Devising a method for energy-efficient control over a data transmission process across the mobile high-density internet of things

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.336111

Keywords:

Internet of Things transactions, energy resource, fog gateway, Pareto-optimal solution, boundary computations

Abstract

This study’s object is the process that controls data transmission across the mobile high-density Internet of Things. The task addressed is to reduce energy consumption when transmitting mobile IoT transactions to fog gateways was by devising a method for energy-efficient data transmission control.

To this end, it was proposed to optimize the distribution of active mobile devices across the fog layer gateways. In the process of research, the architecture of the data transmission subsystem between the boundary and fog layers of the Internet of Things was formed. During the development, an intermediate level of support infrastructure was selected – Communication Layer. That has made it possible to build a mathematical model of the data transmission process control process. The main difference of this model from existing ones is a significant acceleration of calculations when finding a Pareto-optimal solution. To this end, the method of successive concessions was used. It has made it possible to solve a three-criteria optimization problem with objective functions ordered by significance.

The mathematical model has made it possible to devise a method for energy-efficient control over the data transmission process across the mobile high-density Internet of Things. The main difference of this method from existing ones is the optimization of the process simultaneously according to three criteria: energy efficiency, priority, and time. In this case, preference is given to the criterion of energy efficiency of data transmission by mobile IoT devices. That has made it possible to significantly reduce the time of searching for a Pareto-optimal solution when transmitting transactions to a cloud data processing center.

The research results are attributed to the application of the successive concessions method together with the ant colony algorithm with a limited number of iterations. The method proves effective when concessions on the energy resource of mobile devices are from 5 to 15%.

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, National Scientific Center "Hon. Prof. M.S. Bokarius Forensic Science Institute"of the Ministry of Justice of Ukraine

Doctor of Technical Sciences, Senior Researcher

Director

Nina Kuchuk, National Technical University "Kharkiv Polytechnic Institute"

Doctor of Technical Sciences, Professor

Department of Computer Engineering and Programming

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

PhD

Yurii Onishchenko, Kharkiv National University of Internal Affairs

PhD, Associate Professor

Department of Cyber Security and DATA Technologies

Yurii Gnusov, Kharkiv National University of Internal Affairs

PhD, Associate Professor

Department of Cyber Security and DATA Technologies

Olha Brendel, National Scientific Center "Hon. Prof. M.S. Bokarius Forensic Science Institute"of the Ministry of Justice of Ukraine

PhD

Computer Technical Research, Telecommunications Research, Video and Audio Analysis Sector of the Engineering and Technical Research Laboratory

