# LEAF + AIO: Edge-Assisted Energy-Aware Object Detection for Mobile Augmented Reality

arXiv: https://arxiv.org/abs/2205.13770 · HTML: https://mlsanity.com/paper/2205.13770/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 6 of 6)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Latency and Throughput Characterization of Convolutional Neural Networks for Mobile Computer Vision](https://mlsanity.com/paper/1803.09492/) (2018)
- [Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications](https://mlsanity.com/paper/2011.13075/) (2020)
- [Modeling the Temperature Bias of Power Consumption for Nanometer-Scale CPUs in Application Processors](https://mlsanity.com/paper/1404.3381/) (2014)

## Built on by (most cited first, 9 of 9)

- [Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices](https://mlsanity.com/paper/2310.18329/) (2023)
- [Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques](https://mlsanity.com/paper/2211.15751/) (2022)
- [lm-Meter: Unveiling Runtime Inference Latency for On-Device Language Models](https://mlsanity.com/paper/2510.06126/) (2025)
- [EPAM: A Predictive Energy Model for Mobile AI](https://mlsanity.com/paper/2303.01509/) (2023)
- [GreenAuto: An Automated Platform for Sustainable AI Model Design on Edge Devices](https://mlsanity.com/paper/2501.14995/) (2025)
- [User-Centric Communication Service Provision for Edge-Assisted Mobile Augmented Reality](https://mlsanity.com/paper/2509.25905/) (2025)
- [PlatformX: An End-to-End Transferable Platform for Energy-Efficient Neural Architecture Search](https://mlsanity.com/paper/2510.08993/) (2025)
- [A Performance Analysis Modeling Framework for Extended Reality Applications in Edge-Assisted Wireless Networks](https://mlsanity.com/paper/2405.07033/) (2024)
- [Neuromorphic Object Detection: An In-Depth Study and Future Directions](https://mlsanity.com/paper/2607.23576/) (2026)

## Abstract (arXiv)

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.