User Preference Based Energy-Aware Mobile AR System with Edge Computing
User Preference Based Energy-Aware Mobile AR System with Edge Computing
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DOI：
10.1109/infocom41043.2020.9155517
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Haoxin Wang;Jiang Xie
中科院分区：
文献类型：
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作者：
Haoxin Wang;Jiang Xie
深度学习和边缘计算的进步使资源有限的移动设备上的智能移动增强现实（MAR）成为可能。然而，今天很少有基于深度学习的MAR应用程序应用于移动设备，因为它们非常耗电。在本文中，我们设计了一个基于用户偏好的能量感知边缘MAR系统，该系统使MAR客户端能够根据用户偏好、相机采样率和边缘服务器上可用的无线电资源动态更改其配置参数，如CPU频率和计算模型大小。我们提出的动态MAR配置调整可以最小化多个MAR客户端的每帧能量消耗，而不会降低他们首选的MAR性能指标，如服务延迟和检测精度。为了彻底分析MAR配置参数、用户偏好、相机采样率和每帧能量消耗之间的相互作用，我们提出了据我们所知的第一个用于MAR客户端的综合分析能量模型。在此分析模型的基础上，提出了一种LEAF优化算法来指导MAR配置适应和服务器无线电资源分配。进行了广泛的评估，以验证所提出的分析模型和LEAF算法的性能。
The advancement in deep learning and edge computing has enabled intelligent mobile augmented reality (MAR) on resource limited mobile devices. However, today very few deep learning based MAR applications are applied in mobile devices because they are significantly energy-guzzling. In this paper, we design a user preference based energy-aware edge-based MAR system that enables MAR clients to dynamically change their configuration parameters, such as CPU frequency and computation model size, based on their user preferences, camera sampling rates, and available radio resources at the edge server. Our proposed dynamic MAR configuration adaptations can minimize the per frame energy consumption of multiple MAR clients without degrading their preferred MAR performance metrics, such as service latency and detection accuracy. To thoroughly analyze the interactions among MAR configuration parameters, user preferences, camera sampling rate, and per frame energy consumption, we propose, to the best of our knowledge, the first comprehensive analytical energy model for MAR clients. Based on the proposed analytical model, we develop a LEAF optimization algorithm to guide the MAR configuration adaptation and server radio resource allocation. Extensive evaluations are conducted to validate the performance of the proposed analytical model and LEAF algorithm.