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Energy-aware opportunistic mobile data offloading for users in urban environments

机译:为城市环境中的用户提供节能的机会移动数据卸载

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Opportunistic networking (a.k.a. device-to-device communication) is considered a feasible means for offloading mobile data traffic. Since mobile nodes are battery-powered, opportunistic networks must be expected to satisfy the user demand without greatly affecting battery lifetime. To address this requirement, this work introduces progressive selfishness, an adaptive and scalable energy-aware algorithm for opportunistic networks used in the context of mobile data offloading. The paper evaluates the performance of progressive selfishness in terms of both application throughput and energy consumption via extensive trace-driven simulations of realistic pedestrian behavior, and demonstrates that the proposed algorithm is robust against the distributions of node density and initial content availability. The results show that in certain scenarios progressive selfishness achieves up to 85% energy savings during opportunistic downloads while sacrificing less than 1% in application throughput. Furthermore, the study demonstrates that in terms of total energy consumption (by both cellular and opportunistic downloads) in dense environments the performance of progressive selfishness is comparable to downloading contents directly from a mobile network.
机译:机会网络(也称为设备到设备通信)被认为是减轻移动数据流量负担的一种可行方法。由于移动节点由电池供电,因此必须期望机会网络在不大大影响电池寿命的情况下满足用户需求。为了满足这一要求,这项工作引入了渐进式自私性,这是一种适用于移动数据卸载情况下的机会网络的自适应且可扩展的能量感知算法。本文通过对真实行人行为的大量跟踪驱动仿真,评估了应用程序吞吐量和能耗方面的渐进式自私性能,并证明了该算法对节点密度和初始内容可用性的分布具有鲁棒性。结果表明,在某些情况下,渐进式自私可以在机会性下载过程中节省多达85%的能源,同时牺牲应用程序吞吐量的不到1%。此外,研究表明,就密集环境中的总能耗(通过蜂窝下载和机会下载)而言,渐进式自私的性能可与直接从移动网络下载内容相媲美。

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