kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2023-20-1-45-53kaz29-610Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEВСТРАИВАНИЕ МОБИЛЬНОСТИ ИЗ ЗАПИСИ ДАННЫХ О ВЫЗОВАХ С ИСПОЛЬЗОВАНИЕМ WORD2VEC ДЛЯ ПОДДЕРЖКИ СЕТИ С БЕСПИЛОТНЫМ ЛЕТАТЕЛЬНЫМ АППАРАТОМMOBILITY EMBEDDING FROM CALL DATA RECORD USING WORD2VEC TO SUPPORT NETWORK WITH UNMANNED AERIAL VEHICLEhttps://orcid.org/0000-0002-9119-6867СемасселИмед ЕддинеSemasselImed Eddine

Имед Еддине Семассел, докторант, Департамент компьютерных наук Тунисского факультета естественных наук

Imed Eddine Semassel, PhD student, Department of Computer Science, Faculty of Sciences of Tunis

imededdine.semassel@fst.utm.tnhttps://orcid.org/0000-0001-8939-8948Бен ЯхиаСадокBen YahiaSadok

Садок Бен Яхиа, профессор

г. Таллин

Sadok Ben Yahia, Professor

Tallinn

sadok.ben@taltech.eeУниверситет Эль-МанарТунисEl Manar UniversityTunisiaТаллинский университет технологийЭстонияTallinn Univeristy of TechnologyEstonia2023010420232014553Copyright © Семассел И.Е., Бен Яхиа С., 20232023Семассел И.Е., Бен Яхиа С.Semassel I.E., Ben Yahia S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/610

Записи сведений о вызовах (CDR) – это записи, содержащие информацию о телефонных разговорах и текстовых сообщениях. Некоторые исследования доказали, что данные CDR дают полезную информацию о моделях мобильности людей и связях с точными временными и географическими характеристиками. В данной статье предлагается встраивать трассировки, записанные в CDR, для извлечения значимой информации. Трассировки предоставляют информацию о местоположении, для которого может потребоваться поддержка для покрытия или восстановления сети. После внедрения траекторий пользователей мы используем обработанные результаты, для того чтобы рекомендовать антенны с координатами и запросом на поддержку, необходимые для парка беспилотных летательных аппаратов. В данной статье мы столкнулись с проблемой маршрутизации транспортных средств с вместимостью, которую мы решили с помощью программного обеспечения Google с открытым исходным кодом под названием OR-Tools.

Call Detail Records (CDRs) are records that provide information about phone conversations and text messages. CDR data has been proved in several studies to give useful information on people's mobility patterns and associations with fine-grained temporal and geographical characteristics. This paper proposes to embed the traces recorded in the CDRs to extract meaningful information. These latter provide insights about the location that may need support to cover or recover the network. After embedding the users' trajectories step, we use the embedding results to recommend the antennas with coordinates and support demand needed to a fleet of Unmanned Aerial Vehicle. Finally, we ended up with a capacitated vehicle routing problem that we solved using a Google open-source software named OR-Tools.

