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、用于从无线视觉传感器网络获取图像的可持续物联网（IoT）框架。两类可学习的TinyML模型被部署于物联网设备中：i) 行为模型与ii) 图像压缩模型。前者可滤除当前任务无关的图像，减少设备间不必要的传输与资源竞争；后者允许物联网设备通过图像的潜在表征与接收端通信，从而降低通信带宽占用。然而，在物联网设备中集成可学习模块会因推理过程而增加能耗。数值结果表明，与基线方案相比，本框架在边缘服务器（ES）端保持检索图像质量的同时，可节省超过70%的能耗。