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Resources allocation in SWIPT aided fog computing networks

机译:SWIPT辅助雾计算网络中的资源分配

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Fog computing has emerging as a promising technique to meet the ultra-low latency services in wireless network such as Augmented Reality (AR). The fog paradigm tends to distribute computing, storage, control, network resources and services closer to terminal devices as much as possible while most of User Equipments (UEs) do not have constant power supply thus the power supplement has developed as a nontrivial challenge to realize the paradigm. In this paper, simultaneous wireless information and power transfer (SWIPT) is introduced as a power resource to guarantee the UEs complete their computing tasks. We proposed a power, time and data allocation scheme to minimize the total consumption of energy at source node while maintaining the latency requirement. A Quantum particle swarm optimization (QPSO) algorithm in introduced to solve the non-convex problem, numerical results reveal that our proposed allocation scheme consumes less energy than the conventional particle swarm optimization approach.
机译:雾计算已成为一种有前途的技术,可以满足无线网络中的超低延迟服务,例如增强现实(AR)。雾范式倾向于尽可能将计算,存储,控制,网络资源和服务分布在尽可能靠近终端设备的位置,而大多数用户设备(UE)却没有恒定的电源,因此,功率补充已成为实现这一挑战的不平凡的挑战。范式。在本文中,同时引入无线信息和功率传输(SWIPT)作为一种功率资源,以确保UE完成其计算任务。我们提出了一种功率,时间和数据分配方案,以在保持等待时间要求的同时,最小化源节点上的总能耗。引入了量子粒子群算法(QPSO)来解决非凸问题,数值结果表明,我们提出的分配方案比传统的粒子群算法消耗更少的能量。

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