Study on Self-Adaptive Message Scheduler Used for the Vehicle Ad-hoc Network

To satisfy the strict demands of VANET (vehicular ad-hoc network) on communication rate, response speed as well as on reliability, a communication sublayer that provides reliable, real-time and qualified communication services for upper applications is constructed in VANET. By using the queuing theo...

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Published inJournal of Information Science and Engineering Vol. 33; no. 5; pp. 1285 - 1305
Main Authors 刘明剑(MINGJIAN LIU), 谭国真(GUOZHEN TAN), 丁男(NAN DING), 张福新(FUXIN ZANG)
Format Journal Article
LanguageEnglish
Published Taipei 社團法人中華民國計算語言學學會 01.09.2017
Institute of Information Science, Academia Sinica
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ISSN1016-2364
DOI10.6688/JISE.2017.33.5.11

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Summary:To satisfy the strict demands of VANET (vehicular ad-hoc network) on communication rate, response speed as well as on reliability, a communication sublayer that provides reliable, real-time and qualified communication services for upper applications is constructed in VANET. By using the queuing theory and based on researching the message queues in VANET, the current study designed a scheme for optimizing the communication sublayer, and proposed mechanisms for message compression, sending-frequency self-adaption as well as for data transmission compression, which make up an easy-to-bedeployed framework that provides safe, real-time and standard self-adaptive communication services for upper applications and which solve the problem that previous VANET communication is not reliable and real-time. Through simulation and real vehicle experiments ,we verify that the design can well satisfy the communication requirements of VANET in terms of performance and functionality, bandwidth occupancy decrease by 47.8 % and 11.4% relatively compared with CMS (common message set) and unoptimized MD (message dispatcher) decreased ,compared with CMS and unoptimized MD, the transmission frequency can decrease by 60% and 6.1%, and data compression ratio is 12.3%, thus proving the effectiveness of our scheme.
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ISSN:1016-2364
DOI:10.6688/JISE.2017.33.5.11