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首页> 外文期刊>Eurasip Journal on Wireless Communications and Networking >Mobile user-preference-based data dissemination on mobile P2P networks
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Mobile user-preference-based data dissemination on mobile P2P networks

机译:在移动P2P网络上基于移动用户偏好的数据分发

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摘要

A considerable number of studies have been performed recently on mobile peer-to-peer networks (MOPNETs), as the number of services based on mobile devices has increased. However, existing studies still reveal numerous shortcomings, such as bandwidth overhead, and redundant transmission in terms of multi-broadcast between peers. Especially, owing to the characteristics of MOPNETs, it is extremely important to determine broadcast size and to disseminate data within the limited environment of the network. The mechanism directly affects how well resource information in a mobile device is discovered and how data are transmitted. In this context, it is vital to efficiently disseminate data to be able to arrange hierarchal sequences of resource information of each mobile device for better search performance. Especially, it is also vital to determine the broadcast size, considering the consumption patterns of mobile users. In this article, we propose an adaptable algorithm that determines weighted values and disseminates data using the high-order Markov chain (HoMC). We apply weighted values in consideration of the MOPNET environment. In addition, the proposed HoMC-based Mobile User-preference-based Data Dissemination algorithm was simulated with a Qualnet simulator. Results show that the proposed algorithm performs 17.3% better, on average, in terms of data dissemination, than the existing dissemination methods.
机译:随着基于移动设备的服务数量的增加,最近在移动对等网络(MOPNET)上进行了大量研究。但是,现有研究仍显示出许多缺点,例如带宽开销和对等体之间的多播冗余传输。特别是,由于MOPNET的特性,在网络的有限环境中确定广播大小并分发数据非常重要。该机制直接影响移动设备中资源信息的发现程度以及数据的传输方式。在这种情况下,至关重要的是要有效地分发数据,以便能够排列每个移动设备的资源信息的分层序列,以获得更好的搜索性能。特别地,考虑到移动用户的消费模式,确定广播大小也至关重要。在本文中,我们提出了一种自适应算法,该算法使用高阶马尔可夫链(HoMC)确定加权值并分发数据。考虑到MOPNET环境,我们应用加权值。此外,使用Qualnet模拟器对提出的基于HoMC的基于移动用户偏好的数据分发算法进行了仿真。结果表明,与现有的分发方法相比,该算法在数据分发方面的性能平均提高了17.3%。

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