Channel Prediction Based Scheduling for Data Dissemination in VANETs

Fanhui Zeng; Rongqing Zhang; Xiang Cheng; Liuqing Yang

doi:10.1109/LCOMM.2017.2676766

Channel Prediction Based Scheduling for Data Dissemination in VANETs

Fanhui Zeng, Rongqing Zhang, Xiang Cheng, Liuqing Yang

Research output: Contribution to journal › Article › peer-review

90 Scopus citations

Abstract

In vehicular ad hoc networks (VANETs), cooperative data dissemination is a promising approach addressing the problem of limited connection time between vehicles and road side units. However, existing data dissemination strategies rely too much on real-time channel state information, resulting in high communication overhead and transmission delay. To reduce the communication overhead and thus enhance the system throughput, we propose a channel prediction based scheduling strategy for data dissemination in VANETs. The channel prediction is built upon the recursive least squares algorithm. With low computational complexity, our proposed strategy achieves high scheduling efficiency verified by simulations of transmission delay and system throughput under both urban and highway scenarios.

Original language	English (US)
Article number	7867848
Pages (from-to)	1409-1412
Number of pages	4
Journal	IEEE Communications Letters
Volume	21
Issue number	6
DOIs	https://doi.org/10.1109/LCOMM.2017.2676766
State	Published - Jun 2017
Externally published	Yes

Bibliographical note

Funding Information:
This work was jointly supported by the National Natural Science Foundation of China (Grant No. 61622101 and 61571020), the Ministry National Key Research and Development Project under Grant 2016YFE0123100, the National 973 project (Grant No. 2013CB336700), the open research fund of the State Key Laboratory of Integrated Services Networks, Xidian University (Grant No. ISN18-14), and the National Science Foundation under grant number CNS-1343189.

Publisher Copyright:
© 2017 IEEE.

Keywords

Channel prediction
data dissemination
vehicular ad hoc networks

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10.1109/LCOMM.2017.2676766

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title = "Channel Prediction Based Scheduling for Data Dissemination in VANETs",

abstract = "In vehicular ad hoc networks (VANETs), cooperative data dissemination is a promising approach addressing the problem of limited connection time between vehicles and road side units. However, existing data dissemination strategies rely too much on real-time channel state information, resulting in high communication overhead and transmission delay. To reduce the communication overhead and thus enhance the system throughput, we propose a channel prediction based scheduling strategy for data dissemination in VANETs. The channel prediction is built upon the recursive least squares algorithm. With low computational complexity, our proposed strategy achieves high scheduling efficiency verified by simulations of transmission delay and system throughput under both urban and highway scenarios.",

keywords = "Channel prediction, data dissemination, vehicular ad hoc networks",

author = "Fanhui Zeng and Rongqing Zhang and Xiang Cheng and Liuqing Yang",

note = "Funding Information: This work was jointly supported by the National Natural Science Foundation of China (Grant No. 61622101 and 61571020), the Ministry National Key Research and Development Project under Grant 2016YFE0123100, the National 973 project (Grant No. 2013CB336700), the open research fund of the State Key Laboratory of Integrated Services Networks, Xidian University (Grant No. ISN18-14), and the National Science Foundation under grant number CNS-1343189. Publisher Copyright: {\textcopyright} 2017 IEEE.",

year = "2017",

month = jun,

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language = "English (US)",

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journal = "IEEE Communications Letters",

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AU - Zeng, Fanhui

AU - Zhang, Rongqing

AU - Cheng, Xiang

AU - Yang, Liuqing

N1 - Funding Information: This work was jointly supported by the National Natural Science Foundation of China (Grant No. 61622101 and 61571020), the Ministry National Key Research and Development Project under Grant 2016YFE0123100, the National 973 project (Grant No. 2013CB336700), the open research fund of the State Key Laboratory of Integrated Services Networks, Xidian University (Grant No. ISN18-14), and the National Science Foundation under grant number CNS-1343189. Publisher Copyright: © 2017 IEEE.

PY - 2017/6

Y1 - 2017/6

N2 - In vehicular ad hoc networks (VANETs), cooperative data dissemination is a promising approach addressing the problem of limited connection time between vehicles and road side units. However, existing data dissemination strategies rely too much on real-time channel state information, resulting in high communication overhead and transmission delay. To reduce the communication overhead and thus enhance the system throughput, we propose a channel prediction based scheduling strategy for data dissemination in VANETs. The channel prediction is built upon the recursive least squares algorithm. With low computational complexity, our proposed strategy achieves high scheduling efficiency verified by simulations of transmission delay and system throughput under both urban and highway scenarios.

AB - In vehicular ad hoc networks (VANETs), cooperative data dissemination is a promising approach addressing the problem of limited connection time between vehicles and road side units. However, existing data dissemination strategies rely too much on real-time channel state information, resulting in high communication overhead and transmission delay. To reduce the communication overhead and thus enhance the system throughput, we propose a channel prediction based scheduling strategy for data dissemination in VANETs. The channel prediction is built upon the recursive least squares algorithm. With low computational complexity, our proposed strategy achieves high scheduling efficiency verified by simulations of transmission delay and system throughput under both urban and highway scenarios.

KW - Channel prediction

KW - data dissemination

KW - vehicular ad hoc networks

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SN - 1089-7798

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ER -

Channel Prediction Based Scheduling for Data Dissemination in VANETs

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