Data Control in Distributed Self-organizing Sensor Network Under Speciffic Deployment Condition

A wireless sensor mesh network can be deployed in special conditions where stable GSM, Wi-Fi, and other coverage are absent. At the same time, there is also a risk of malicious interference with the transmitted information. A popular cognitive radio (CR) communication device with spread spectrum tec...

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Главные авторы:	Lapina, M. A., Лапина, М. А., Lapin, V. G., Лапин, В. Г.
Формат:	Статья
Язык:	English
Опубликовано:	Springer Science and Business Media Deutschland GmbH 2024
Темы:	Self – similarity Wireless sensor networks Self-organizing network Sensor nodes
Online-ссылка:	https://dspace.ncfu.ru/handle/123456789/29183
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id	ir-123456789-29183
record_format	dspace
spelling	ir-123456789-291832024-10-31T10:05:39Z Data Control in Distributed Self-organizing Sensor Network Under Speciffic Deployment Condition Lapina, M. A. Лапина, М. А. Lapin, V. G. Лапин, В. Г. Self – similarity Wireless sensor networks Self-organizing network Sensor nodes A wireless sensor mesh network can be deployed in special conditions where stable GSM, Wi-Fi, and other coverage are absent. At the same time, there is also a risk of malicious interference with the transmitted information. A popular cognitive radio (CR) communication device with spread spectrum technology is also susceptible to radio jamming. Recent practical knowledge regarding radio jamming, radio reconnaissance, and electronic warfare indicates that radio jamming is effective but has certain limitations in terms of working distance and energy consumption. A foundational practical test under conditions approximating special deployment circumstances revealed that typical radio jamming schemes operate at distances up to 1–1.3 km when applied to cognitive radio (CR) devices. These data, overall, are corroborated by the practical application of networks of this kind in specialized deployment conditions. Additionally, Wireless Sensor Networks (WSMN) are vulnerable to man-in-the-middle attacks, which are challenging to identify. The utilization of machine learning methods and the XGBoost algorithm for analyzing the content of sensor network data frames provide close to 100% probability of detecting data substitution within a frame. The use of this mentioned method facilitates rapid training, both based on synthetic data and real-world data within the system and does not require significant computational resources. 2024-10-31T10:04:27Z 2024-10-31T10:04:27Z 2024 Статья Sosnovskiy Y., Ilyina V., Milyukov V., Timofeeva S., Lapina M., Lapin V., Sumbwanyambe M. Data Control in Distributed Self-organizing Sensor Network Under Speciffic Deployment Condition // Lecture Notes in Networks and Systems. - 2024. - 1044 LNNS. - pp. 355 - 365. - DOI: 10.1007/978-3-031-64010-0_33 https://dspace.ncfu.ru/handle/123456789/29183 en Lecture Notes in Networks and Systems application/pdf Springer Science and Business Media Deutschland GmbH
institution	СКФУ
collection	Репозиторий
language	English
topic	Self – similarity Wireless sensor networks Self-organizing network Sensor nodes
spellingShingle	Self – similarity Wireless sensor networks Self-organizing network Sensor nodes Lapina, M. A. Лапина, М. А. Lapin, V. G. Лапин, В. Г. Data Control in Distributed Self-organizing Sensor Network Under Speciffic Deployment Condition
description	A wireless sensor mesh network can be deployed in special conditions where stable GSM, Wi-Fi, and other coverage are absent. At the same time, there is also a risk of malicious interference with the transmitted information. A popular cognitive radio (CR) communication device with spread spectrum technology is also susceptible to radio jamming. Recent practical knowledge regarding radio jamming, radio reconnaissance, and electronic warfare indicates that radio jamming is effective but has certain limitations in terms of working distance and energy consumption. A foundational practical test under conditions approximating special deployment circumstances revealed that typical radio jamming schemes operate at distances up to 1–1.3 km when applied to cognitive radio (CR) devices. These data, overall, are corroborated by the practical application of networks of this kind in specialized deployment conditions. Additionally, Wireless Sensor Networks (WSMN) are vulnerable to man-in-the-middle attacks, which are challenging to identify. The utilization of machine learning methods and the XGBoost algorithm for analyzing the content of sensor network data frames provide close to 100% probability of detecting data substitution within a frame. The use of this mentioned method facilitates rapid training, both based on synthetic data and real-world data within the system and does not require significant computational resources.
format	Статья
author	Lapina, M. A. Лапина, М. А. Lapin, V. G. Лапин, В. Г.
author_facet	Lapina, M. A. Лапина, М. А. Lapin, V. G. Лапин, В. Г.
author_sort	Lapina, M. A.
title	Data Control in Distributed Self-organizing Sensor Network Under Speciffic Deployment Condition
title_short	Data Control in Distributed Self-organizing Sensor Network Under Speciffic Deployment Condition
title_full	Data Control in Distributed Self-organizing Sensor Network Under Speciffic Deployment Condition
title_fullStr	Data Control in Distributed Self-organizing Sensor Network Under Speciffic Deployment Condition
title_full_unstemmed	Data Control in Distributed Self-organizing Sensor Network Under Speciffic Deployment Condition
title_sort	data control in distributed self-organizing sensor network under speciffic deployment condition
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2024
url	https://dspace.ncfu.ru/handle/123456789/29183
work_keys_str_mv	AT lapinama datacontrolindistributedselforganizingsensornetworkunderspecifficdeploymentcondition AT lapinama datacontrolindistributedselforganizingsensornetworkunderspecifficdeploymentcondition AT lapinvg datacontrolindistributedselforganizingsensornetworkunderspecifficdeploymentcondition AT lapinvg datacontrolindistributedselforganizingsensornetworkunderspecifficdeploymentcondition
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Data Control in Distributed Self-organizing Sensor Network Under Speciffic Deployment Condition

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