet of things supporting fog platform. Innovative technologies and scientific solutions for industries, 3 (25), 88–97. https://doi.org/10.30837/itssi.2023.25.088
  14. Kuchuk, H., Mozhaiev, O., Kuchuk, N., Tiulieniev, S., Mozhaiev, M., Gnusov, Y. et al. (2024). Devising a method for the virtual clustering of the Internet of Things edge environment. Eastern-European Journal of Enterprise Technologies, 1 (9 (127)), 60–71. https://doi.org/10.15587/1729-4061.2024.298431
  15. Lee, B. M. (2025). Efficient Resource Management for Massive MIMO in High-Density Massive IoT Networks. IEEE Transactions on Mobile Computing, 24 (3), 1963–1980. https://doi.org/10.1109/tmc.2024.3486712
  16. Kuchuk, H., Mozhaiev, O., Tiulieniev, S., Mozhaiev, M., Kuchuk, N., Lubentsov, A. et al. (2025). Devising a method for energy-efficient control over a data transmission process across the mobile high-density internet of things. Eastern-European Journal of Enterprise Technologies, 4 (4 (136)), 46–57. https://doi.org/10.15587/1729-4061.2025.336111
  17. Sandanasamy, A., Charles, P. J. (2025). Dynamic load balancing through TOPSIS based optimal server selection and resource allocation in SDN IoT network. OPSEARCH. https://doi.org/10.1007/s12597-025-00985-z
  18. Shamsa, Z., Rezaee, A., Adabi, S., Rahimabadi, A. M., Rahmani, A. M. (2024). A distributed load balancing method for IoT/Fog/Cloud environments with volatile resource support. Cluster Computing, 27 (4), 4281–4320. https://doi.org/10.1007/s10586-024-04403-9
  19. Shuaib, M., Bhatia, S., Alam, S., Masih, R. K., Alqahtani, N., Basheer, S., Alam, M. S. (2023). An Optimized, Dynamic, and Efficient Load-Balancing Framework for Resource Management in the Internet of Things (IoT) Environment. Electronics, 12 (5), 1104. https://doi.org/10.3390/electronics12051104
  20. Kuchuk, H., Husieva, Y., Novoselov, S., Lysytsia, D., Krykhovetskyi, H. (2025). Load balancing of the layers IoT fog-cloud support network. Advanced Information Systems, 9 (1), 91–98. https://doi.org/10.20998/2522-9052.2025.1.11
  21. Kanbar, A. B., Faraj, K. (2022). Region aware dynamic task scheduling and resource virtualization for load balancing in IoT–fog multi-cloud environment. Future Generation Computer Systems, 137, 70–86. https://doi.org/10.1016/j.future.2022.06.005
  22. Chiang, W.-K., Chih, Y.-H. (2025). A pareto-optimal heuristic algorithm to refactor IMS core for cloud-native NFV. Cluster Computing, 28 (8). https://doi.org/10.1007/s10586-025-05177-4
  23. Ritter, F. (2023). Technical note: A procedure to clean, decompose, and aggregate time series. Hydrology and Earth System Sciences, 27 (2), 349–361. https://doi.org/10.5194/hess-27-349-2023
  24. Hu, C., Sun, Z., Li, C., Zhang, Y., Xing, C. (2023). Survey of Time Series Data Generation in IoT. Sensors, 23 (15), 6976. https://doi.org/10.3390/s23156976
  25. Sajjad, F., Rashid, M., Zafar, A., Zafar, K., Fida, B., Arshad, A. et al. (2023). An efficient hybrid approach for optimization using simulated annealing and grasshopper algorithm for IoT applications. Discover Internet of Things, 3 (1). https://doi.org/10.1007/s43926-023-00036-3
  26. Seyedi, B., Postolache, O. (2025). Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method. Sensors, 25 (13), 4098. https://doi.org/10.3390/s25134098
  27. Gupta, N., Sharma, V. (2023). Context Aware Hybrid Network Architecture for Iot with Machine Learning Based Intelligent Gateway. SN Computer Science, 4 (3). https://doi.org/10.1007/s42979-023-01736-x
Devising a method for managing computing resources in a fog layer of the mobile high-density internet of things

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Published

2025-12-17

How to Cite

Kuchuk, H., Mozhaiev, O., Tiulieniev, S., Mozhaiev, M., Kuchuk, N., Khorobrykh, P., Gnusov, Y., Horelov, Y., Svitlychnyi, V., & Bilyk, O. (2025). Devising a method for managing computing resources in a fog layer of the mobile high-density internet of things. Eastern-European Journal of Enterprise Technologies, 6(4 (138), 15–25. https://doi.org/10.15587/1729-4061.2025.344553

Issue

Vol. 6 No. 4 (138) (2025): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

License

Copyright (c) 2025 Heorhii Kuchuk, Oleksandr Mozhaiev, Serhii Tiulieniev, Mykhailo Mozhaiev, Nina Kuchuk, Pavlo Khorobrykh, Yurii Gnusov, Yurii Horelov, Vitalii Svitlychnyi, Oleksandr Bilyk

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