  Today very few deep learning-based mobile augmented reality (MAR)
applications are applied in mobile devices because they are significantly
energy-guzzling. In this paper, we design an edge-based energy-aware MAR system
that enables MAR devices to dynamically change their configurations, such as
CPU frequency, computation model size, and image offloading frequency based on
user preferences, camera sampling rates, and available radio resources. 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 latency and detection accuracy. To thoroughly
analyze the interactions among MAR configurations, user preferences, camera
sampling rate, and energy consumption, we propose, to the best of our
knowledge, the first comprehensive analytical energy model for MAR devices.
Based on the proposed analytical model, we design a LEAF optimization algorithm
to guide the MAR configuration adaptation and server radio resource allocation.
An image offloading frequency orchestrator, coordinating with the LEAF, is
developed to adaptively regulate the edge-based object detection invocations
and to further improve the energy efficiency of MAR devices. Extensive
evaluations are conducted to validate the performance of the proposed
analytical model and algorithms.
