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ADMB: Application‐driven multihop broadcast for vehicular networks
Author(s) -
Liu Fangqi,
Liu Yanheng,
Wang Jian,
Deng Weiwen,
Xu Shaoqing
Publication year - 2017
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3306
Subject(s) - computer science , computer network , network packet , broadcasting (networking) , relay , dissemination , hop (telecommunications) , distributed computing , telecommunications , power (physics) , physics , quantum mechanics
Summary Considering the strong correlation between packet's dissemination range and its originating application's benefit in vehicular networks, this paper introduces 2 new concepts of distance‐dependent application influence and time‐dependent application accuracy, both of which comprise the application profit. Then, we propose an application‐driven multihop broadcast (ADMB) method through hop‐by‐hop tradeoff between the application profit and network cost in deciding whether or not to relay the broadcast. The dynamic network states and elapsed broadcasting value are joined into the decision‐making process, by such way ADMB enables the cross‐layer joint optimization between application and network layers. Moreover, ADMB permits applications themselves to preferentially customize their desired dissemination distance and tolerant sensitivity to the time‐lag effect, which are characterized by influence coefficient and accuracy coefficient, respectively. Every relaying participant also could customize its priority consideration about application influence and accuracy as well as selfish extent in making decision. We perform extensive numerical calculations and simulations to investigate the applicability and use of ADMB against various combinations of application requirements and network parameters. The results show that ADMB can behave superiorly over the traditional fixed‐hop broadcast, fully use the restrictive network resources, and adaptively react to the time‐varying network context.