Viktoriia Roh, Kharkiv National University of Internal Affairs

Senior Lecturer

Department of Information Systems and Technologies

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. Vaiyapuri, T., Parvathy, V. S., Manikandan, V., Krishnaraj, N., Gupta, D., Shankar, K. (2021). A Novel Hybrid Optimization for Cluster‐Based Routing Protocol in Information-Centric Wireless Sensor Networks for IoT Based Mobile Edge Computing. Wireless Personal Communications, 127 (1), 39–62. https://doi.org/10.1007/s11277-021-08088-w
  3. 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
  4. Alqasimi, A., Al Marzouqi, K., Alhammadi, A., Aljasmi, A., Alnabulsi, A., Al-Ali, A. R. (2025). An IoT-Based Mobile Air Pollution Monitoring System. Proceedings of IEMTRONICS 2024, 221–233. https://doi.org/10.1007/978-981-97-4784-9_16
  5. 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
  6. Tzeng, S.-S., Lin, Y.-J., Wang, S.-W. (2025). Age of Information in IoT Devices With Integrated Heterogeneous Sensors Under Slotted ALOHA. IEEE Sensors Journal, 25 (11), 20842–20853. https://doi.org/10.1109/jsen.2025.3563452
  7. Sobchuk, V., Pykhnivskyi, R., Barabash, O., Korotin, S., Omarov, S. (2024). Sequential intrusion detection system for zero-trust cyber defense of IOT/IIOT networks. Advanced Information Systems, 8 (3), 92–99. https://doi.org/10.20998/2522-9052.2024.3.11
  8. Cui, Y., Shi, G., Xu, L., Ji, J. (2023). Average dwell time based networked predictive control for switched linear systems with data transmission time-varying delays. IMA Journal of Mathematical Control and Information, 40 (2), 210–231. https://doi.org/10.1093/imamci/dnad007
  9. 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
  10. Kuchuk, H., Mozhaiev, O., Tiulieniev, S., Mozhaiev, M., Kuchuk, N., Tymoshchyk, L. et al. (2025). Devising a method for stabilizing control over a load on a cluster gateway in the internet of things edge layer. Eastern-European Journal of Enterprise Technologies, 2 (9 (134)), 24–32. https://doi.org/10.15587/1729-4061.2025.326040
  11. 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
  12. 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
  13. 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
  14. Kuchuk, H., Mozhaiev, O., Tiulieniev, S., Mozhaiev, M., Kuchuk, N., Tymoshchyk, L. et al. (2025). Devising a method for forming a stable mobile cluster of the internet of things fog layer. Eastern-European Journal of Enterprise Technologies, 1 (4 (133)), 6–14. https://doi.org/10.15587/1729-4061.2025.322263
  15. 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
  16. Yu, J., Yu, G., Chen, Z. (2024). RAllo: Region Attention-based Edge Resource Allocation in Mobile Internet of Things. GLOBECOM 2024 - 2024 IEEE Global Communications Conference, 3413–3418. https://doi.org/10.1109/globecom52923.2024.10901347
  17. Zheng, Z., Nazif, H. (2023). An Energy-aware Technique for Resource Allocation in Mobile Internet of Thing (MIoT) Using Selfish Node Ranking and an Optimization Algorithm. IETE Journal of Research, 70 (4), 3546–3571. https://doi.org/10.1080/03772063.2023.2202163
  18. Zheng, K., Luo, R., Liu, X., Qiu, J., Liu, J. (2024). Distributed DDPG-Based Resource Allocation for Age of Information Minimization in Mobile Wireless-Powered Internet of Things. IEEE Internet of Things Journal, 11 (17), 29102–29115. https://doi.org/10.1109/jiot.2024.3406044
  19. Liu, J., Wei, X., Fan, J. (2019). Tolerable Data Transmission of Mobile Edge Computing Under Internet of Things. IEEE Access, 7, 71859–71871. https://doi.org/10.1109/access.2019.2920442
  20. 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
  21. Kang, S., Li, K., Wang, R. (2024). A survey on pareto front learning for multi-objective optimization. Journal of Membrane Computing, 7 (2), 128–134. https://doi.org/10.1007/s41965-024-00170-z
  22. Hu, Y., Qu, Y., Li, W., Huang, Y. (2025). A Pareto Front searching algorithm based on reinforcement learning for constrained multiobjective optimization. Information Sciences, 705, 121985. https://doi.org/10.1016/j.ins.2025.121985
  23. Pardalos, P. M., Steponavičė, I., Z̆ilinskas, A. (2011). Pareto set approximation by the method of adjustable weights and successive lexicographic goal programming. Optimization Letters, 6 (4), 665–678. https://doi.org/10.1007/s11590-011-0291-5
  24. Śliwiński, T. (2024). Efficient Approximation Methods for Lexicographic Max-Min Optimization. Journal of Telecommunications and Information Technology, 1, 46–53. https://doi.org/10.26636/jtit.2024.1.1421
  25. Zhang, J., Xu, M., Wang, L. (2025). Research on Link Selection and Allocation for IoT Localization Systems Based on an Improved Ant Colony Algorithm. Cyber Security Intelligence and Analytics, 140–150. https://doi.org/10.1007/978-3-031-88287-6_13
  26. Zhang, N., Shang, F., Li, X., Zhu, W. (2022). Research on Test Data Generation Method of IOT Management Platform Based on Ant Colony Algorithm. 2022 11th International Conference of Information and Communication Technology (ICTech)), 175–178. https://doi.org/10.1109/ictech55460.2022.00042
  27. Zhao, H.-Y., Wang, J.-C., Guan, X., Wang, Z.-H., He, Y.-H., Xie, H.-L. (2019). Ant Colony System for Energy Consumption Optimization in Mobile IoT Networks. Journal of Circuits, Systems and Computers, 29 (09), 2050150. https://doi.org/10.1142/s0218126620501509
  28. Petrovska, I., Kuchuk, H., Kuchuk, N., Mozhaiev, O., Pochebut, M., Onishchenko, Y. (2023). Sequential Series-Based Prediction Model in Adaptive Cloud Resource Allocation for Data Processing and Security. 2023 13th International Conference on Dependable Systems, Services and Technologies (DESSERT), 1–6. https://doi.org/10.1109/dessert61349.2023.10416496
  29. 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
  30. Kuchuk, H., Mozhaiev, O., Tiulieniev, S., Mozhaiev, M., Kuchuk, N., Tymoshchyk, L. et al. (2025). Devising a method for increasing data transmission speed in monitoring systems based on the mobile high-density internet of things. Eastern-European Journal of Enterprise Technologies, 3 (4 (135)), 52–61. https://doi.org/10.15587/1729-4061.2025.330644
  31. 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
  32. Datsenko, S., Kuchuk, H. (2023). Biometric authentication utilizing convolutional neural networks. Advanced Information Systems, 7 (2), 87–91. https://doi.org/10.20998/2522-9052.2023.2.12
  33. Singh, S. P., Singh, P., Diwakar, M., Kumar, P. (2024). Improving quality of service for Internet of Things(IoT) in real life application: A novel adaptation based Hybrid Evolutionary Algorithm. Internet of Things, 27, 101323. https://doi.org/10.1016/j.iot.2024.101323
  34. Zhou, Y., Liu, X., Hu, S., Wang, Y., Yin, M. (2022). Combining max–min ant system with effective local search for solving the maximum set k-covering problem. Knowledge-Based Systems, 239, 108000. https://doi.org/10.1016/j.knosys.2021.108000
Devising a method for energy-efficient control over a data transmission process across the mobile high-density internet of things

Downloads

Published

2025-08-30

How to Cite

Kuchuk, H., Mozhaiev, O., Tiulieniev, S., Mozhaiev, M., Kuchuk, N., Lubentsov, A., Onishchenko, Y., Gnusov, Y., Brendel, O., & Roh, V. (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

Issue

Section

Mathematics and Cybernetics - applied aspects