The vision of pervasive artificial intelligence (AI) services can be realized by training an AI model on time using real-time data collected by internet of things (IoT) devices. To this end, IoT devices require offloading their data to an edge server in proximity. However, transmitting high-dimensional and voluminous data from energy-constrained IoT devices poses a significant challenge. To address this limitation, we propose a novel offloading architecture, called joint data deepening-and-prefetching (JD2P), which is feature-by-feature offloading comprising two key techniques. The first one is data deepening, where each data sample's features are sequentially offloaded in the order of importance determined by the data embedding technique such as principle component analysis (PCA). Offloading is terminated once the already transmitted features are sufficient for accurate data classification, resulting in a reduction in the amount of transmitted data. The criteria to offload data are derived for binary and multi-class classifiers, which are designed based on support vector machine (SVM) and deep neural network (DNN), respectively. The second one is data prefetching, where some features potentially required in the future are offloaded in advance, thus achieving high efficiency via precise prediction and parameter optimization. We evaluate the effectiveness of JD2P through experiments using the MNIST dataset, and the results demonstrate its significant reduction in expected energy consumption compared to several benchmarks without degrading learning accuracy.

翻译：泛在人工智能（AI）服务的愿景可通过利用物联网（IoT）设备实时收集的数据及时训练AI模型来实现。为此，物联网设备需将数据卸载至附近的边缘服务器。然而，能量受限的物联网设备传输高维、大量数据构成重大挑战。为解决这一局限，我们提出一种新型卸载架构——联合数据深化与预取（JD2P），该架构采用逐特征卸载策略，包含两项关键技术：第一项是数据深化，即按照数据嵌入技术（如主成分分析PCA）确定的特征重要性顺序依次卸载数据样本的特征。一旦已传输特征足以实现精确数据分类，卸载即终止，从而减少传输数据量。针对二分类与多分类分类器（分别基于支持向量机SVM与深度神经网络DNN设计），推导了数据卸载准则。第二项是数据预取，即提前卸载未来可能需要的部分特征，通过精确预测与参数优化实现高效卸载。我们利用MNIST数据集通过实验评估了JD2P的有效性，结果表明，与多种基准方法相比，该方案在保持学习精度的同时显著降低了预期能耗。