LEAF + AIO: Edge-Assisted Energy-Aware Object Detection for Mobile Augmented Reality
نویسندگان
چکیده
Today very few deep learning-based mobile augmented reality (MAR) applications are applied in devices because they significantly energy-guzzling. In this paper, we design an edge-based energy-aware MAR system that enables to dynamically change their configurations, such as CPU frequency, computation model size, and image offloading frequency based on user preferences, camera sampling rates, available radio resources. Our proposed dynamic configuration adaptations can minimize the per frame energy consumption of multiple clients without degrading preferred performance metrics, latency detection accuracy. To thoroughly analyze interactions among rate, consumption, propose, best our knowledge, first comprehensive analytical for devices. Based model, a LEAF optimization algorithm guide adaptation server resource allocation. An orchestrator, AIO, coordinating with LEAF, is developed adaptively regulate object invocations further improve efficiency Extensive evaluations conducted validate algorithms.
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ژورنال
عنوان ژورنال: IEEE Transactions on Mobile Computing
سال: 2022
ISSN: ['2161-9875', '1536-1233', '1558-0660']
DOI: https://doi.org/10.1109/tmc.2022.3179943