This letter introduces an energy-efficient pull-based data collection framework for Internet of Things (IoT) devices that use Tiny Machine Learning (TinyML) to interpret data queries. A TinyML model is transmitted from the edge server to the IoT devices. The devices employ the model to facilitate the subsequent semantic queries. This reduces the transmission of irrelevant data, but receiving the ML model and its processing at the IoT devices consume additional energy. We consider the specific instance of image retrieval in a single device scenario and investigate the gain brought by the proposed scheme in terms of energy efficiency and retrieval accuracy, while considering the cost of computation and communication, as well as memory constraints. Numerical evaluation shows that, compared to a baseline scheme, the proposed scheme reaches up to 67% energy reduction under the accuracy constraint when many images are stored. Although focused on image retrieval, our analysis is indicative of a broader set of communication scenarios in which the preemptive transmission of an ML model can increase communication efficiency.

翻译：本文提出了一种基于拉取模式的节能数据收集框架，适用于采用微型机器学习（TinyML）解析数据查询的物联网设备。该框架将TinyML模型从边缘服务器传输至物联网设备，设备利用该模型处理后续语义查询，从而减少无关数据的传输。然而，接收机器学习模型及其在物联网设备上的处理过程会消耗额外能量。我们以单设备场景下的图像检索为具体案例，在综合考虑计算与通信开销以及内存限制的前提下，探究所提方案在能效与检索精度方面的增益。数值评估表明，在存储大量图像且满足精度约束的条件下，相较于基线方案，所提方案最高可实现67%的能耗降低。尽管聚焦于图像检索，我们的分析对更广泛的通信场景具有启示意义，即预先传输机器学习模型能够提升通信效率。