Edge computing enables data processing closer to the source, significantly reducing latency, an essential requirement for real-time vision-based analytics such as object detection in surveillance and smart city environments. However, these tasks place substantial demands on resource-constrained edge devices, making the joint optimization of energy consumption and detection accuracy critical. To address this challenge, we propose ECORE, a framework that integrates multiple dynamic routing strategies, including a novel estimation-based techniques and an innovative greedy selection algorithm, to direct image processing requests to the most suitable edge device-model pair. ECORE dynamically balances energy efficiency and detection performance based on object characteristics. We evaluate our framework through extensive experiments on real-world datasets, comparing against widely used baseline techniques. The evaluation leverages established object detection models (YOLO, SSD, EfficientDet) and diverse edge platforms, including Jetson Orin Nano, Raspberry Pi 4 and 5, and TPU accelerators. Results demonstrate that our proposed context-aware routing strategies can reduce energy consumption and latency by 35% and 49%, respectively, while incurring only a 2% loss in detection accuracy compared to accuracy-centric methods.

翻译：边缘计算使得数据处理更接近数据源，显著降低了延迟，这对于监控和智慧城市环境中的实时视觉分析（如目标检测）至关重要。然而，这些任务对资源受限的边缘设备提出了巨大需求，使得能耗与检测精度的联合优化变得尤为关键。为应对这一挑战，我们提出了ECORE框架。该框架集成了多种动态路由策略，包括一种新颖的基于估计的技术和一种创新的贪心选择算法，以将图像处理请求导向最合适的边缘设备-模型组合。ECORE能够根据目标特征动态平衡能效与检测性能。我们在真实数据集上进行了大量实验来评估该框架，并与广泛使用的基线技术进行了比较。评估采用了成熟的目标检测模型（YOLO、SSD、EfficientDet）以及多样化的边缘平台，包括Jetson Orin Nano、Raspberry Pi 4和5以及TPU加速器。实验结果表明，与以精度为中心的方法相比，我们提出的上下文感知路由策略能够分别降低35%的能耗和49%的延迟，而检测精度损失仅为2%。