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Rethink data dissemination in opportunistic mobile networks with mutually exclusive requirement

机译:重新考虑机会互斥要求下的机会移动网络中的数据分发

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With the increase of mobile devices, opportunistic mobile networks become a promising technique for disseminating data in a local area. However, existing works focus on the single data dissemination and fail to consider the practical applications where there are multiple data under different topics. Multiple data dissemination shows the potential applications in many scenarios, e.g., product coupon distribution. In this paper, we focus on budget-constrained multiple data dissemination services. A mobile user may be interested in data under different topics, but receiving data for any topic is enough due to user experiences and participation constraints. This is the mutually exclusive delivery requirement in many scenarios. In light of the different amounts of data and the different popularity levels of data in each topic, deciding which data should be forwarded to mobile users becomes an important problem. This paper aims to design an efficient data dissemination scheme that minimizes the maximum dissemination delay while incurring a small communication overhead for the aforementioned scenario. In this paper, we discuss three different scenarios according to different knowledge. We start with the data dissemination with network topology, and a corresponding optimal solution is proposed. Later, we consider the probability estimation with k-hop information, and lastly propose a distributed data forwarding algorithm, which considers the amount of data in different topics, the mobile users' interest, and their data forwarding abilities, respectively. The real trace-driven experiments show that the proposed scheme achieves a good performance. (C) 2018 Elsevier Inc. All rights reserved.
机译:随着移动设备的增加,机会移动网络成为在本地分发数据的有前途的技术。但是,现有作品只关注单一数据的分发,而没有考虑不同主题下有多个数据的实际应用。多种数据分发显示了在许多情况下的潜在应用,例如产品优惠券分发。在本文中,我们专注于预算有限的多种数据发布服务。移动用户可能对不同主题下的数据感兴趣,但是由于用户体验和参与限制,接收任何主题的数据就足够了。在许多情况下,这是互斥的交付要求。鉴于每个主题中数据量的不同和数据受欢迎程度的不同,决定应将哪些数据转发给移动用户成为一个重要问题。本文旨在设计一种有效的数据分发方案,该方案可最大程度地减少最大分发延迟,同时为上述场景带来较小的通信开销。在本文中,我们根据不同的知识讨论了三种不同的方案。我们从使用网络拓扑结构进行数据分发开始,并提出了相应的最佳解决方案。随后,我们考虑了具有k跳信息的概率估计,最后提出了一种分布式数据转发算法,该算法考虑了不同主题中的数据量,移动用户的兴趣以及他们的数据转发能力。真实的跟踪驱动实验表明,该方案具有良好的性能。 (C)2018 Elsevier Inc.保留所有权利。

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