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Adaptive Working Schedule for Duty-Cycle Opportunistic Mobile Networks

机译:占空比机会移动网络的自适应工作时间表

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In opportunistic mobile networks (OppNets), a large amount of energy is consumed by idle listening, instead of infrequent data exchange. This makes energy saving a challenging and fundamental problem in OppNets since nodes are typically battery powered. Asynchronous duty-cycle operation is a promising approach for energy saving in OppNets; however, if its working schedule is not effectively designed, it may also cause significant network performance degradation. Therefore, it is pressing to design an energy-efficient working schedule for duty-cycle OppNets. In this paper, we first analyze the contact process in duty-cycle OppNets and then propose an adaptive working schedule for duty-cycle OppNets. The proposed adaptive working schedule uses the past recorded contact histories to predict future contact information to adaptively configure the working schedule of each node in the network. Finally, extensive real trace-driven simulations are conducted to evaluate the performance of our proposed adaptive working schedule. Extensive real trace-driven simulation results demonstrate that our proposed adaptive working schedule is superior to the random working schedule and the periodical working schedule algorithms in terms of the number of effect contacts, delivery ratio, and delivery delay.
机译:在机会移动网络(OppNets)中,空闲侦听会消耗大量能量,而不是很少进行数据交换。由于节点通常由电池供电,因此这在OppNets中使节能成为一个具有挑战性的根本问题。异步占空比操作是OppNets中节能的一种有前途的方法。但是,如果未有效设计其工作计划,则也可能导致网络性能显着下降。因此,迫切需要为占空比OppNets设计节能工作时间表。在本文中,我们首先分析占空比OppNets中的联系过程,然后提出占空比OppNets的自适应工作计划。所提出的自适应工作时间表使用过去记录的联系历史来预测将来的联系信息,以自适应地配置网络中每个节点的工作时间表。最后,进行了广泛的实际跟踪驱动模拟,以评估我们提出的自适应工作计划的性能。大量实际跟踪驱动的仿真结果表明,我们提出的自适应工作计划在效果接触的数量,传送比率和传送延迟方面优于随机工作计划和周期性工作计划算法。

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