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A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs
In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum netwo...
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Published in: | PloS one 2015-11, Vol.10 (11), p.e0142775-e0142775 |
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description | In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network. |
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To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0142775</identifier><identifier>PMID: 26571042</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Ad hoc networks ; Algorithms ; Beacons ; Cities ; Communication ; Computer Communication Networks ; Computer Simulation ; Control stability ; Control theory ; Electricity consumption ; Floating structures ; Forecasting ; Kalman filter ; Kalman filters ; Load ; Mobile ad hoc networks ; Power control ; Sensors ; Signal processing ; Traffic ; Traffic congestion ; Traffic flow ; Traffic surveys ; Transmissions (automotive) ; Vehicles ; Wireless communications ; Wireless networks ; Wireless Technology</subject><ispartof>PloS one, 2015-11, Vol.10 (11), p.e0142775-e0142775</ispartof><rights>COPYRIGHT 2015 Public Library of Science</rights><rights>2015 Mo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Mo et al 2015 Mo et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-205844003e2b84bf25327e2cd80577e84d85afd4d2e6e379228fa03f219cc6843</citedby><cites>FETCH-LOGICAL-c692t-205844003e2b84bf25327e2cd80577e84d85afd4d2e6e379228fa03f219cc6843</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1733489755/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1733489755?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26571042$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Xia, Cheng-Yi</contributor><creatorcontrib>Mo, Yuanfu</creatorcontrib><creatorcontrib>Yu, Dexin</creatorcontrib><creatorcontrib>Song, Jun</creatorcontrib><creatorcontrib>Zheng, Kun</creatorcontrib><creatorcontrib>Guo, Yajuan</creatorcontrib><title>A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.</description><subject>Ad hoc networks</subject><subject>Algorithms</subject><subject>Beacons</subject><subject>Cities</subject><subject>Communication</subject><subject>Computer Communication Networks</subject><subject>Computer Simulation</subject><subject>Control stability</subject><subject>Control theory</subject><subject>Electricity consumption</subject><subject>Floating structures</subject><subject>Forecasting</subject><subject>Kalman filter</subject><subject>Kalman filters</subject><subject>Load</subject><subject>Mobile ad hoc networks</subject><subject>Power control</subject><subject>Sensors</subject><subject>Signal processing</subject><subject>Traffic</subject><subject>Traffic congestion</subject><subject>Traffic flow</subject><subject>Traffic surveys</subject><subject>Transmissions (automotive)</subject><subject>Vehicles</subject><subject>Wireless communications</subject><subject>Wireless networks</subject><subject>Wireless 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exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>26571042</pmid><doi>10.1371/journal.pone.0142775</doi><oa>free_for_read</oa></addata></record> |
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subjects | Ad hoc networks Algorithms Beacons Cities Communication Computer Communication Networks Computer Simulation Control stability Control theory Electricity consumption Floating structures Forecasting Kalman filter Kalman filters Load Mobile ad hoc networks Power control Sensors Signal processing Traffic Traffic congestion Traffic flow Traffic surveys Transmissions (automotive) Vehicles Wireless communications Wireless networks Wireless Technology |
title | A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs |
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