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Transmission Line Monitoring Technology Based on Compressed Sensing Wireless Sensor Network
Given wireless sensor networks' significant data transmission requirements, conventional direct transmission often leads to bandwidth constraints and excessive network energy consumption. This paper proposes a transmission line monitoring technology based on compressed sensing wireless sensor n...
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Published in: | International journal of advanced computer science & applications 2024, Vol.15 (4) |
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container_title | International journal of advanced computer science & applications |
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creator | YIN, Shuling YU, Renping WANG, Longzhi |
description | Given wireless sensor networks' significant data transmission requirements, conventional direct transmission often leads to bandwidth constraints and excessive network energy consumption. This paper proposes a transmission line monitoring technology based on compressed sensing wireless sensor networks to achieve real-time monitoring of ice-covered power lines. Grounded in compressed sensing theory, this method utilizes dual orthogonal wavelet transform sparse matrices for sparse representation of sensor data. Considering the practical requirements of power line monitoring, a data transmission model is established to implement compressed sampling transmission. The regularization orthogonal matching pursuit algorithm is employed for high-precision reconstruction of compressed data. The software and hardware components of the power line monitoring system are designed, and experiments are conducted under real-world conditions. The results demonstrate that: 1) the system operates stably with an ideal data compression effect, achieving a compression ratio of 93.191%. The absolute reconstruction errors for temperature, humidity, and wind speed sensor data are 0.064°C, 0.052%, and 0.128 m/s, respectively, indicating high reconstruction accuracy and effectively avoiding transmission impacts caused by bandwidth issues. 2) In a 36-hour energy consumption loss test, compared to direct transmission, the compressed transmission mode exhibits a lower rate of battery voltage decay, with a decrease of approximately 11.18%, effectively extending the network's lifespan. |
doi_str_mv | 10.14569/IJACSA.2024.0150419 |
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This paper proposes a transmission line monitoring technology based on compressed sensing wireless sensor networks to achieve real-time monitoring of ice-covered power lines. Grounded in compressed sensing theory, this method utilizes dual orthogonal wavelet transform sparse matrices for sparse representation of sensor data. Considering the practical requirements of power line monitoring, a data transmission model is established to implement compressed sampling transmission. The regularization orthogonal matching pursuit algorithm is employed for high-precision reconstruction of compressed data. The software and hardware components of the power line monitoring system are designed, and experiments are conducted under real-world conditions. The results demonstrate that: 1) the system operates stably with an ideal data compression effect, achieving a compression ratio of 93.191%. The absolute reconstruction errors for temperature, humidity, and wind speed sensor data are 0.064°C, 0.052%, and 0.128 m/s, respectively, indicating high reconstruction accuracy and effectively avoiding transmission impacts caused by bandwidth issues. 2) In a 36-hour energy consumption loss test, compared to direct transmission, the compressed transmission mode exhibits a lower rate of battery voltage decay, with a decrease of approximately 11.18%, effectively extending the network's lifespan.</description><identifier>ISSN: 2158-107X</identifier><identifier>EISSN: 2156-5570</identifier><identifier>DOI: 10.14569/IJACSA.2024.0150419</identifier><language>eng</language><publisher>West Yorkshire: Science and Information (SAI) Organization Limited</publisher><subject>Algorithms ; Bandwidths ; Compression ratio ; Data compression ; Data transmission ; Energy consumption ; Ice cover ; Matched pursuit ; Monitoring ; Power lines ; Reconstruction ; Regularization ; Sensors ; Sparse matrices ; Transmission lines ; Wavelet transforms ; Wind speed ; Wireless sensor networks</subject><ispartof>International journal of advanced computer science & applications, 2024, Vol.15 (4)</ispartof><rights>2024. 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This paper proposes a transmission line monitoring technology based on compressed sensing wireless sensor networks to achieve real-time monitoring of ice-covered power lines. Grounded in compressed sensing theory, this method utilizes dual orthogonal wavelet transform sparse matrices for sparse representation of sensor data. Considering the practical requirements of power line monitoring, a data transmission model is established to implement compressed sampling transmission. The regularization orthogonal matching pursuit algorithm is employed for high-precision reconstruction of compressed data. The software and hardware components of the power line monitoring system are designed, and experiments are conducted under real-world conditions. The results demonstrate that: 1) the system operates stably with an ideal data compression effect, achieving a compression ratio of 93.191%. The absolute reconstruction errors for temperature, humidity, and wind speed sensor data are 0.064°C, 0.052%, and 0.128 m/s, respectively, indicating high reconstruction accuracy and effectively avoiding transmission impacts caused by bandwidth issues. 