{"id":2275,"date":"2025-05-13T19:29:53","date_gmt":"2025-05-13T18:29:53","guid":{"rendered":"http:\/\/newskbncran.ru\/?page_id=2275"},"modified":"2026-03-27T10:35:11","modified_gmt":"2026-03-27T10:35:11","slug":"26-6-10-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/26-6-10-en\/","title":{"rendered":"26.6.10 en"},"content":{"rendered":"\n

Intelligent system to analyse distributed geophysical data for a network of test ranges with multiple landscapes<\/strong><\/h1>\n\n\n\n

M.A. Abazokov, K.Ch. Bzhikhatlov<\/strong><\/strong><\/strong><\/strong><\/p>\n\n\n\n

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Abstract.<\/em><\/strong> The article presents a system for collecting and analysing distributed geophysical data from a sensor network of test ranges with multiple landscapes. The paper presents an architecture and features of a software for collecting data from sensors and for their intelligent analysis, taking into account the lack of stable access to the Internet on the territory of the test ranges. The article shows principles of intelligent system analysis of sensor data designed to use various approaches to create artificial intelligence systems. Moreover, the process of data exchanging with programs to model intelligent systems based on multi-agent neurocognitive architectures is described. In addition, the structure of a website and database of a service for collecting and processing data are presented.<\/p>\n\n\n\n

Keywords<\/em><\/strong>:<\/em> distributed sensor network, intelligent data processing, multi-agent systems, neurocognitive architectures, test ranges with multiple landscapes, data forecasting, wireless sensor networks, geophysical data<\/p>\n\n\n\n

For citation<\/strong>. <\/strong>Abazokov M.A., Bzhikhatlov K.Ch. Intelligent system to analyse distributed geophysical data for a network of test ranges with multiple landscapes. News of the Kabardino-Balkarian Scientific Center of RAS.<\/em> 2024. Vol. 26. No. 6. Pp. 129\u2013138. DOI: 10.35330\/1991-6639-2024-26-6-129-138<\/p>\n\n\n\n

R<\/strong>eferences<\/summary>\n
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  2. Ksalov A.M., Bzhikhatlov K.Ch., Pshenokova I.A., Zammoev A.U. Development of a transport subsystem for autonomous robots for plant protection system. News of the Kabardino-Balkarian Scientific Center of RAS. 2022. No. 2(106). Pp. 31\u201340. DOI: 10.35330\/1991-6639-2022-2-106-31-40. (In Russian)<\/li>\n\n\n\n
  3. Tu J. Application of wireless sensor network model based on big data ecosystem in intelligent health monitoring system. Journal of Function Spaces. 2022. Vol. 2022. Pp. 1\u201310. DOI: 10.1155\/2022\/3179915<\/li>\n\n\n\n
  4. Zhang Y., Huang W. Design of intelligent diagnosis system for teaching quality based on wireless sensor network and data mining. Eurasip Journal on Wireless Communications and Networking. 2021. Vol. 2021. No. 1. DOI: 10.1186\/s13638-021-01902-w<\/li>\n\n\n\n
  5. LI F., Valero M., Cheng Y. et al. Distributed sensor networks based shallow subsurface imaging and infrastructure monitoring. IEEE Transactions on Signal and Information Processing over Networks. 2020. Vol. 6. Pp. 241\u2013250. DOI: 10.1109\/tsipn.2020.2975349<\/li>\n\n\n\n
  6. Ge X., Han Q., Zhang X. et al. Distributed event-triggered estimation over sensor networks: A survey. IEEE Transactions on Cybernetics. 2020. Vol. 50. No. 3. Pp. 1306\u20131320. DOI: 10.1109\/tcyb.2019.2917179<\/li>\n\n\n\n
  7. Hou C., Zhao Q., Basar T. Optimization of web service-based data-collection system with smart sensor nodes for balance between network traffic and sensing accuracy. IEEE Transactions on Automation Science and Engineering. 2021. Vol. 18. No. 4. Pp. 2022\u20132034. DOI: 10.1109\/tase.2020.3030835<\/li>\n\n\n\n
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  10. Seng K.P., Ang L., Ngharamike E. Artificial intelligence internet of things: A new paradigm of distributed sensor networks. International Journal of Distributed Sensor Networks. Vol. 18. No. 3. P. 155014772110628. DOI: 10.1177\/15501477211062835<\/li>\n\n\n\n
  11. Nagoev Z.V., Pshenokova I.A., Nagoeva O.V., Sundukov Z.A. Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures. Cognitive Systems Research. 2021. Vol. 66. Pp. 82\u201388. DOI: 10.1016\/j.cogsys.2020.10.015<\/li>\n\n\n\n
  12. Nagoev Z.V., Bzhikhatlov K.Ch., Pshenokova I.A., Unagasov A.A. Algorithms and software for simulation of intelligent systems of autonomous robots based on multi-agent neurocognitive architectures. Interactive Collaborative Robotics. Lecture Notes in Computer Science. 2024. Vol. 14898. Springer, Cham. DOI: 10.1007\/978-3-031-71360-6_29<\/li>\n<\/ol>\n<\/details>\n\n\n\n
    Information about the author<\/strong><\/summary>\n
    \n

    Mukhamed A. Abazokov<\/strong>, Junior Researcher, Laboratory of Neurocognitive Autonomous Intelligent
    Systems, Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
    360010, Russia, Nalchik, 2 Balkarov street;
    Chief Researcher of Laboratory 57, V.A. Trapeznikov Institute of Control Sciences of the Russian
    Academy of Sciences;
    117997, Russia, Moscow, 65 Profsoyuznaya street;
    abazokov1997@mail.ru, ORCID: https:\/\/orcid.org\/0000-0002-8710-1562, SPIN-code: 5167-5962
    Kantemir Ch. Bzhikhatlov<\/strong>, Candidate of Physical and Mathematical Sciences, Head of the
    Laboratory of Neurocognitive Autonomous Intelligent Systems, Kabardino-Balkarian Scientific
    Center of the Russian Academy of Sciences;
    360010, Russia, Nalchik, 2 Balkarov street;
    haosit13@mail.ru, ORCID: https:\/\/orcid.org\/0000-0003-0924-0193, SPIN-code: 9551-5494<\/p>\n\n\n\n

    <\/p>\n<\/div>\n<\/details>\n\n\n\n

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    Intelligent system to analyse distributed geophysical data for a network of test ranges with multiple landscapes M.A. Abazokov, K.Ch. Bzhikhatlov Upload the full text Abstract. The article presents a system for collecting and analysing distributed geophysical data from a sensor network of test ranges with multiple landscapes. The paper presents an architecture and features of […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-2275","page","type-page","status-publish","hentry"],"yoast_head":"\n26.6.10 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/26-6-10-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"26.6.10 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Intelligent system to analyse distributed geophysical data for a network of test ranges with multiple landscapes M.A. Abazokov, K.Ch. 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