This paper introduces EcoPull, a sustainable Internet of Things (IoT) framework empowered by tiny machine learning (TinyML) models for fetching images from wireless visual sensor networks. Two types of learnable TinyML models are installed in the IoT devices: i) a behavior model and ii) an image compressor model. The first filters out irrelevant images for the current task, reducing unnecessary transmission and resource competition among the devices. The second allows IoT devices to communicate with the receiver via latent representations of images, reducing communication bandwidth usage. However, integrating learnable modules into IoT devices comes at the cost of increased energy consumption due to inference. The numerical results show that the proposed framework can save > 70% energy compared to the baseline while maintaining the quality of the retrieved images at the ES.

翻译：本文提出EcoPull，一种由微型机器学习（TinyML）模型赋能的可持续物联网（IoT）框架，用于从无线视觉传感器网络中获取图像。在物联网设备中部署了两类可学习的TinyML模型：（i）行为模型，用于滤除与当前任务无关的图像，减少不必要的传输及设备间的资源竞争；（ii）图像压缩模型，允许物联网设备通过图像的潜表示与接收端通信，降低通信带宽消耗。然而，在物联网设备中集成可学习模块因推理过程增加了能耗成本。数值结果表明，与基线方法相比，所提框架能节省超过70%的能耗，同时保证边缘服务器端检索图像的质量。