The rapid proliferation of the Internet of Things (IoT) and smart applications has led to a surge in data generated by distributed sensing devices. Edge computing is a mainstream approach to managing this data by pushing computation closer to the data source, typically onto resource-constrained devices such as single-board computers (SBCs). In such environments, the unavoidable heterogeneity of hardware and software makes effective load balancing particularly challenging. In this paper, we propose a multi-objective load balancing method tailored to heterogeneous, edge-based object detection systems. We study a setting in which multiple device-model pairs expose distinct accuracy, latency, and energy profiles, while both request intensity and scene complexity fluctuate over time. To handle this dynamically varying environment, our approach uses a two-stage decision mechanism: it first performs accuracy-aware filtering to identify suitable device-model candidates that provide accuracy within the acceptable range, and then applies a weighted-sum scoring function over expected latency and energy consumption to select the final execution target. We evaluate the proposed load balancer through extensive experiments on real-world datasets, comparing against widely used baseline strategies. The results indicate that the proposed multi-objective load balancing method halves energy consumption and achieves an 80% reduction in end-to-end latency, while incurring only a modest, up to 10%, decrease in detection accuracy relative to an accuracy-centric baseline.

翻译：物联网（IoT）与智能应用的快速发展导致分布式传感设备产生的数据激增。边缘计算是管理此类数据的主流方法，其将计算推近数据源，通常部署在资源受限的设备（如单板计算机）上。在此类环境中，硬件与软件不可避免的异构性使得有效的负载均衡尤为困难。本文提出一种专为异构边缘目标检测系统定制的多目标负载均衡方法。我们研究了一种场景，其中多个设备-模型组合展现出不同的精度、延迟与能耗特性，同时请求强度与场景复杂度随时间动态波动。为应对这种动态变化的环境，我们的方法采用两阶段决策机制：首先执行精度感知过滤，以识别出能提供可接受范围内精度的合适设备-模型候选组合；随后，对预期延迟与能耗应用加权求和评分函数，以选择最终执行目标。我们通过在真实数据集上进行大量实验来评估所提出的负载均衡器，并与广泛使用的基线策略进行比较。结果表明，所提出的多目标负载均衡方法在检测精度相较于以精度为中心的基线仅下降最多10%的情况下，将能耗降低了一半，并使端到端延迟减少了80%。