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Optimal Communication-Computing-Caching for Maximizing Revenue in UAV-Aided Mobile Edge Computing

机译:最佳通信 - 计算 - 缓解无人机辅助移动边缘计算中的收入

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Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for Internet of Things (IoT), Constrained by the limited computation and battery capabilities of IoT devices, computational-intensive tasks are difficult to be tackled locally. In this paper, a UAV-aided mobile edge computing (UAMEC) system is constructed to assist IoT devices to tackle computational-intensive tasks. Furthermore, we jointly optimize communications, computing and caching resources allocation strategies for the sake of maximizing the net revenue from the UAMEC under the condition of guaranteeing user's quality of experience (QoE). A multi-dimensional hybrid adaptive particle swarm (MHAPSO) algorithm is conceived to solve this joint optimization problem. Finally, the effectiveness and superiority of our proposed scheme are demonstrated.
机译:无人驾驶飞行器(无人机)已被广泛用于提供增强的信息覆盖范围以及用于物联网(物联网)的中继服务,受到物联网设备的有限计算和电池功能的限制,计算密集型任务难以在本地地解决。在本文中,构造了一种无人驾驶辅助移动边缘计算(UAMEC)系统以帮助IOT设备来解决计算密集型任务。此外,我们共同优化了通信,计算和缓存资源分配策略,以便在保证用户经验质量(QoE)的条件下从UAMEC中最大化净收入。构思了多维混合自适应粒子群(MHAPSO)算法以解决该联合优化问题。最后,证明了我们提出的计划的有效性和优势。

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