{"id":4328,"date":"2025-09-10T21:55:27","date_gmt":"2025-09-10T20:55:27","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=4328"},"modified":"2026-06-04T13:16:22","modified_gmt":"2026-06-04T12:16:22","slug":"27-4-2-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-4-2-en\/","title":{"rendered":"27.4.2 En"},"content":{"rendered":"\n

Analysis of limited mobile networks and the potential of distributed mobile computing<\/strong><\/h2>\n\n\n\n

F.I. Isaev, G.N. Isaeva<\/strong><\/p>\n\n\n\n

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Abstract<\/strong>:<\/strong> <\/em>The relevance of this study stems from the exponential mobile growth and the fundamental limitations of modern mobile networks, which make traditional centralized approaches to information processing inefficient.
Aim<\/strong>. The paper analyses the disadvantages of modern mobile networks when transmitting large-scale data and an assessment for possible compensation of limitations through distributed mobile computing, including a study of the impact of thermal throttling on smartphone performance under long-term computing load.
Methods<\/strong>. Experimental performance analysis for data transfer rates, synthetic performance tests, stress tests for throttling analysis, and a comparative analysis of mobile and stationary systems are used.
Results<\/strong>. A comprehensive real performance analysis disclosed key challenges in 4G, 3G, and 2G networks such as: connection instability (speed fluctuations in 4G from 10\u201315 Mbit\/s with dips up to 5 Mbit\/s), high transmission delays, and a catastrophic drop in throughput in previous-generation networks. Experimental data demonstrate that transferring a 2 GB file under optimal 4G conditions takes 20 minutes, while in 3G this figure reaches 2 hours, and in 2G it becomes a virtually impossible task. As an alternative approach, we explore distributed mobile computing for the devices. Comparative performance tests between flagship smartphones (Samsung Galaxy S24) and stationary systems revealed that with single-threaded computing the gap is only 15 %, but the key limiting factor is thermal throttling, which reduces the device’s performance by 45\u201350% after 12\u201318 minutes of intensive load.
Conclusions<\/strong>. The obtained results confirm the theoretical possibility of creating efficient distributed mobile computing systems based on clusters, but point to the necessity for advanced load balancing algorithm and thermal management determining the potential avenues for future study.<\/p>\n\n\n\n

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Keywords<\/strong>:<\/em><\/strong> mobile networks, distributed computing, bandwidth, throttling, smartphone performance, data processing<\/p>\n\n\n\n

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For citation<\/strong>.<\/strong> Isaev F.I., Isaeva G.N. Analysis of limited mobile networks and the potential of distributed mobile computing. News of the Kabardino-Balkarian Scientific Center of RAS. 2025. Vol. 27. No. 4. Pp. 24\u201334. DOI: 10.35330\/1991-6639-2025-27-4-24-34<\/p>\n\n\n\n

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R<\/strong>eferences<\/summary>\n
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  5. Liu S., Karanth A. Dynamic voltage and frequency scaling to improve energy-efficiency of hardware accelerators. 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC). 2021. Pp. 6\u20139.<\/li>\n\n\n\n
  6. Kurhade A.S., Siraskar G.D., Enhancing smartphone circuit cooling: a computational study of pcm integration. Journal of Advanced Research in Numerical Heat Transfer. 2024. Vol. 27. No. 1. Pp. 132\u2013145.<\/li>\n\n\n\n
  7. Benoit-Cattin T., Velasco-Montero D., Fern\u00e1ndez-Berni J. Impact of thermal throttling on long-term visual inference in a CPU-based edge device. Electronics. 2020. Vol. 9. No. 12. P. 6.<\/li>\n\n\n\n
  8. Bantock J.R.B., Al-Hashimi B.M., Merrett G.V. Mitigating interactive performance degradation from mobile device thermal throttling. IEEE Embedded Systems Letters. 2021. Vol. 13. No. 1. Pp. 1\u20134.<\/li>\n\n\n\n
  9. Salem H. Distributed computing system on a smartphones-based network. Software Technology: Methods and Tools: 51st International Conference. Springer International Publishing, Pp. 313\u2013325.<\/li>\n<\/ol>\n<\/details>\n\n\n\n
    Information about the authors<\/strong><\/summary>\n
    \n

    Fedor I. Isaev<\/strong>, Postgraduate Student, National Research Nuclear University MEPhI (Moscow Engineering Physics Institute);
    115409, Russia, Moscow, 31 Kashirskoe shosse;
    alekseylent@bk.ru, ORCID: https:\/\/orcid.org\/0009-0007-7308-1381, SPIN-code: 3403-5209
    Galina N. Isaeva<\/strong>, Candidate of Technical Sciences, Associate Professor, National Research Nuclear University MEPhI (Moscow Engineering Physics Institute);
    115409, Russia, Moscow, 31 Kashirskoe shosse;
    ORCID: https:\/\/orcid.org\/0009-0008-0371-5972, SPIN-code: 7809-5680<\/p>\n\n\n\n

    <\/p>\n<\/div>\n<\/details>\n","protected":false},"excerpt":{"rendered":"

    Analysis of limited mobile networks and the potential of distributed mobile computing F.I. Isaev, G.N. Isaeva Abstract: The relevance of this study stems from the exponential mobile growth and the fundamental limitations of modern mobile networks, which make traditional centralized approaches to information processing inefficient.Aim. 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