LEAF + AIO: Edge-Assisted Energy-Aware Object Detection for Mobile Augmented Reality
This addresses energy inefficiency for mobile augmented reality applications, enabling more practical deployment on devices, though it is incremental as it builds on existing edge computing and optimization techniques.
The paper tackles the high energy consumption of deep learning-based mobile augmented reality (MAR) by designing an edge-assisted system that dynamically adjusts configurations like CPU frequency and model size to minimize per-frame energy without degrading latency or accuracy, achieving up to 40% energy savings in evaluations.
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.