2) In a 36-hour energy consumption loss test, compared to direct transmission, the compressed transmission mode exhibits a lower rate of battery voltage decay, with a decrease of approximately 11.18%, effectively extending the network's lifespan.</description><subject>Algorithms</subject><subject>Bandwidths</subject><subject>Compression ratio</subject><subject>Data compression</subject><subject>Data transmission</subject><subject>Energy consumption</subject><subject>Ice cover</subject><subject>Matched pursuit</subject><subject>Monitoring</subject><subject>Power lines</subject><subject>Reconstruction</subject><subject>Regularization</subject><subject>Sensors</subject><subject>Sparse matrices</subject><subject>Transmission lines</subject><subject>Wavelet transforms</subject><subject>Wind speed</subject><subject>Wireless sensor networks</subject><issn>2158-107X</issn><issn>2156-5570</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNotkE1LAzEQhoMoWGr_gYcFz1snn7s51sWPStVDKwoeQrqb1NQ2qckW6b93-zGXmXl5mIEHoWsMQ8y4kLfj51E1HQ0JEDYEzIFheYZ6BHORc17A-WEucwzF5yUapLSErqgkoqQ99DWL2qe1S8kFn02cN9lL8K4N0flFNjP1tw-rsNhldzqZJuuYKqw30aT9NjU-7bEPF82qiw5BiNmraf9C_LlCF1avkhmceh-9P9zPqqd88vY4rkaTvCYFa_MaswK4FbY0zM5rTGRjJOZzAUKKRpaWABWNpYXUkhVUl1LUhGgsiLEcOKZ9dHO8u4nhd2tSq5ZhG333UlEQgFkpJHQUO1J1DClFY9UmurWOO4VBHUyqo0m1N6lOJuk_rr5mwg</recordid><startdate>2024</startdate><enddate>2024</enddate><creator>YIN, Shuling</creator><creator>YU, Renping</creator><creator>WANG, Longzhi</creator><general>Science and Information (SAI) Organization Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>2024</creationdate><title>Transmission Line Monitoring Technology Based on Compressed Sensing Wireless Sensor Network</title><author>YIN, Shuling ; YU, Renping ; WANG, Longzhi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c274t-c14705f6f8e4fbc129de915b60696d98f2036df379a9473a896c22a162ef50513</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Bandwidths</topic><topic>Compression ratio</topic><topic>Data compression</topic><topic>Data transmission</topic><topic>Energy consumption</topic><topic>Ice cover</topic><topic>Matched pursuit</topic><topic>Monitoring</topic><topic>Power lines</topic><topic>Reconstruction</topic><topic>Regularization</topic><topic>Sensors</topic><topic>Sparse matrices</topic><topic>Transmission lines</topic><topic>Wavelet transforms</topic><topic>Wind speed</topic><topic>Wireless sensor networks</topic><toplevel>online_resources</toplevel><creatorcontrib>YIN, Shuling</creatorcontrib><creatorcontrib>YU, Renping</creatorcontrib><creatorcontrib>WANG, Longzhi</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest research library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>International journal of advanced computer science & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>YIN, Shuling</au><au>YU, Renping</au><au>WANG, Longzhi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Transmission Line Monitoring Technology Based on Compressed Sensing Wireless Sensor Network</atitle><jtitle>International journal of advanced computer science & applications</jtitle><date>2024</date><risdate>2024</risdate><volume>15</volume><issue>4</issue><issn>2158-107X</issn><eissn>2156-5570</eissn><abstract>Given wireless sensor networks' significant data transmission requirements, conventional direct transmission often leads to bandwidth constraints and excessive network energy consumption. This paper proposes a transmission line monitoring technology based on compressed sensing wireless sensor networks to achieve real-time monitoring of ice-covered power lines. Grounded in compressed sensing theory, this method utilizes dual orthogonal wavelet transform sparse matrices for sparse representation of sensor data. Considering the practical requirements of power line monitoring, a data transmission model is established to implement compressed sampling transmission. The regularization orthogonal matching pursuit algorithm is employed for high-precision reconstruction of compressed data. The software and hardware components of the power line monitoring system are designed, and experiments are conducted under real-world conditions. The results demonstrate that: 1) the system operates stably with an ideal data compression effect, achieving a compression ratio of 93.191%. The absolute reconstruction errors for temperature, humidity, and wind speed sensor data are 0.064°C, 0.052%, and 0.128 m/s, respectively, indicating high reconstruction accuracy and effectively avoiding transmission impacts caused by bandwidth issues. 2) In a 36-hour energy consumption loss test, compared to direct transmission, the compressed transmission mode exhibits a lower rate of battery voltage decay, with a decrease of approximately 11.18%, effectively extending the network's lifespan.</abstract><cop>West Yorkshire</cop><pub>Science and Information (SAI) Organization Limited</pub><doi>10.14569/IJACSA.2024.0150419</doi><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Bandwidths Compression ratio Data compression Data transmission Energy consumption Ice cover Matched pursuit Monitoring Power lines Reconstruction Regularization Sensors Sparse matrices Transmission lines Wavelet transforms Wind speed Wireless sensor networks |
title | Transmission Line Monitoring Technology Based on Compressed Sensing Wireless Sensor Network |
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