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Multi-copy data dissemination with probabilistic delay constraint in mobile opportunistic device-to-device networks

机译:移动机会性设备到设备网络中具有概率延迟约束的多副本数据分发

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摘要

Device-to-device (D2D) is a new paradigm that enhances network performance by offering a wide variety of advantages over traditional cellular networks, e.g., efficient spectral usage and extended network coverage. Efficient data dissemination is indispensable for supporting many D2D applications such as content distribution and location-aware advertisement. In this work, we study the problem of multi-copy data dissemination with probabilistic delay constraint in mobile opportunistic D2D networks. We first formally formulate the problem and introduce a centralized heuristic algorithm which aims to discover a graph for multicasting, in order to meet delay constraint and achieve low communication cost. While the centralized solution can be adapted to a distributed implementation, it is inefficient in a mobile opportunistic D2D network, since it intends to apply a deterministic transmission strategy in a nondeterministic network by delivering all data packets via a predetermined route. Based on such observation, we develop a distributed online algorithm based on the optimal stopping strategy that makes an efficient decision on every transmission opportunity. Extensive simulations under real-world traces and random walk mobility model are carried out to learn the performance trend of the proposed schemes under various network settings.
机译:设备到设备(D2D)是一种新的范例,通过提供比传统蜂窝网络更广泛的优势(例如有效的频谱使用和扩展的网络覆盖范围)来增强网络性能。有效的数据分发对于支持许多D2D应用程序(例如内容分发和位置感知广告)必不可少。在这项工作中,我们研究了移动机会D2D网络中具有概率延迟约束的多副本数据分发问题。首先,我们正式提出问题,并引入集中式启发式算法,旨在发现多播图,以满足延迟约束并实现较低的通信成本。尽管集中式解决方案可以适用于分布式实现,但它在移动机会性D2D网络中效率不高,因为它打算通过预定路径传送所有数据包,从而在非确定性网络中应用确定性传输策略。基于这种观察,我们开发了一种基于最佳停止策略的分布式在线算法,该算法可以对每个传输机会做出有效决策。在真实世界的痕迹和随机步行移动性模型下进行了广泛的仿真,以了解所提出的方案在各种网络设置下的性能趋势。

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