LPRLC: Linear Predictive Run Length Coding to Improve Energy Consumption of WBANs

LPRLC: Linear Predictive Run Length Coding to Improve Energy Consumption of WBANs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article LPRLC: Linear Predictive Run Length Coding to Improve Energy Consumption of WBANs Mahdieh Hajiloo Vakil, Zahra Shirmohammadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4371727/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Wireless Body Area Networks (WBANs) are biosensors placed on the body, inside the body, and around it. Body Area Network (BAN) are designed in micro sizes and have limited resources. Biosensors sometimes have errors in data recording and faced with duplicate and noisy data in real time. Data redundancy causes a significant energy consumption in sending and receiving data in the sensor. One of the most effective ways to reduce data volume is to compress it to save more energy. To solve these problems, the Linear Predictive Run Length Coding method (LPRLC) is presented which is a combination of Linear Predictive Coding (LPC) for data prediction and Run Length Encoding (RLE) for data compression. The signals attained from biosensors include blood pressure systolic (BPsys), blood pressure diastolic (BPdias), Respiration, Oxygen, and Heart Rate, which are recorded as a time series. First, the received signal is predicted continuously, and then the error resulting from the actual signal and the expected signal is calculated. In the last step, the resulting error is compressed by the RLE algorithm and sent to the destination. To compare the criteria of Energy Conservation (EC) and Compression Rate (CR), Huffman, Arithmetic, and Lempel-Ziv-Welch (LZW) algorithms are placed instead of RLE. The results show that the RLE algorithm has an average of 98% energy saving and up to 70 times reduction of data volume compared to other algorithms, which has improved 6% in energy consumption and 9 times reduction of data volume. Wireless Body Area Network data compression energy conservation LPC RLE Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4371727","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299366434,"identity":"80e37d70-a814-4221-b5bf-096f1b12114e","order_by":0,"name":"Mahdieh Hajiloo Vakil","email":"","orcid":"","institution":"Shahid Rajaee Teacher Training University","correspondingAuthor":false,"prefix":"","firstName":"Mahdieh","middleName":"Hajiloo","lastName":"Vakil","suffix":""},{"id":299366435,"identity":"4f2f7063-90a9-4e1b-bd2f-43134f9c1644","order_by":1,"name":"Zahra Shirmohammadi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYDACCcYGIHkAwk6ogAkbEK3lDFFawCRUC2MbEe6Sn93c9ulGxR153WmHH954OK8u3+AA88MPDAX3cGoxuHOweXbOmWeG226nGVskbjtsueEAm7EEg0Exbi0Sic3MuW2HGbfdTjCTSNx2wMDgAIMZUDwBt8NmQLTYb7ud/k0icU4dUAv7N7xaGG5AtCRuu50DtKWBGaiFB78tBiAtOWcOJwO1FFskHDtsIHmYp1giAa/D0h8z51QctgU6bOPNHzV1BnzH2zd++PAHj8MwATMQk6RhFIyCUTAKRgEGAACq9ljRYqQ5YQAAAABJRU5ErkJggg==","orcid":"","institution":"Shahid Rajaee Teacher Training University","correspondingAuthor":true,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Shirmohammadi","suffix":""}],"badges":[],"createdAt":"2024-05-05 12:56:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4371727/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4371727/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67740130,"identity":"f3adc2d3-f52c-410e-a423-d7ee9f6c5f8a","added_by":"auto","created_at":"2024-10-29 08:39:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":776045,"visible":true,"origin":"","legend":"","description":"","filename":"LPRLCv55.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4371727/v1_covered_454d049b-4747-4fda-b9f1-1604866a2d2b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"LPRLC: Linear Predictive Run Length Coding to Improve Energy Consumption of WBANs","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wireless Body Area Network, data compression, energy conservation, LPC, RLE","lastPublishedDoi":"10.21203/rs.3.rs-4371727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4371727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The Wireless Body Area Networks (WBANs) are biosensors placed on the body, inside the body, and around it. Body Area Network (BAN) are designed in micro sizes and have limited resources. Biosensors sometimes have errors in data recording and faced with duplicate and noisy data in real time. Data redundancy causes a significant energy consumption in sending and receiving data in the sensor. One of the most effective ways to reduce data volume is to compress it to save more energy. To solve these problems, the Linear Predictive Run Length Coding method (LPRLC) is presented which is a combination of Linear Predictive Coding (LPC) for data prediction and Run Length Encoding (RLE) for data compression. The signals attained from biosensors include blood pressure systolic (BPsys), blood pressure diastolic (BPdias), Respiration, Oxygen, and Heart Rate, which are recorded as a time series. First, the received signal is predicted continuously, and then the error resulting from the actual signal and the expected signal is calculated. 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