User Preference Based Energy-Aware Mobile AR System with Edge Computing

Abstract

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.