встраивание мобильностивстраивание WordWord2Vecданные CDRMobility embeddingWord embeddingWord2VecCDR dataReferencesAssociation C. T. (2017, July). How mobile phones are changing the developing world. Retrieved from https://www.cta.tech/News/Blog/Articles/2015/July/How-Mobile-Phones-Are-Changing-the-Developing-Worl.aspx.Association C. T. (2017, July). How mobile phones are changing the developing world. Retrieved from https://www.cta.tech/News/Blog/Articles/2015/July/How-Mobile-Phones-Are-Changing-the-Developing-Worl.aspx.Bianchi F. M., Scardapane, S., Uncini, A., Rizzi, A., & Sadeghian, A. (2015). Prediction of telephone calls load using Echo State Network with exogenous variables. Neural Networks, 71, 204–213. https://doi.org/https://doi.org/10.1016/j.neunet.2015.08.010.Bianchi F. M., Scardapane, S., Uncini, A., Rizzi, A., & Sadeghian, A. (2015). Prediction of telephone calls load using Echo State Network with exogenous variables. Neural Networks, 71, 204–213. https://doi.org/https://doi.org/10.1016/j.neunet.2015.08.010.Bradley P.S., Bennett K.P. & Demiriz A. (2000). Constrained k-means clustering. Microsoft Research, Redmond, 20(0), 0.Bradley P.S., Bennett K.P. & Demiriz A. (2000). Constrained k-means clustering. Microsoft Research, Redmond, 20(0), 0.Crivellari A. & Beinat E. (2019). From motion activity to geo-embeddings: Generating and exploring vector representations of locations, traces and visitors through large-scale mobility data. ISPRS International Journal of Geo-Information, 8(3), 134.Crivellari A. & Beinat E. (2019). From motion activity to geo-embeddings: Generating and exploring vector representations of locations, traces and visitors through large-scale mobility data. ISPRS International Journal of Geo-Information, 8(3), 134.Cuzzocrea A., Ferri F. & Grifoni P. (2018). Intelligent Sensor Data Fusion for Supporting Advanced Smart Health Processes. In L. Barolli & O. Terzo (Eds.), Complex, Intelligent, and Software Intensive Systems (Vol. 611, pp. 361–370). https://doi.org/10.1007/978-3-319-61566-0_33Cuzzocrea A., Ferri F. & Grifoni P. (2018). Intelligent Sensor Data Fusion for Supporting Advanced Smart Health Processes. In L. Barolli & O. Terzo (Eds.), Complex, Intelligent, and Software Intensive Systems (Vol. 611, pp. 361–370). https://doi.org/10.1007/978-3-319-61566-0_33OR-tools. Retrieved from https://developers.google.com/optimizationOR-tools. Retrieved from https://developers.google.com/optimizationGore R., Wozny P., Dignum F. P. M., Shults F. L. van Burken C. B. & Royakkers, L. (2019). A Value Sensitive ABM of the Refugee Crisis in the Netherlands. Proceeding 2019 Spring Simulation Conference (SpringSim), 1–12.Gore R., Wozny P., Dignum F. P. M., Shults F. L. van Burken C. B. & Royakkers, L. (2019). A Value Sensitive ABM of the Refugee Crisis in the Netherlands. Proceeding 2019 Spring Simulation Conference (SpringSim), 1–12.Louail T., Lenormand M., Ros O.G. C., Picornell M., Herranz R., Frias-Martinez E., … Barthelemy M. (2015). From mobile phone data to the spatial structure of cities. Scientific Reports, 4. https://doi.org/https://doi.org/10.1038/srep05276.Louail T., Lenormand M., Ros O.G. C., Picornell M., Herranz R., Frias-Martinez E., … Barthelemy M. (2015). From mobile phone data to the spatial structure of cities. Scientific Reports, 4. https://doi.org/https://doi.org/10.1038/srep05276.Mikolov T., Chen K., Corrado G. & Dean J. (2013). Efficient estimation of word representations in vector space. ArXiv Preprint ArXiv:1301.3781.Mikolov T., Chen K., Corrado G. & Dean J. (2013). Efficient estimation of word representations in vector space. ArXiv Preprint ArXiv:1301.3781.Mobile policy handbook: an insider’s guide to the issues. (2017). Retrieved from https://www.gsma.com/mena/wp-content/uploads/2018/10/Mobile_Policy_Handbook_2017_EN.pdfMobile policy handbook: an insider’s guide to the issues. (2017). Retrieved from https://www.gsma.com/mena/wp-content/uploads/2018/10/Mobile_Policy_Handbook_2017_EN.pdfSolomon A., Bar A., Yanai C., Shapira B. & Rokach, L. (2018). Predict demographic information using word2vec on spatial trajectories. Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, 331–339.Solomon A., Bar A., Yanai C., Shapira B. & Rokach, L. (2018). Predict demographic information using word2vec on spatial trajectories. Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, 331–339.Tian C., Zhang Y. & Weng Z. (2021). Learning Large-scale Location Embedding From Human Mobility Trajectories with Graphs. CoRR, abs/2103.00483. Retrieved from https://arxiv.org/abs/2103.00483Tian C., Zhang Y. & Weng Z. (2021). Learning Large-scale Location Embedding From Human Mobility Trajectories with Graphs. CoRR, abs/2103.00483. Retrieved from https://arxiv.org/abs/2103.00483Zhou N., Zhao W. X., Zhang X., Wen J.-R. & Wang S. (2016). A general multi-context embedding model for mining human trajectory data. IEEE Transactions on Knowledge and Data Engineering, 28(8), 1945–1958.Zhou N., Zhao W. X., Zhang X., Wen J.-R. & Wang S. (2016). A general multi-context embedding model for mining human trajectory data. IEEE Transactions on Knowledge and Data Engineering, 28(8), 1945–1958.Zhu M., Chen W., Xia J., Ma Y., Zhang Y., Luo Y. … Liu L. (2019). Location2vec: a situation-aware representation for visual exploration of urban locations. IEEE Transactions on Intelligent Transportation Systems, 20(10), 3981–3990.Zhu M., Chen W., Xia J., Ma Y., Zhang Y., Luo Y. … Liu L. (2019). Location2vec: a situation-aware representation for visual exploration of urban locations. IEEE Transactions on Intelligent Transportation Systems, 20(10), 3981–3990.

The authors declare that there are no conflicts of interest present.