Owing to the widespread adoption of the Internet of Things, a vast amount of sensor information is being acquired in real time. Accordingly, the communication cost of data from edge devices is increasing. Compressed sensing (CS), a data compression method that can be used on edge devices, has been attracting attention as a method to reduce communication costs. In CS, estimating the appropriate compression ratio is important. There is a method to adaptively estimate the compression ratio for the acquired data using reinforcement learning (RL). However, the computational costs associated with existing RL methods that can be utilized on edges are often high. In this study, we developed an efficient RL method for edge devices, referred to as the actor--critic online sequential extreme learning machine (AC-OSELM), and a system to compress data by estimating an appropriate compression ratio on the edge using AC-OSELM. The performance of the proposed method in estimating the compression ratio is evaluated by comparing it with other RL methods for edge devices. The experimental results indicate that AC-OSELM demonstrated the same or better compression performance and faster compression ratio estimation than the existing methods.

翻译：由于广泛采用Things互联网,大量传感信息正在实时获得。因此,边缘装置数据的通信成本正在增加。压缩式遥感(CS)是可用于边缘装置的一种数据压缩方法,作为降低通信成本的一种方法,吸引人们的注意。在CS中,估计适当的压缩比例很重要。有一种方法,利用强化学习(RL)对获得的数据的压缩比例进行适应性估计。然而,与现有RL方法有关的、可在边缘使用的现有RL方法有关的计算成本往往很高。在本研究中,我们为边缘装置开发了一种高效RL方法,称为演员-critic在线连续极端学习机器(AC-OSELM),以及一种通过使用AC-OSELM估计边缘适当压缩比例来压缩数据系统。通过将拟议压缩比率与其他边缘装置的RL方法进行比较,评估了拟议方法的性能。实验结果表明,AC-OSELM展示了与现有方法相同或更好的压缩性能和压缩率估计。</s>